Dissertations / Theses on the topic 'Semantic Web RDF OWL Datenintegration'

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada
The vision of the semantic web is to unleash the next generation of information sharing and interoperability by encoding meaning into the symbols that are used to describe various computational capabilities within the World Wide Web or other networks. This paper describes the application of semantic web technologies to Test and Evaluation (T&E) metadata verification and validation. Verification is a quality process that is used to evaluate whether or not a product, service, or system complies with a regulation, specification, or conditions imposed at the start of a development phase or which exists in the organization. Validation is the process of establishing documented evidence that provides a high degree of assurance that a product, service, or system accomplishes its intended requirements. While this often involves acceptance and suitability with external customers, automation provides significant assistance to the customers.

With the advent of the Semantic Web and Semantic Technologies, ontologies have become one of the most prominent paradigms for knowledge representation and reasoning. The popular ontology language OWL, based on description logics, became a W3C recommendation in 2004 and a standard for modelling ontologies on the Web. In the meantime, many studies and applications using OWL have been reported in research and industrial environments, many of which go beyond Internet usage and employ the power of ontological modelling in other fields such as biology, medicine, software engineering, knowledge management, and cognitive systems. However, recent progress in the field faces a lack of well-structured ontologies with large amounts of instance data due to the fact that engineering such ontologies requires a considerable investment of resources. Nowadays, knowledge bases often provide large volumes of data without sophisticated schemata. Hence, methods for automated schema acquisition and maintenance are sought. Schema acquisition is closely related to solving typical classification problems in machine learning, e.g. the detection of chemical compounds causing cancer. In this work, we investigate both, the underlying machine learning techniques and their application to knowledge acquisition in the Semantic Web. In order to leverage machine-learning approaches for solving these tasks, it is required to develop methods and tools for learning concepts in description logics or, equivalently, class expressions in OWL. In this thesis, it is shown that methods from Inductive Logic Programming (ILP) are applicable to learning in description logic knowledge bases. The results provide foundations for the semi-automatic creation and maintenance of OWL ontologies, in particular in cases when extensional information (i.e. facts, instance data) is abundantly available, while corresponding intensional information (schema) is missing or not expressive enough to allow powerful reasoning over the ontology in a useful way. Such situations often occur when extracting knowledge from different sources, e.g. databases, or in collaborative knowledge engineering scenarios, e.g. using semantic wikis. It can be argued that being able to learn OWL class expressions is a step towards enriching OWL knowledge bases in order to enable powerful reasoning, consistency checking, and improved querying possibilities. In particular, plugins for OWL ontology editors based on learning methods are developed and evaluated in this work. The developed algorithms are not restricted to ontology engineering and can handle other learning problems. Indeed, they lend themselves to generic use in machine learning in the same way as ILP systems do. The main difference, however, is the employed knowledge representation paradigm: ILP traditionally uses logic programs for knowledge representation, whereas this work rests on description logics and OWL. This difference is crucial when considering Semantic Web applications as target use cases, as such applications hinge centrally on the chosen knowledge representation format for knowledge interchange and integration. The work in this thesis can be understood as a broadening of the scope of research and applications of ILP methods. This goal is particularly important since the number of OWL-based systems is already increasing rapidly and can be expected to grow further in the future. The thesis starts by establishing the necessary theoretical basis and continues with the specification of algorithms. It also contains their evaluation and, finally, presents a number of application scenarios. The research contributions of this work are threefold: The first contribution is a complete analysis of desirable properties of refinement operators in description logics. Refinement operators are used to traverse the target search space and are, therefore, a crucial element in many learning algorithms. Their properties (completeness, weak completeness, properness, redundancy, infinity, minimality) indicate whether a refinement operator is suitable for being employed in a learning algorithm. The key research question is which of those properties can be combined. It is shown that there is no ideal, i.e. complete, proper, and finite, refinement operator for expressive description logics, which indicates that learning in description logics is a challenging machine learning task. A number of other new results for different property combinations are also proven. The need for these investigations has already been expressed in several articles prior to this PhD work. The theoretical limitations, which were shown as a result of these investigations, provide clear criteria for the design of refinement operators. In the analysis, as few assumptions as possible were made regarding the used description language. The second contribution is the development of two refinement operators. The first operator supports a wide range of concept constructors and it is shown that it is complete and can be extended to a proper operator. It is the most expressive operator designed for a description language so far. The second operator uses the light-weight language EL and is weakly complete, proper, and finite. It is straightforward to extend it to an ideal operator, if required. It is the first published ideal refinement operator in description logics. While the two operators differ a lot in their technical details, they both use background knowledge efficiently. The third contribution is the actual learning algorithms using the introduced operators. New redundancy elimination and infinity-handling techniques are introduced in these algorithms. According to the evaluation, the algorithms produce very readable solutions, while their accuracy is competitive with the state-of-the-art in machine learning. Several optimisations for achieving scalability of the introduced algorithms are described, including a knowledge base fragment selection approach, a dedicated reasoning procedure, and a stochastic coverage computation approach. The research contributions are evaluated on benchmark problems and in use cases. Standard statistical measurements such as cross validation and significance tests show that the approaches are very competitive. Furthermore, the ontology engineering case study provides evidence that the described algorithms can solve the target problems in practice. A major outcome of the doctoral work is the DL-Learner framework. It provides the source code for all algorithms and examples as open-source and has been incorporated in other projects.

Il crescente sviluppo e la promozione della trasparenza dei dati nell’ambito della pubblica amministrazione copre molteplici aspetti, fra cui l’educazione universitaria. Attualmente sono difatti numerosi i dataset rilasciati in formato Linked Open Data disponibili a livello nazionale ed internazionale. Fra le informazioni pubblicamente disponibili spiccano concetti riguardo l’occupazione e la numerosità dei laureati. Nonostante il progresso riscontrato, la mancanza di una metodologia standard per la descrizione di informazioni statistiche sui laureati rende difficoltoso un confronto di determinati fatti a partire da differenti sorgenti di dati. Sul piano nazionale, le indagini AlmaLaurea colmano il gap informativo dell’eterogeneità delle fonti proponendo statistiche centralizzate su profilo dei laureati e relativa condizione occupazionale, aggiornate annualmente. Scopo del progetto di tesi è la realizzazione di un’ontologia di dominio che descriva diverse peculiarità dei laureati, promuovendo allo stesso tempo la definizione strutturata dei dati AlmaLaurea e la successiva pubblicazione nel contesto Linked Open Data. Il progetto, realizzato con l’ausilio delle tecnologie del Web Semantico, propone infine la creazione di un endpoint SPARQL e di una interfaccia web per l'interrogazione e la visualizzazione dei dati strutturati.

Un nombre croissant de sources de données est publié sur le Web, décrites dans les langages proposés par le W3C tels que RDF, RDF(S) et OWL. Une quantité de données sans précédent est ainsi disponible pour les utilisateurs et les applications, mais l'exploitation pertinente de ces sources constitue encore un défi : l'interrogation des sources est en effet limitée d'abord car elle suppose la maîtrise d'un langage de requêtes tel que SPARQL, mais surtout car elle suppose une certaine connaissance de la source de données qui permet de cibler les ressources et les propriétés pertinentes pour les besoins spécifiques des applications. Le travail présenté ici s'intéresse à l'exploration de sources de données RDF, et ce selon deux axes complémentaires : découvrir d'une part les thèmes sur lesquels porte la source de données, fournir d'autre part un support pour l'interrogation d'une source sans l'utilisation de langage de requêtes, mais au moyen de mots clés. L'approche d'exploration proposée se compose ainsi de deux stratégies complémentaires : l'exploration thématique et la recherche par mots clés. La découverte de thèmes dans une source de données RDF consiste à identifier un ensemble de sous-graphes, non nécessairement disjoints, chacun représentant un ensemble cohérent de ressources sémantiquement liées et définissant un thème selon le point de vue de l'utilisateur. Ces thèmes peuvent être utilisés pour permettre une exploration thématique de la source, où les utilisateurs pourront cibler les thèmes pertinents pour leurs besoins et limiter l'exploration aux seules ressources composant les thèmes sélectionnés. La recherche par mots clés est une façon simple et intuitive d'interroger les sources de données. Dans le cas des sources de données RDF, cette recherche pose un certain nombre de problèmes, comme l'indexation des éléments du graphe, l'identification des fragments du graphe pertinents pour une requête spécifique, l'agrégation de ces fragments pour former un résultat, et le classement des résultats obtenus. Nous abordons dans cette thèse ces différents problèmes, et nous proposons une approche qui permet, en réponse à une requête mots clés, de construire une liste de sous-graphes et de les classer, chaque sous-graphe correspondant à un résultat pertinent pour la requête. Pour chacune des deux stratégies d'exploration d'une source RDF, nous nous sommes intéressés à prendre en compte de la connaissance externe, permettant de mieux répondre aux besoins des utilisateurs. Cette connaissance externe peut représenter des connaissances du domaine, qui permettent de préciser le besoin exprimé dans le cas d'une requête, ou de prendre en compte des connaissances permettant d'affiner la définition des thèmes. Dans notre travail, nous nous sommes intéressés à formaliser cette connaissance externe et nous avons pour cela introduit la notion de pattern. Ces patterns représentent des équivalences de propriétés et de chemins dans le graphe représentant la source. Ils sont évalués et intégrés dans le processus d'exploration pour améliorer la qualité des résultats
An increasing number of datasets is published on the Web, expressed in languages proposed by the W3C to describe Web data such as RDF, RDF(S) and OWL. The Web has become a unprecedented source of information available for users and applications, but the meaningful usage of this information source is still a challenge. Querying these data sources requires the knowledge of a formal query language such as SPARQL, but it mainly suffers from the lack of knowledge about the source itself, which is required in order to target the resources and properties relevant for the specific needs of the application. The work described in this thesis addresses the exploration of RDF data sources. This exploration is done according to two complementary ways: discovering the themes or topics representing the content of the data source, and providing a support for an alternative way of querying the data sources by using keywords instead of a query formulated in SPARQL. The proposed exploration approach combines two complementary strategies: thematic-based exploration and keyword search. Theme discovery from an RDF dataset consists in identifying a set of sub-graphs which are not necessarily disjoints, and such that each one represents a set of semantically related resources representing a theme according to the point of view of the user. These themes can be used to enable a thematic exploration of the data source where users can target the relevant theme and limit their exploration to the resources composing this theme. Keyword search is a simple and intuitive way of querying data sources. In the case of RDF datasets, this search raises several problems, such as indexing graph elements, identifying the relevant graph fragments for a specific query, aggregating these relevant fragments to build the query results, and the ranking of these results. In our work, we address these different problems and we propose an approach which takes as input a keyword query and provides a list of sub-graphs, each one representing a candidate result for the query. These sub-graphs are ordered according to their relevance to the query. For both keyword search and theme identification in RDF data sources, we have taken into account some external knowledge in order to capture the users needs, or to bridge the gap between the concepts invoked in a query and the ones of the data source. This external knowledge could be domain knowledge allowing to refine the user's need expressed by a query, or to refine the definition of themes. In our work, we have proposed a formalization to this external knowledge and we have introduced the notion of pattern to this end. These patterns represent equivalences between properties and paths in the dataset. They are evaluated and integrated in the exploration process to improve the quality of the result

Drug repositioning is the discovery of new indications for approved or failed drugs. This practice is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Nowadays, an increasing number of computational methodologies aim at predicting repositioning opportunities in an automated fashion. Some approaches rely on the direct physical interaction between molecules and protein targets (docking) and some methods consider more abstract descriptors, such as a gene expression signature, in order to characterise the potential pharmacological action of a drug (Chapter 1). On a fundamental level, repositioning opportunities exist because drugs perturb multiple biological entities, (on and off-targets) themselves involved in multiple biological processes. Therefore, a drug can play multiple roles or exhibit various mode of actions responsible for its pharmacology. The work done for my thesis aims at characterising these various modes and mechanisms of action for approved drugs, using a mathematical framework called description logics. In this regard, I first specify how living organisms can be compared to complex black box machines and how this analogy can help to capture biomedical knowledge using description logics (Chapter 2). Secondly, the theory is implemented in the Functional Therapeutic Chemical Classification System (FTC - https://www.ebi.ac.uk/chembl/ftc/), a resource defining over 20,000 new categories representing the modes and mechanisms of action of approved drugs. The FTC also indexes over 1,000 approved drugs, which have been classified into the mode of action categories using automated reasoning. The FTC is evaluated against a gold standard, the Anatomical Therapeutic Chemical Classification System (ATC), in order to characterise its quality and content (Chapter 3). Finally, from the information available in the FTC, a series of drug repositioning hypotheses were generated and made publicly available via a web application (https://www.ebi.ac.uk/chembl/research/ftc-hypotheses). A subset of the hypotheses related to the cardiovascular hypertension as well as for Alzheimer’s disease are further discussed in more details, as an example of an application (Chapter 4). The work performed illustrates how new valuable biomedical knowledge can be automatically generated by integrating and leveraging the content of publicly available resources using description logics and automated reasoning. The newly created classification (FTC) is a first attempt to formally and systematically characterise the function or role of approved drugs using the concept of mode of action. The open hypotheses derived from the resource are available to the community to analyse and design further experiments.

Mit mehr als 120 Millionen registrierten Internetadressen (Stand: März 2007) symbolisiert das Internet heutzutage das größte Informationsmedium unserer Zeit. Täglich wächst das Internet um eine unüberschaubare Menge an Informationen. Diese Informationen sind häufig in Dokumenten hinterlegt, welche zur Auszeichnung die Hypertext Markup Language verwenden. Seit Beginn der Neunziger Jahre hat sich dieses System bewährt, da dadurch der einzelne Nutzer in die Lage versetzt wird, auf einfache und effiziente Weise Dokumentinhalte mit Darstellungsanweisungen zu versehen und diese eigenständig im Internet zu veröffentlichen. Diese Layoutinformationen können bei Abruf der entsprechenden Ressource durch ein Computerprogramm leicht ausgewertet und zur Darstellung der Inhalte genutzt werden. Obwohl sowohl die Layoutinformationen als auch die eigentlichen Dokumentinhalte in einem textuellen Format vorliegen, konnten die Nutzertextinhalte durch eine Maschine bisher nur sehr eingeschränkt verarbeitet werden. Während es menschlichen Nutzern keinerlei Probleme bereitet, die Bedeutung einzelner Texte auf einer Webseite zu identifizieren, stellen diese für einen Rechner prinzipiell nur eine Aneinanderreihung von ASCII-Zeichen dar. Sobald es möglich werden würde, die Bedeutung von Informationen durch ein Computerprogramm effizient zu erfassen und weiterzuverarbeiten, wären völlig neue Anwendungen mit qualitativ hochwertigeren Ergebnissen im weltweiten Datennetz möglich. Nutzer könnten Anfragen an spezielle Agenten stellen, welche sich selbstständig auf die Suche nach passenden Resultaten begeben; Informationen verschiedener Informationsquellen könnten nicht nur auf semantischer Ebene verknüpft, sondern daraus sogar neue, nicht explizit enthaltene Informationen abgeleitet werden. Ansätze dazu, wie Dokumente mit semantischen Metadaten versehen werden können, gibt es bereits seit einiger Zeit. Lange umfasste dies jedoch die redundante Bereitstellung der Informationen in einem eigenen Dokumentenformat, weswegen sich keines der Konzepte bis in den Privatbereich durchsetzen konnte und als Endkonsequenz in den vergangenen Monaten besonderes Forschungsinteresse darin aufkam, Möglichkeiten zu finden, wie semantische Informationen ohne großen Zusatzaufwand direkt in bestehende HTML-Dokumente eingebettet werden können. Die vorliegende Diplomarbeit möchte diese neuen Möglichkeiten im Bereich des kollaborativen Arbeitens näher untersuchen. Ziel ist es dazu, eine Webapplikation zur Abwicklung typischer Projektmanagement-Aufgaben zu entwickeln, welche jegliche Informationen unter einem semantischen Gesichtspunkt analysieren, aufbereiten und weiterverarbeiten kann und unabhängig von der konkreten Anwendungsdomain und Plattform systemübergreifend eingesetzt werden kann. Die Konzepte Microformats und RDFa werden dabei besonders herausgestellt und nach Schwächen und zukünftigen Potentialen hin untersucht
The World Wide Web supposably symbolizes with currently more than 120 million registered internet domains (March 2007) the most comprehensive information reference of all times. The amount of information available increases by a storming bulk of data ever day. Those information is often embedded in documents which utilize the Hypertext Markup Language. This enables the user to mark out certain layout properties of a text in an easy and efficient fashion and to publish the final document containing both layout and data information. A computer application is then able to extract style information from the document resource and to use it in order to render the resulting website. Although layout information and data are both equally represented in a textual manner, a machine was hardly capable of processing user content so far. Whereas human consumers have no problem to identify and understand the sense of several paragraphs on a website, they basically represent only a concatenation of ASCII characters for a machine. If it were possible to efficiently disclose the sense of a word or phrase to a computer program in order to process it, new astounding applications with output results of high quality would be possible. Users could create queries for specialized agents which autonomously start to search the web for adequate result matches. Moreover, the data of multiple information sources could be linked and processed together on a semantic level so that above all new, not explicitly stated information could be inferred. Approaches already exist, how documents could be enhanced with semantic metadata, however, many of these involve the redundant provision of those information in a specialized document format. As a consequence none of these concepts succeeded in becoming a widely used method and research started again to find possibilities how to embed semantic annotations without huge additional efforts in an ordinary HTML document. The present thesis focuses on an analysis of these new concepts and possibilities in the area of collaborative work. The objective is to develop the prototype of a web application with which it is possible to manage typical challenges in the realm of project and workflow management. Any information available should be processable under a semantic viewpoint which includes analysis, conditioning and reuse independently from a specific application domain and a certain system platform. Microformats and RDFa are two of those relatively new concepts which enable an application to extract semantic information from a document resource and are therefore particularly exposed and compared with respect to advantages and disadvantages in the context of a “Semantic Web”

This masters thesis deals with the Semantic Web, its link to existing Web and the technologies, that produce it. It also deals with its current use in practice, further examines the deployment in web content management systems and proposes semantic extensions for the Kentico CMS.

Dans cette thèse, nous étudions plusieurs modèles de collaboration entre l’ingénierie logiciel et le web sémantique. À partir de l’état de l’art, nous proposons une approche d’utilisation de l’ontologie dans la couche de métier d’une application. L’objectif principal de notre travail est de fournir au développeur des outils pour concevoir la matière déclarative une couche de métier "exécutable" d’une application afin de simuler son fonctionnement et de montrer ainsi la conformité de l’application par rapport aux exigences du client au début du cycle de vie du logiciel. Un autre avantage de cette approche est de permettre au développeur de partager et de réutiliser la description de la couche de métier d’une application dans un domaine en utilisant l’ontologie. Celle-ci est appelée "patron d’application". La réutilisation de la description de la couche de métier d’une application est un aspect intéressant à l'ingénier logiciel. C’est le point-clé que nous voulons considérer dans cette thèse. Dans la première partie de notre travail, nous traitons la modélisation de la couche de métier. Nous présentons d’abord une approche fondée sur l’ontologie pour représenter les processus de métiers et les règles de métiers et nous montrons comment vérifier la cohérence du processus et de l’ensemble des règles de métier. Puis, nous présentons le mécanisme de vérification automatique de la conformité d’un processus de métier avec un ensemble de règles de métier. La deuxième partie de cette thèse est consacrée à définir une méthodologie, dite de personnalisation, de création une application à partir d'un "patron d’application". Cette méthode permettra à l'utilisateur d'utiliser un patron d'application pour créer sa propre application en évitant les erreurs de structures et les erreurs sémantiques. Nous introduisons à la fin de cette partie, la description d’une plateforme expérimentale permettant d’illustrer la faisabilité des mécanismes proposés dans cette thèse. Cette plateforme est réalisée sur un SGBD relationnel
In this thesis, we study several models of collaboration between Software Engineering and Semantic Web. From the state of the art, we propose an approach to the use of ontology in the business application layer. The main objective of our work is to provide the developer with the tools to design, in the declarative manner, a business "executable" layer of an application in order to simulate its operation and thus show the compliance of the application with the customer requirements defined at the beginning of the software life cycle. On the other hand, another advantage of this approach is to allow the developer to share and reuse the business layer description of a typical application in a domain using ontology. This typical application description is called "Application Template". The reuse of the business layer description of an application is an interesting aspect of software engineering. That is the key point we want to consider in this thesis. In the first part of this thesis, we deal with the modeling of the business layer. We first present an ontology-based approach to represent business process and the business rules and show how to verify the consistency of business process and the set of business rules. Then, we present an automatic check mechanism of compliance of business process with a set of business rules. The second part of this thesis is devoted to define a methodology, called personalization, of creating of an application from an "Application Template". This methodology will allow the user to use an Application Template to create his own application by avoiding deadlock and semantic errors. We introduce at the end of this part the description of an experimental platform to illustrate the feasibility of the mechanisms proposed in the thesis. This platform s carried out on a relational DBMS.Finally, we present, in a final chapter, the conclusion, the perspective and other annexed works developed during this thesis

This master's thesis describes importing the ontology in language OWL 2 into the internal structures of 4A annotation server. It is concerned in anonymous nodes, for example in anonymous classes or anonymous properties. The solution was to use the library The OWL API for import ontology. The solution also allows automatic generation of names to anonymous classes and properties.

This thesis is a compendium of scientific works and engineering specifications that have been contributed to a large community of stakeholders to be copied, adapted, mixed, built upon and exploited in any way possible to achieve a common goal: Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data The explosion of information technology in the last two decades has led to a substantial growth in quantity, diversity and complexity of web-accessible linguistic data. These resources become even more useful when linked with each other and the last few years have seen the emergence of numerous approaches in various disciplines concerned with linguistic resources and NLP tools. It is the challenge of our time to store, interlink and exploit this wealth of data accumulated in more than half a century of computational linguistics, of empirical, corpus-based study of language, and of computational lexicography in all its heterogeneity. The vision of the Giant Global Graph (GGG) was conceived by Tim Berners-Lee aiming at connecting all data on the Web and allowing to discover new relations between this openly-accessible data. This vision has been pursued by the Linked Open Data (LOD) community, where the cloud of published datasets comprises 295 data repositories and more than 30 billion RDF triples (as of September 2011). RDF is based on globally unique and accessible URIs and it was specifically designed to establish links between such URIs (or resources). This is captured in the Linked Data paradigm that postulates four rules: (1) Referred entities should be designated by URIs, (2) these URIs should be resolvable over HTTP, (3) data should be represented by means of standards such as RDF, (4) and a resource should include links to other resources. Although it is difficult to precisely identify the reasons for the success of the LOD effort, advocates generally argue that open licenses as well as open access are key enablers for the growth of such a network as they provide a strong incentive for collaboration and contribution by third parties. In his keynote at BNCOD 2011, Chris Bizer argued that with RDF the overall data integration effort can be “split between data publishers, third parties, and the data consumer”, a claim that can be substantiated by observing the evolution of many large data sets constituting the LOD cloud. As written in the acknowledgement section, parts of this thesis has received numerous feedback from other scientists, practitioners and industry in many different ways. The main contributions of this thesis are summarized here: Part I – Introduction and Background. During his keynote at the Language Resource and Evaluation Conference in 2012, Sören Auer stressed the decentralized, collaborative, interlinked and interoperable nature of the Web of Data. The keynote provides strong evidence that Semantic Web technologies such as Linked Data are on its way to become main stream for the representation of language resources. The jointly written companion publication for the keynote was later extended as a book chapter in The People’s Web Meets NLP and serves as the basis for “Introduction” and “Background”, outlining some stages of the Linked Data publication and refinement chain. Both chapters stress the importance of open licenses and open access as an enabler for collaboration, the ability to interlink data on the Web as a key feature of RDF as well as provide a discussion about scalability issues and decentralization. Furthermore, we elaborate on how conceptual interoperability can be achieved by (1) re-using vocabularies, (2) agile ontology development, (3) meetings to refine and adapt ontologies and (4) tool support to enrich ontologies and match schemata. Part II - Language Resources as Linked Data. “Linked Data in Linguistics” and “NLP & DBpedia, an Upward Knowledge Acquisition Spiral” summarize the results of the Linked Data in Linguistics (LDL) Workshop in 2012 and the NLP & DBpedia Workshop in 2013 and give a preview of the MLOD special issue. In total, five proceedings – three published at CEUR (OKCon 2011, WoLE 2012, NLP & DBpedia 2013), one Springer book (Linked Data in Linguistics, LDL 2012) and one journal special issue (Multilingual Linked Open Data, MLOD to appear) – have been (co-)edited to create incentives for scientists to convert and publish Linked Data and thus to contribute open and/or linguistic data to the LOD cloud. Based on the disseminated call for papers, 152 authors contributed one or more accepted submissions to our venues and 120 reviewers were involved in peer-reviewing. “DBpedia as a Multilingual Language Resource” and “Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Linked Data Cloud” contain this thesis’ contribution to the DBpedia Project in order to further increase the size and inter-linkage of the LOD Cloud with lexical-semantic resources. Our contribution comprises extracted data from Wiktionary (an online, collaborative dictionary similar to Wikipedia) in more than four languages (now six) as well as language-specific versions of DBpedia, including a quality assessment of inter-language links between Wikipedia editions and internationalized content negotiation rules for Linked Data. In particular the work described in created the foundation for a DBpedia Internationalisation Committee with members from over 15 different languages with the common goal to push DBpedia as a free and open multilingual language resource. Part III - The NLP Interchange Format (NIF). “NIF 2.0 Core Specification”, “NIF 2.0 Resources and Architecture” and “Evaluation and Related Work” constitute one of the main contribution of this thesis. The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations. The core specification is included in and describes which URI schemes and RDF vocabularies must be used for (parts of) natural language texts and annotations in order to create an RDF/OWL-based interoperability layer with NIF built upon Unicode Code Points in Normal Form C. In , classes and properties of the NIF Core Ontology are described to formally define the relations between text, substrings and their URI schemes. contains the evaluation of NIF. In a questionnaire, we asked questions to 13 developers using NIF. UIMA, GATE and Stanbol are extensible NLP frameworks and NIF was not yet able to provide off-the-shelf NLP domain ontologies for all possible domains, but only for the plugins used in this study. After inspecting the software, the developers agreed however that NIF is adequate enough to provide a generic RDF output based on NIF using literal objects for annotations. All developers were able to map the internal data structure to NIF URIs to serialize RDF output (Adequacy). The development effort in hours (ranging between 3 and 40 hours) as well as the number of code lines (ranging between 110 and 445) suggest, that the implementation of NIF wrappers is easy and fast for an average developer. Furthermore the evaluation contains a comparison to other formats and an evaluation of the available URI schemes for web annotation. In order to collect input from the wide group of stakeholders, a total of 16 presentations were given with extensive discussions and feedback, which has lead to a constant improvement of NIF from 2010 until 2013. After the release of NIF (Version 1.0) in November 2011, a total of 32 vocabulary employments and implementations for different NLP tools and converters were reported (8 by the (co-)authors, including Wiki-link corpus, 13 by people participating in our survey and 11 more, of which we have heard). Several roll-out meetings and tutorials were held (e.g. in Leipzig and Prague in 2013) and are planned (e.g. at LREC 2014). Part IV - The NLP Interchange Format in Use. “Use Cases and Applications for NIF” and “Publication of Corpora using NIF” describe 8 concrete instances where NIF has been successfully used. One major contribution in is the usage of NIF as the recommended RDF mapping in the Internationalization Tag Set (ITS) 2.0 W3C standard and the conversion algorithms from ITS to NIF and back. One outcome of the discussions in the standardization meetings and telephone conferences for ITS 2.0 resulted in the conclusion there was no alternative RDF format or vocabulary other than NIF with the required features to fulfill the working group charter. Five further uses of NIF are described for the Ontology of Linguistic Annotations (OLiA), the RDFaCE tool, the Tiger Corpus Navigator, the OntosFeeder and visualisations of NIF using the RelFinder tool. These 8 instances provide an implemented proof-of-concept of the features of NIF. starts with describing the conversion and hosting of the huge Google Wikilinks corpus with 40 million annotations for 3 million web sites. The resulting RDF dump contains 477 million triples in a 5.6 GB compressed dump file in turtle syntax. describes how NIF can be used to publish extracted facts from news feeds in the RDFLiveNews tool as Linked Data. Part V - Conclusions. provides lessons learned for NIF, conclusions and an outlook on future work. Most of the contributions are already summarized above. One particular aspect worth mentioning is the increasing number of NIF-formated corpora for Named Entity Recognition (NER) that have come into existence after the publication of the main NIF paper Integrating NLP using Linked Data at ISWC 2013. These include the corpora converted by Steinmetz, Knuth and Sack for the NLP & DBpedia workshop and an OpenNLP-based CoNLL converter by Brümmer. Furthermore, we are aware of three LREC 2014 submissions that leverage NIF: NIF4OGGD - NLP Interchange Format for Open German Governmental Data, N^3 – A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format and Global Intelligent Content: Active Curation of Language Resources using Linked Data as well as an early implementation of a GATE-based NER/NEL evaluation framework by Dojchinovski and Kliegr. Further funding for the maintenance, interlinking and publication of Linguistic Linked Data as well as support and improvements of NIF is available via the expiring LOD2 EU project, as well as the CSA EU project called LIDER, which started in November 2013. Based on the evidence of successful adoption presented in this thesis, we can expect a decent to high chance of reaching critical mass of Linked Data technology as well as the NIF standard in the field of Natural Language Processing and Language Resources.

This thesis deals with the use of knowledge technologies in support of self-organization of people with joint political goals. It first provides a theoretical background for a development of a social-semantic system intended to support self-organization and then it applies this background in the development of a core ontology and algorithms for support of self-organization of people. It also presents a design and implementation of a proof-of-concept social-semantic web application that has been built to test our research. The application stores all data in an RDF store and represents them using the core ontology. Descriptions of content are disambiguated using the WordNet thesaurus. Emerging politically engaged groups can establish themselves into local political initiatives, NGOs, or even new political parties. Therefore, the system may help people easily participate on solutions of issues which are influencing them.

Dans le contexte du Web sémantique, les ontologies OWL représentent des connaissances explicites sur un domaine sur la base d'une conceptualisation des domaines d'intérêt, tandis que la connaissance correspondante sur les individus est donnée par les données RDF qui s'y réfèrent. Dans cette thèse, sur la base d'idées dérivées de l'ILP, nous visons à découvrir des motifs de connaissance cachés sous la forme de règles d'association multi-relationnelles en exploitant l'évidence provenant des assertions contenues dans les bases de connaissances ontologiques. Plus précisément, les règles découvertes sont codées en SWRL pour être facilement intégrées dans l'ontologie, enrichissant ainsi son pouvoir expressif et augmentant les connaissances sur les individus (assertions) qui en peuvent être dérivées. Deux algorithmes appliqués aux bases de connaissances ontologiques peuplées sont proposés pour trouver des règles à forte puissance inductive : (i) un algorithme de génération et test par niveaux et (ii) un algorithme évolutif. Nous avons effectué des expériences sur des ontologies accessibles au public, validant les performances de notre approche et les comparant avec les principaux systèmes de l'état de l'art. En outre, nous effectuons une comparaison des métriques asymétriques les plus répandues, proposées à l'origine pour la notation de règles d'association, comme éléments constitutifs d'une fonction de fitness pour l'algorithme évolutif afin de sélectionner les métriques qui conviennent à la sémantique des données. Afin d'améliorer les performances du système, nous avons proposé de construire un algorithme pour calculer les métriques au lieu d'interroger viaSPARQL-DL
In the Semantic Web context, OWL ontologies represent explicit domain knowledge based on the conceptualization of domains of interest while the corresponding assertional knowledge is given by RDF data referring to them. In this thesis, based on ideas derived from ILP, we aim at discovering hidden knowledge patterns in the form of multi-relational association rules by exploiting the evidence coming from the assertional data of ontological knowledge bases. Specifically, discovered rules are coded in SWRL to be easily integrated within the ontology, thus enriching its expressive power and augmenting the assertional knowledge that can be derived. Two algorithms applied to populated ontological knowledge bases are proposed for finding rules with a high inductive power: (i) level-wise generated-and-test algorithm and (ii) evolutionary algorithm. We performed experiments on publicly available ontologies, validating the performances of our approach and comparing them with the main state-of-the-art systems. In addition, we carry out a comparison of popular asymmetric metrics, originally proposed for scoring association rules, as building blocks for a fitness function for evolutionary algorithm to select metrics that are suitable with data semantics. In order to improve the system performance, we proposed to build an algorithm to compute metrics instead of querying via SPARQL-DL

Atualmente a popularidade da Web incentiva o desenvolvimento de sistemas hipermídia dedicados ao ensino a distância. Não obstante a maior parte destes sistemas usa os mesmos recursos do ensino tradicional, apresentado o conteúdo como páginas HTML estáticas, não fazendo uso das novas tecnologias que a Web oferece. Um desafío atual é desenvolver sistemas educativos que adaptem seu conteúdo ao estilo de aprendizagem, contexto e nível de conhecimento de cada aluno. Outro assunto de pesquisa é a capacidade de interoperar na Web reutilizando objetos de ensino. Este trabalho apresenta um enfoque que trata esses dois assuntos com as tecnologias da Web Semântica. O trabalho aqui apresentado modela o conhecimento do conteúdo educativo e do perfil do aluno pelo uso de ontologias cujo vocabulário é um refinamento de vocabulários padrões existentes na Web como pontos de referência para apoiar a interoperabilidade semântica. As ontologias permitem a representação numa linguagem formal dos metadados relativos a objetos de ensino simples e das regras que definem suas possíveis formas de agrupamento para desenvolver objetos mais complexos. Estes objetos mais complexos podem ser projetados para se adequar ao perfil de cada aluno por agentes inteligentes que usam a ontologia como origem de suas crenças. A reutilização de objetos de ensino entre diferentes aplicações é viabilizada pela construção de um perfil de aplicação do padrão IEEE LOM-Learning Object Metadata.
Nowadays, the popularity of the Web encourages the development of Hypermedia Systems dedicated to e-learning. Nevertheless, most of the available Web teaching systems apply the traditional paper-based learning resources presented as HTML pages making no use of the new capabilities provided by the Web. There is a challenge to develop educative systems that adapt the educative content to the style of learning, context and background of each student. Another research issue is the capacity to interoperate on the Web reusing learning objects. This work presents an approach to address these two issues by using the technologies of the Semantic Web. The approach presented here models the knowledge of the educative content and the learner’s profile with ontologies whose vocabularies are a refinement of those defined on standards situated on the Web as reference points to provide semantics. Ontologies enable the representation of metadata concerning simple learning objects and the rules that define the way that they can feasibly be assembled to configure more complex ones. These complex learning objects could be created dynamically according to the learners’ profile by intelligent agents that use the ontologies as the source of their beliefs. Interoperability issues were addressed by using an application profile of the IEEE LOM- Learning Object Metadata standard.

Dans les dernières années, le Web de données a connu une croissance fulgurante arrivant à un grand nombre des triples RDF. Un des objectifs les plus importants des applications RDF est l’intégration de données décrites dans les différents jeux de données RDF et la création des liens sémantiques entre eux. Ces liens expriment des correspondances sémantiques entre les entités d’ontologies ou entre les données. Parmi les différents types de liens sémantiques qui peuvent être établis, les liens d’identité expriment le fait que différentes ressources réfèrent au même objet du monde réel. Le nombre de liens d’identité déclaré reste souvent faible si on le compare au volume des données disponibles. Plusieurs approches de liage de données déduisent des liens d’identité en utilisant des clés. Une clé représente un ensemble de propriétés qui identifie de façon unique chaque ressource décrite par les données. Néanmoins, dans la plupart des jeux de données publiés sur le Web, les clés ne sont pas disponibles et leur déclaration peut être difficile, même pour un expert.L’objectif de cette thèse est d’étudier le problème de la découverte automatique de clés dans des sources de données RDF et de proposer de nouvelles approches efficaces pour résoudre ce problème. Les données publiées sur le Web sont général volumineuses, incomplètes, et peuvent contenir des informations erronées ou des doublons. Aussi, nous nous sommes focalisés sur la définition d’approches capables de découvrir des clés dans de tels jeux de données. Par conséquent, nous nous focalisons sur le développement d’approches de découverte de clés capables de gérer des jeux de données contenant des informations nombreuses, incomplètes ou erronées. Notre objectif est de découvrir autant de clés que possible, même celles qui sont valides uniquement dans des sous-ensembles de données.Nous introduisons tout d’abord KD2R, une approche qui permet la découverte automatique de clés composites dans des jeux de données RDF pour lesquels l’hypothèse du nom Unique est respectée. Ces données peuvent être conformées à des ontologies différentes. Pour faire face à l’incomplétude des données, KD2R propose deux heuristiques qui per- mettent de faire des hypothèses différentes sur les informations éventuellement absentes. Cependant, cette approche est difficilement applicable pour des sources de données de grande taille. Aussi, nous avons développé une seconde approche, SAKey, qui exploite différentes techniques de filtrage et d’élagage. De plus, SAKey permet à l’utilisateur de découvrir des clés dans des jeux de données qui contiennent des données erronées ou des doublons. Plus précisément, SAKey découvre des clés, appelées "almost keys", pour lesquelles un nombre d’exceptions est toléré
In the recent years, the Web of Data has increased significantly, containing a huge number of RDF triples. Integrating data described in different RDF datasets and creating semantic links among them, has become one of the most important goals of RDF applications. These links express semantic correspondences between ontology entities or data. Among the different kinds of semantic links that can be established, identity links express that different resources refer to the same real world entity. By comparing the number of resources published on the Web with the number of identity links, one can observe that the goal of building a Web of data is still not accomplished. Several data linking approaches infer identity links using keys. Nevertheless, in most datasets published on the Web, the keys are not available and it can be difficult, even for an expert, to declare them.The aim of this thesis is to study the problem of automatic key discovery in RDF data and to propose new efficient approaches to tackle this problem. Data published on the Web are usually created automatically, thus may contain erroneous information, duplicates or may be incomplete. Therefore, we focus on developing key discovery approaches that can handle datasets with numerous, incomplete or erroneous information. Our objective is to discover as many keys as possible, even ones that are valid in subparts of the data.We first introduce KD2R, an approach that allows the automatic discovery of composite keys in RDF datasets that may conform to different schemas. KD2R is able to treat datasets that may be incomplete and for which the Unique Name Assumption is fulfilled. To deal with the incompleteness of data, KD2R proposes two heuristics that offer different interpretations for the absence of data. KD2R uses pruning techniques to reduce the search space. However, this approach is overwhelmed by the huge amount of data found on the Web. Thus, we present our second approach, SAKey, which is able to scale in very large datasets by using effective filtering and pruning techniques. Moreover, SAKey is capable of discovering keys in datasets where erroneous data or duplicates may exist. More precisely, the notion of almost keys is proposed to describe sets of properties that are not keys due to few exceptions

This master's thesis deals with the design of a distributed information system, where the data distribution is based on semantic technologies. The project analyzes the semantic web technologies with the focus on information exchange between information systems and the related terms, mainly ontologies, ontology languages and the Resource description framework. Furthermore, there is described a proposal an ontology which is used to describe the data exchanged between the systems and the technologies used to implement distributed information system. The most important of them are Java Server Faces and Sesame.

With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange.

Present web is aiming to the possibility of automatization of user behavior on web applications. Adding of semantics and creation of web service interface are the main approaches for accomplishment of this user comfort. Nevertheless, this direction brings some problems which can make more difficult publishing and implementation of web documents. Web services can connect heterogeneous systems, because they are based on XML markup language that is a place where all applications can meet without lost of platform independence. The automatic transformation of a web application into a web service could be considerably more effective than to create a web service from the beginning. However, this step is for some applications almost unreal without knowledge of their inner structure. In most cases, the transformation will be done semiautomatically with help of human decisions.

This thesis deals with the analysis, design and implementation of a system based on semantic technologies and ontology languages. There is described the semantic web and its concept, technology of semantic web especially RDF and OWL and the ability to view on the Web. Another part of this work treats with the specification, analysis, design actual implementation of the system, which processes the ontology and allows users to create individual depending on the definition of the ontology. The created content is presented on the Web annotated. The result of this work is the demonstration application.

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Uma das tarefas críticas na confecção de sistemas de software é a elicitação de requisitos, a qual configura uma ação de descoberta de conhecimento. Assim, muitas técnicas são empregadas na tentativa de minimizar conflitos de idéias, conceitos mal formados, interpretações redundantes e omissão de dados; sendo que, para tanto, o uso de cenários, entrevistas, cartões, viewpoints e diagramas de Use Case são utilizados como ferramentas para diminuir a distância entre o técnico e o usuário na definição dos requisitos. Além disso, os Mapas Conceituais têm sido empregados com muita eficiência em tarefas de captura de conhecimento, portanto, este trabalho utiliza esse conceito como forma de organizar, identificar, aprimorar conceitos e definições dos requisitos de um software de forma cooperativa, formatado em User Story da metodologia Extreme Programming (XP). Com esse objetivo, o processo é apoiado por uma ferramenta baseada na web, que automatiza a geração, organização e acompanhamento da captura dos requisitos ge
One of the hardest tasks of building a software system is requirements elicitation, which triggers a knowledge discovery action. Thus, many techniques are used with the intention to minimize idea conflicts, misformed concepts, erroneous interpretations and missing data; In order to achieve this goal, scenarios interviews, User Stories, viewpoints and Use Case diagrams are techniques to reduce the distance between the researcher and the user on requirement elicitation. Concept maps have been used as efficient way to represent knowledge. This research uses concept maps to deal with the organization, identification and improvement of concepts and software requirements definitions in a cooperative way, making use of the User Story format introduced by the Extreme Programming (XP) methodology. The proposed process is supported by a web-based tool, which automates the generation, organization and management of the requirements capture generated in the Concept Maps format

Data masses on the World Wide Web can hardly be managed by humans or machines. One option is the formal description and linking of data sources using Semantic Web and Linked Data technologies. Ontologies written in standardised languages foster the sharing and linking of data as they provide a means to formally define concepts and relations between these concepts. A second option is visualisation. The visual representation allows humans to perceive information more directly, using the highly developed visual sense. Relatively few efforts have been made on combining both options, although the formality and rich semantics of ontological data make it an ideal candidate for visualisation. Advanced visualisation design systems support the visualisation of tabular, typically statistical data. However, visualisations of ontological data still have to be created manually, since automated solutions are often limited to generic lists or node-link diagrams. Also, the semantics of ontological data are not exploited for guiding users through visualisation tasks. Finally, once a good visualisation setting has been created, it cannot easily be reused and shared. Trying to tackle these problems, we had to answer how to define composable and shareable mappings from ontological data to visual means and how to guide the visual mapping of ontological data. We present an approach that allows for the guided visualisation of ontological data, the creation of effective graphics and the reuse of visualisation settings. Instead of generic graphics, we aim at tailor-made graphics, produced using the whole palette of visual means in a flexible, bottom-up approach. It not only allows for visualising ontologies, but uses ontologies to guide users when visualising data and to drive the visualisation process at various places: First, as a rich source of information on data characteristics, second, as a means to formally describe the vocabulary for building abstract graphics, and third, as a knowledge base of facts on visualisation. This is why we call our approach ontology-driven. We suggest generating an Abstract Visual Model (AVM) to represent and »synthesise« a graphic following a role-based approach, inspired by the one used by J. v. Engelhardt for the analysis of graphics. It consists of graphic objects and relations formalised in the Visualisation Ontology (VISO). A mappings model, based on the declarative RDFS/OWL Visualisation Language (RVL), determines a set of transformations from the domain data to the AVM. RVL allows for composable visual mappings that can be shared and reused across platforms. To guide the user, for example, we discourage the construction of mappings that are suboptimal according to an effectiveness ranking formalised in the fact base and suggest more effective mappings instead. The guidance process is flexible, since it is based on exchangeable rules. VISO, RVL and the AVM are additional contributions of this thesis. Further, we initially analysed the state of the art in visualisation and RDF-presentation comparing 10 approaches by 29 criteria. Our approach is unique because it combines ontology-driven guidance with composable visual mappings. Finally, we compare three prototypes covering the essential parts of our approach to show its feasibility. We show how the mapping process can be supported by tools displaying warning messages for non-optimal visual mappings, e.g., by considering relation characteristics such as »symmetry«. In a constructive evaluation, we challenge both the RVL language and the latest prototype trying to regenerate sketches of graphics we created manually during analysis. We demonstrate how graphics can be varied and complex mappings can be composed from simple ones. Two thirds of the sketches can be almost or completely specified and half of them can be almost or completely implemented
Datenmassen im World Wide Web können kaum von Menschen oder Maschinen erfasst werden. Eine Option ist die formale Beschreibung und Verknüpfung von Datenquellen mit Semantic-Web- und Linked-Data-Technologien. Ontologien, in standardisierten Sprachen geschrieben, befördern das Teilen und Verknüpfen von Daten, da sie ein Mittel zur formalen Definition von Konzepten und Beziehungen zwischen diesen Konzepten darstellen. Eine zweite Option ist die Visualisierung. Die visuelle Repräsentation ermöglicht es dem Menschen, Informationen direkter wahrzunehmen, indem er seinen hochentwickelten Sehsinn verwendet. Relativ wenige Anstrengungen wurden unternommen, um beide Optionen zu kombinieren, obwohl die Formalität und die reichhaltige Semantik ontologische Daten zu einem idealen Kandidaten für die Visualisierung machen. Visualisierungsdesignsysteme unterstützen Nutzer bei der Visualisierung von tabellarischen, typischerweise statistischen Daten. Visualisierungen ontologischer Daten jedoch müssen noch manuell erstellt werden, da automatisierte Lösungen häufig auf generische Listendarstellungen oder Knoten-Kanten-Diagramme beschränkt sind. Auch die Semantik der ontologischen Daten wird nicht ausgenutzt, um Benutzer durch Visualisierungsaufgaben zu führen. Einmal erstellte Visualisierungseinstellungen können nicht einfach wiederverwendet und geteilt werden. Um diese Probleme zu lösen, mussten wir eine Antwort darauf finden, wie die Definition komponierbarer und wiederverwendbarer Abbildungen von ontologischen Daten auf visuelle Mittel geschehen könnte und wie Nutzer bei dieser Abbildung geführt werden könnten. Wir stellen einen Ansatz vor, der die geführte Visualisierung von ontologischen Daten, die Erstellung effektiver Grafiken und die Wiederverwendung von Visualisierungseinstellungen ermöglicht. Statt auf generische Grafiken zielt der Ansatz auf maßgeschneiderte Grafiken ab, die mit der gesamten Palette visueller Mittel in einem flexiblen Bottom-Up-Ansatz erstellt werden. Er erlaubt nicht nur die Visualisierung von Ontologien, sondern verwendet auch Ontologien, um Benutzer bei der Visualisierung von Daten zu führen und den Visualisierungsprozess an verschiedenen Stellen zu steuern: Erstens als eine reichhaltige Informationsquelle zu Datencharakteristiken, zweitens als Mittel zur formalen Beschreibung des Vokabulars für den Aufbau von abstrakten Grafiken und drittens als Wissensbasis von Visualisierungsfakten. Deshalb nennen wir unseren Ansatz ontologie-getrieben. Wir schlagen vor, ein Abstract Visual Model (AVM) zu generieren, um eine Grafik rollenbasiert zu synthetisieren, angelehnt an einen Ansatz der von J. v. Engelhardt verwendet wird, um Grafiken zu analysieren. Das AVM besteht aus grafischen Objekten und Relationen, die in der Visualisation Ontology (VISO) formalisiert sind. Ein Mapping-Modell, das auf der deklarativen RDFS/OWL Visualisation Language (RVL) basiert, bestimmt eine Menge von Transformationen von den Quelldaten zum AVM. RVL ermöglicht zusammensetzbare »Mappings«, visuelle Abbildungen, die über Plattformen hinweg geteilt und wiederverwendet werden können. Um den Benutzer zu führen, bewerten wir Mappings anhand eines in der Faktenbasis formalisierten Effektivitätsrankings und schlagen ggf. effektivere Mappings vor. Der Beratungsprozess ist flexibel, da er auf austauschbaren Regeln basiert. VISO, RVL und das AVM sind weitere Beiträge dieser Arbeit. Darüber hinaus analysieren wir zunächst den Stand der Technik in der Visualisierung und RDF-Präsentation, indem wir 10 Ansätze nach 29 Kriterien vergleichen. Unser Ansatz ist einzigartig, da er eine ontologie-getriebene Nutzerführung mit komponierbaren visuellen Mappings vereint. Schließlich vergleichen wir drei Prototypen, welche die wesentlichen Teile unseres Ansatzes umsetzen, um seine Machbarkeit zu zeigen. Wir zeigen, wie der Mapping-Prozess durch Tools unterstützt werden kann, die Warnmeldungen für nicht optimale visuelle Abbildungen anzeigen, z. B. durch Berücksichtigung von Charakteristiken der Relationen wie »Symmetrie«. In einer konstruktiven Evaluation fordern wir sowohl die RVL-Sprache als auch den neuesten Prototyp heraus, indem wir versuchen Skizzen von Grafiken umzusetzen, die wir während der Analyse manuell erstellt haben. Wir zeigen, wie Grafiken variiert werden können und komplexe Mappings aus einfachen zusammengesetzt werden können. Zwei Drittel der Skizzen können fast vollständig oder vollständig spezifiziert werden und die Hälfte kann fast vollständig oder vollständig umgesetzt werden

Questa Tesi, nell’ambito del Semantic Web applicato al campo dei Beni Culturali (BC), si propone lo scopo di definire in un nuovo formato ontologico l’attuale sistema di registrazione e salvataggio delle informazioni che riguardano un Bene Culturale, ad esempio i dati identificativi, bibliografici, di scavo, ecc., attualmente registrati e salvati senza alcuna tecnologia Semantic Web. Nello specifico, questo progetto di Tesi si svilupperà considerando tra le tante schede che descrivono i termini per la catalogazione di un qualsiasi Bene Culturale, la scheda dei Reperti Archeologici (RA). Per fare questo verrà definito un mapping tra l’attuale sistema di registrazione delle informazioni di un Reperto Archeologico, e di conseguenza un nuovo dominio ontologico, in formato standardizzato RDF e OWL, seguendo le direttive sulle informazioni necessarie alla catalogazione dettate dagli organi del settore. Il risultato è una nuova ontologia, Central Institute for Cataloguing and Documentation Ontology CICDO, che a sua volta importa diverse ontologie e vocabolari tra cui: FOAF (Vocabolario Friend of a Friend), CITO (Citation Typing Ontology), Erlangen CRM (CIDOC Conceptual Reference Model), PROV-O, FaBiO (FRBR-aligned Bibliographic Ontology), HiCO (Historical Context Ontology), FRBR (Functional Requirements for Bibliographic Records), ed altre importate indirettamente. Nello specifico, CICDO definisce nuove entità e specializza quelle importate per un totale di: quarantasei classi e quarantasette object properties che descrivono le sezioni e le relazioni dei documenti da compilare di un reperto archeologico, e due datatype properties. Il mapping qui presentato è in forma tabellare, gli elementi sono raggruppati, quando serve, in sotto-tabelle, riproducendo parzialmente i paragrafi, campi e sotto-campi dei documenti ICCD di riferimento.

Das Semantic Web, eine Erweiterung des ursprünglichen World Wide Web um eine se- mantische Schicht, kann die Integration von Informationen aus verschiedenen Datenquellen stark vereinfachen. Mit RDF und der SPARQL-Anfragesprache wurden Standards etabliert, die eine einheitliche Darstellung von strukturierten Informationen ermöglichen und diese abfragbar machen. Mit Linked Data werden diese Informationen über ein einheitliches Pro- tokoll verfügbar gemacht und es entsteht ein Netz aus Daten, anstelle von Dokumenten. In der vorliegenden Arbeit werden Aspekte einer auf solchen semantischen Technologien basierenden Datenintegration betrachtet und analysiert. Darauf aufbauend wird ein System spezifiziert und implementiert, das die Ergebnisse dieser Untersuchungen in einer konkreten Anwendung realisiert. Als Basis für die Implementierung dient OntoWiki, ein semantisches Daten-Wiki.

Ένα υποσύνολο του τεράστιου όγκου πληροφοριών του Ιστού αφορά τα Ανοικτά Δεδομένα (Open Data), τα οποία αποτελούν πληροφορίες, δημόσιες ή άλλες, στις οποίες ο καθένας μπορεί να έχει πρόσβαση και να τις χρησιμοποιεί περαιτέρω για οποιονδήποτε σκοπό με στόχο να προσθέσει αξία σε αυτές. Η δυναμική των ανοιχτών δεδομένων γίνεται αντιληπτή όταν σύνολα δεδομένων των δημόσιων οργανισμών μετατρέπονται σε πραγματικά ανοιχτά δεδομένα, δηλαδή χωρίς νομικούς, οικονομικούς ή τεχνολογικούς περιορισμούς για την περαιτέρω χρήση τους από τρίτους. Τα ανοικτά δεδομένα ενός Τμήματος ή Σχολής Πανεπιστημίου μπορούν να δημιουργήσουν προστιθέμενη αξία και να έχουν θετικό αντίκτυπο σε πολλές διαφορετικές περιοχές, στη συμμετοχή, την καινοτομία, τη βελτίωση της αποδοτικότητας και αποτελεσματικότητας των Πανεπιστημιακών υπηρεσιών, την παραγωγή νέων γνώσεων από συνδυασμό στοιχείων κ.α. Ο τελικός στόχος είναι τα ανοικτά δεδομένα να καταστούν Ανοικτά Διασυνδεδεμένα Δεδομένα. Τα Διασυνδεδεμένα Δεδομένα, αποκτούν νόημα αντιληπτό και επεξεργάσιμο από μηχανές, επειδή περιγράφονται σημασιολογικά με την χρήση οντολογιών. Έτσι τα δεδομένα γίνονται πιο «έξυπνα» και πιο χρήσιμα μέσα από την διάρθρωση που αποκτούν. Στην παρούσα διπλωματική εργασία, υλοποιείται μια πρότυπη δικτυακή πύλη με την χρήση του Συστήματος Διαχείρισης Περιεχομένου CMS Drupal, το οποίο ενσωματώνει τεχνολογίες Σημασιολογικού Ιστού στον πυρήνα του, με σκοπό την μετατροπή των δεδομένων ενός Τμήματος ή Σχολής Πανεπιστημίου σε Ανοικτά Διασυνδεδεμένα Δεδομένα διαθέσιμα στην τρίτη γενιά του Ιστού τον Σημασιολογικό Ιστό.
A subset of the vast amount of information of the web is concerned with open data, which is information, whether public or other, in which everyone can have access and use it for any purpose with a view to add value. The dynamics of open data becomes noticeable when datasets of public bodies are transformed into truly open data , i.e. without legal, financial or technological limitations for further use by third parties. The open data of a university department or faculty can add value and have a positive impact on many different areas such as participation, innovation, improvisation of the efficiency and effectiveness of university services, generating new knowledge from a combination of elements , etc. The ultimate goal is to transform open data into open linked data. The linked data , become meaningful and processable by machines, given that they are semantically described, using ontologies. Thus, the data become more " intelligent " and more useful through the structure they acquire. In this thesis , a prototype web portal is implemented using the content management system CMS Drupal, which incorporates semantic web technologies in the core, in order to convert the data of a University Department or School in open linked data available in the third generation web semantic web.

Semantic Web ist eine der größten aktuellen Herausforderungen in der Informatik. Um Daten einem semantischen Wert zuzuweisen, werden Ontologien benutzt. Ontologien definieren und verwalten Konzepte. Letztere beschreiben Objekte, haben Eigenschaften und was hier bedeutender ist, Relationen zueinander. Diese Konzepte und Relationen werden mit Hilfe einer Spezifikation (OWL zum Beispiel) charakterisiert. Diesen Konzepten werden Instanzen zugeordnet. Das heisst, dass beispielweise mit dem Konzept ”Physiker“ die Instanz ”Albert Einstein“ verbunden wird. Um zu erfahren, was ”Albert Einstein“ mit der Stadt Berlin verbindet, gibt es Anfragesprachen, die bekannteste ist SPARQL. Ohne Vorkenntnisse der Struktur einer Ontologie, ist es nicht möglich, präzise Anfragen zu erstellen. Die einzige Möglichkeit herauszufinden was zwei Instanzen verbindet, ist die Nutzung einer SPARQL-Anfrage mit Platzhaltern, also eine Anfrage auf Instanzebene durchzuführen. Es ist viel Aufwand nötig, um eine Anfrage auf Instanzebene zu lösen ohne vorher zu wissen, wie diese Instanzen miteinander verknüpft sein können. Um eine solche Anfrage lösen zu können, müssen alle Relationen, die die erste Instanz betreffen, verfolgt werden, und von den so erreichten Instanzen diesen Vorgang weiterführen, bis die richtige Instanz gefunden wird. Die Instanzebene, auch A-Box genannt, ist die Ebene der tatsächlichen Elemente. Sie enthält zum Beispiel: ”Berlin ist die Hauptstadt von Deutschland“ . Als erstes muss hierbei ”Berlin“ als Instanz des richtigen Konzept erkannt werden. In diesem Fall müsste also ”Berlin“ als eine Instanz des Konzept ”Stadt“ erkannt werden. Die Rückfü ̈hrung zu einem bestimmten Konzept wird in dieser Arbeit als gelöst betrachtet.

Mit mehr als 120 Millionen registrierten Internetadressen (Stand: März 2007) symbolisiert das Internet heutzutage das größte Informationsmedium unserer Zeit. Täglich wächst das Internet um eine unüberschaubare Menge an Informationen. Diese Informationen sind häufig in Dokumenten hinterlegt, welche zur Auszeichnung die Hypertext Markup Language verwenden. Seit Beginn der Neunziger Jahre hat sich dieses System bewährt, da dadurch der einzelne Nutzer in die Lage versetzt wird, auf einfache und effiziente Weise Dokumentinhalte mit Darstellungsanweisungen zu versehen und diese eigenständig im Internet zu veröffentlichen. Diese Layoutinformationen können bei Abruf der entsprechenden Ressource durch ein Computerprogramm leicht ausgewertet und zur Darstellung der Inhalte genutzt werden. Obwohl sowohl die Layoutinformationen als auch die eigentlichen Dokumentinhalte in einem textuellen Format vorliegen, konnten die Nutzertextinhalte durch eine Maschine bisher nur sehr eingeschränkt verarbeitet werden. Während es menschlichen Nutzern keinerlei Probleme bereitet, die Bedeutung einzelner Texte auf einer Webseite zu identifizieren, stellen diese für einen Rechner prinzipiell nur eine Aneinanderreihung von ASCII-Zeichen dar. Sobald es möglich werden würde, die Bedeutung von Informationen durch ein Computerprogramm effizient zu erfassen und weiterzuverarbeiten, wären völlig neue Anwendungen mit qualitativ hochwertigeren Ergebnissen im weltweiten Datennetz möglich. Nutzer könnten Anfragen an spezielle Agenten stellen, welche sich selbstständig auf die Suche nach passenden Resultaten begeben; Informationen verschiedener Informationsquellen könnten nicht nur auf semantischer Ebene verknüpft, sondern daraus sogar neue, nicht explizit enthaltene Informationen abgeleitet werden. Ansätze dazu, wie Dokumente mit semantischen Metadaten versehen werden können, gibt es bereits seit einiger Zeit. Lange umfasste dies jedoch die redundante Bereitstellung der Informationen in einem eigenen Dokumentenformat, weswegen sich keines der Konzepte bis in den Privatbereich durchsetzen konnte und als Endkonsequenz in den vergangenen Monaten besonderes Forschungsinteresse darin aufkam, Möglichkeiten zu finden, wie semantische Informationen ohne großen Zusatzaufwand direkt in bestehende HTML-Dokumente eingebettet werden können. Die vorliegende Diplomarbeit möchte diese neuen Möglichkeiten im Bereich des kollaborativen Arbeitens näher untersuchen. Ziel ist es dazu, eine Webapplikation zur Abwicklung typischer Projektmanagement-Aufgaben zu entwickeln, welche jegliche Informationen unter einem semantischen Gesichtspunkt analysieren, aufbereiten und weiterverarbeiten kann und unabhängig von der konkreten Anwendungsdomain und Plattform systemübergreifend eingesetzt werden kann. Die Konzepte Microformats und RDFa werden dabei besonders herausgestellt und nach Schwächen und zukünftigen Potentialen hin untersucht.
The World Wide Web supposably symbolizes with currently more than 120 million registered internet domains (March 2007) the most comprehensive information reference of all times. The amount of information available increases by a storming bulk of data ever day. Those information is often embedded in documents which utilize the Hypertext Markup Language. This enables the user to mark out certain layout properties of a text in an easy and efficient fashion and to publish the final document containing both layout and data information. A computer application is then able to extract style information from the document resource and to use it in order to render the resulting website. Although layout information and data are both equally represented in a textual manner, a machine was hardly capable of processing user content so far. Whereas human consumers have no problem to identify and understand the sense of several paragraphs on a website, they basically represent only a concatenation of ASCII characters for a machine. If it were possible to efficiently disclose the sense of a word or phrase to a computer program in order to process it, new astounding applications with output results of high quality would be possible. Users could create queries for specialized agents which autonomously start to search the web for adequate result matches. Moreover, the data of multiple information sources could be linked and processed together on a semantic level so that above all new, not explicitly stated information could be inferred. Approaches already exist, how documents could be enhanced with semantic metadata, however, many of these involve the redundant provision of those information in a specialized document format. As a consequence none of these concepts succeeded in becoming a widely used method and research started again to find possibilities how to embed semantic annotations without huge additional efforts in an ordinary HTML document. The present thesis focuses on an analysis of these new concepts and possibilities in the area of collaborative work. The objective is to develop the prototype of a web application with which it is possible to manage typical challenges in the realm of project and workflow management. Any information available should be processable under a semantic viewpoint which includes analysis, conditioning and reuse independently from a specific application domain and a certain system platform. Microformats and RDFa are two of those relatively new concepts which enable an application to extract semantic information from a document resource and are therefore particularly exposed and compared with respect to advantages and disadvantages in the context of a “Semantic Web”.

Τα τελευταία χρόνια υπάρχει εκθετική αύξηση του όγκου της πληροφορίας που δημοσιεύεται στο Διαδίκτυο. Καθώς όμως η πληροφορία αυτή δε συνδέεται με τη σημασιολογία της παρατηρείται δυσκολία στη διαχείρισή της και στην πρόσβαση σε αυτήν. Ο Σημασιολογικός Ιστός, λοιπόν, είναι μια ομάδα μεθόδων και τεχνολογιών που σκοπεύουν να δώσουν τη δυνατότητα στις μηχανές να κατανοήσουν τη “σημασιολογία” των πληροφοριών σχετικά με τον Παγκόσμιο Ιστό. Ο Σημασιολογικός Ιστός (Semantic Web) αποτελεί επέκταση του Παγκοσμίου Ιστού. Στο Σημασιολογικό Ιστό οι πληροφορίες εμπλουτίζονται με μεταδεδομένα, τα οποία υπακουούν σε κοινά πρότυπα και επιτρέπουν την εξαγωγή γνώσεως από την ήδη υπάρχουσα, καθώς επίσης και το συνδυασμό της υπάρχουσας πληροφορίας με στόχο την εξαγωγή συμπερασμάτων. Απώτερος στόχος του Σημασιολογικού Ιστού είναι η βελτιωμένη αναζήτηση, η εκτέλεση σύνθετων διεργασιών και η εξατομίκευση της πληροφορίας σύμφωνα με τις ανάγκες του κάθε χρήστη. Στην παρούσα διπλωματική εργασία μελετήθηκε η χρήση των τεχνολογιών του Σημασιολογικού Ιστού για τη βελτίωση της πρόσβασης σε πολιτισμικά δεδομένα. Συγκεκριμένα αρχικά έγινε εμβάθυνση στις τεχνολογίες και στις θεμελιώδεις έννοιες του Σημασιολογικού Ιστού. Παρουσιάστηκαν αναλυτικά οι βασικές γλώσσες σήμανσης: XML που επιτρέπει τη δημιουργία δομημένων εγγράφων με λεξιλόγιο καθορισμένο από το χρήστη, RDF που προσφέρει ένα μοντέλο δεδομένων για την περιγραφή πληροφοριών με τέτοιο τρόπο ώστε να είναι δυνατή η ανάγνωση και η κατανόησή τους από μηχανές. Αναφέρθηκαν, ακόμη, οι διάφοροι τρόποι σύνταξης της γλώσσας RDF καθώς και πως γίνεται αναζήτηση σε γράφους RDF με το πρωτόκολλο SPARQL. Στη συνέχεια ακολουθεί η περιγραφή της RDFS, που πρόκειται για γλώσσα περιγραφής του RDF λεξιλογίου. Έχοντας παρουσιαστεί σε προηγούμενο κεφάλαιο η έννοια της οντολογίας, γίνεται αναφορά στη σημασιολογική γλώσσα σήμανσης OWL, που χρησιμοποιείται για την έκδοση και διανομή οντολογιών στο Διαδίκτυο. Έπειτα ακολουθεί μια ανασκόπηση από επιλεγμένα έργα, ελληνικά, ευρωπαϊκά και διεθνή, των τελευταίων ετών που χρησιμοποιούν τις τεχνολογίες του Σημασιολογικού Ιστού στο τομέα του πολιτισμού και της πολιτισμικής κληρονομιάς. Τέλος στο έβδομο κεφάλαιο παρουσιάζεται μία εφαρμογή διαχείρισης αρχαιολογικών χώρων-μνημείων και μελετώνται σε βάθος οι τεχνολογίες και τα εργαλεία που χρησιμοποιήθηκαν για την υλοποίησή της.
Over the past few years there has been exponential increase of the volume of information published on the Internet. Since information is not connected to its semantics, it is difficult to manipulate and access it. Therefore, the Semantic Web consists of methods and technologies that aim to enable machines to understand information’s semantics. The Semantic Web is an extension of the World Wide Web (WWW). Specifically, information is enriched with metadata, which are subject to common standards and permit knowledge extraction from the existing one and the combination of existing information in order to infer implicit knowledge, as well. Future goals of the Semantic Web are enhanced searching, complicated processes’ execution and information personalization according to each user’s needs. This post-graduate diploma thesis researches the usage of Semantic Web technologies for the enhancement of the access to cultural data. More specifically, Semantic Web technologies and essential concepts were studied. Basic markup languages were presented analytically: XML that allows structured documents’ creation with user defined vocabulary, RDF that offers a data model for such information description that it is readable and understandable by machines. Also, various RDF syntaxes and how to search RDF graphs using SPARQL protocol were referred. Below RDFS description follows, that is a description language of RDF vocabulary. After having introduced the concept of ontology in previous chapter, the semantic markup language OWL is presented, that is used for ontology publishing and distribution on the Internet. A review of selected projects of the last years, Greek, European and international, which are characterized by the application of technologies of the Semantic Web in the sector of Culture and Cultural heritage, is presented. In the last chapter, an application that manages archaeological places- sites is presented and it is studied technologies and tools that were used for it.

This thesis is a compendium of scientific works and engineering specifications that have been contributed to a large community of stakeholders to be copied, adapted, mixed, built upon and exploited in any way possible to achieve a common goal: Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data The explosion of information technology in the last two decades has led to a substantial growth in quantity, diversity and complexity of web-accessible linguistic data. These resources become even more useful when linked with each other and the last few years have seen the emergence of numerous approaches in various disciplines concerned with linguistic resources and NLP tools. It is the challenge of our time to store, interlink and exploit this wealth of data accumulated in more than half a century of computational linguistics, of empirical, corpus-based study of language, and of computational lexicography in all its heterogeneity. The vision of the Giant Global Graph (GGG) was conceived by Tim Berners-Lee aiming at connecting all data on the Web and allowing to discover new relations between this openly-accessible data. This vision has been pursued by the Linked Open Data (LOD) community, where the cloud of published datasets comprises 295 data repositories and more than 30 billion RDF triples (as of September 2011). RDF is based on globally unique and accessible URIs and it was specifically designed to establish links between such URIs (or resources). This is captured in the Linked Data paradigm that postulates four rules: (1) Referred entities should be designated by URIs, (2) these URIs should be resolvable over HTTP, (3) data should be represented by means of standards such as RDF, (4) and a resource should include links to other resources. Although it is difficult to precisely identify the reasons for the success of the LOD effort, advocates generally argue that open licenses as well as open access are key enablers for the growth of such a network as they provide a strong incentive for collaboration and contribution by third parties. In his keynote at BNCOD 2011, Chris Bizer argued that with RDF the overall data integration effort can be “split between data publishers, third parties, and the data consumer”, a claim that can be substantiated by observing the evolution of many large data sets constituting the LOD cloud. As written in the acknowledgement section, parts of this thesis has received numerous feedback from other scientists, practitioners and industry in many different ways. The main contributions of this thesis are summarized here: Part I – Introduction and Background. During his keynote at the Language Resource and Evaluation Conference in 2012, Sören Auer stressed the decentralized, collaborative, interlinked and interoperable nature of the Web of Data. The keynote provides strong evidence that Semantic Web technologies such as Linked Data are on its way to become main stream for the representation of language resources. The jointly written companion publication for the keynote was later extended as a book chapter in The People’s Web Meets NLP and serves as the basis for “Introduction” and “Background”, outlining some stages of the Linked Data publication and refinement chain. Both chapters stress the importance of open licenses and open access as an enabler for collaboration, the ability to interlink data on the Web as a key feature of RDF as well as provide a discussion about scalability issues and decentralization. Furthermore, we elaborate on how conceptual interoperability can be achieved by (1) re-using vocabularies, (2) agile ontology development, (3) meetings to refine and adapt ontologies and (4) tool support to enrich ontologies and match schemata. Part II - Language Resources as Linked Data. “Linked Data in Linguistics” and “NLP & DBpedia, an Upward Knowledge Acquisition Spiral” summarize the results of the Linked Data in Linguistics (LDL) Workshop in 2012 and the NLP & DBpedia Workshop in 2013 and give a preview of the MLOD special issue. In total, five proceedings – three published at CEUR (OKCon 2011, WoLE 2012, NLP & DBpedia 2013), one Springer book (Linked Data in Linguistics, LDL 2012) and one journal special issue (Multilingual Linked Open Data, MLOD to appear) – have been (co-)edited to create incentives for scientists to convert and publish Linked Data and thus to contribute open and/or linguistic data to the LOD cloud. Based on the disseminated call for papers, 152 authors contributed one or more accepted submissions to our venues and 120 reviewers were involved in peer-reviewing. “DBpedia as a Multilingual Language Resource” and “Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Linked Data Cloud” contain this thesis’ contribution to the DBpedia Project in order to further increase the size and inter-linkage of the LOD Cloud with lexical-semantic resources. Our contribution comprises extracted data from Wiktionary (an online, collaborative dictionary similar to Wikipedia) in more than four languages (now six) as well as language-specific versions of DBpedia, including a quality assessment of inter-language links between Wikipedia editions and internationalized content negotiation rules for Linked Data. In particular the work described in created the foundation for a DBpedia Internationalisation Committee with members from over 15 different languages with the common goal to push DBpedia as a free and open multilingual language resource. Part III - The NLP Interchange Format (NIF). “NIF 2.0 Core Specification”, “NIF 2.0 Resources and Architecture” and “Evaluation and Related Work” constitute one of the main contribution of this thesis. The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations. The core specification is included in and describes which URI schemes and RDF vocabularies must be used for (parts of) natural language texts and annotations in order to create an RDF/OWL-based interoperability layer with NIF built upon Unicode Code Points in Normal Form C. In , classes and properties of the NIF Core Ontology are described to formally define the relations between text, substrings and their URI schemes. contains the evaluation of NIF. In a questionnaire, we asked questions to 13 developers using NIF. UIMA, GATE and Stanbol are extensible NLP frameworks and NIF was not yet able to provide off-the-shelf NLP domain ontologies for all possible domains, but only for the plugins used in this study. After inspecting the software, the developers agreed however that NIF is adequate enough to provide a generic RDF output based on NIF using literal objects for annotations. All developers were able to map the internal data structure to NIF URIs to serialize RDF output (Adequacy). The development effort in hours (ranging between 3 and 40 hours) as well as the number of code lines (ranging between 110 and 445) suggest, that the implementation of NIF wrappers is easy and fast for an average developer. Furthermore the evaluation contains a comparison to other formats and an evaluation of the available URI schemes for web annotation. In order to collect input from the wide group of stakeholders, a total of 16 presentations were given with extensive discussions and feedback, which has lead to a constant improvement of NIF from 2010 until 2013. After the release of NIF (Version 1.0) in November 2011, a total of 32 vocabulary employments and implementations for different NLP tools and converters were reported (8 by the (co-)authors, including Wiki-link corpus, 13 by people participating in our survey and 11 more, of which we have heard). Several roll-out meetings and tutorials were held (e.g. in Leipzig and Prague in 2013) and are planned (e.g. at LREC 2014). Part IV - The NLP Interchange Format in Use. “Use Cases and Applications for NIF” and “Publication of Corpora using NIF” describe 8 concrete instances where NIF has been successfully used. One major contribution in is the usage of NIF as the recommended RDF mapping in the Internationalization Tag Set (ITS) 2.0 W3C standard and the conversion algorithms from ITS to NIF and back. One outcome of the discussions in the standardization meetings and telephone conferences for ITS 2.0 resulted in the conclusion there was no alternative RDF format or vocabulary other than NIF with the required features to fulfill the working group charter. Five further uses of NIF are described for the Ontology of Linguistic Annotations (OLiA), the RDFaCE tool, the Tiger Corpus Navigator, the OntosFeeder and visualisations of NIF using the RelFinder tool. These 8 instances provide an implemented proof-of-concept of the features of NIF. starts with describing the conversion and hosting of the huge Google Wikilinks corpus with 40 million annotations for 3 million web sites. The resulting RDF dump contains 477 million triples in a 5.6 GB compressed dump file in turtle syntax. describes how NIF can be used to publish extracted facts from news feeds in the RDFLiveNews tool as Linked Data. Part V - Conclusions. provides lessons learned for NIF, conclusions and an outlook on future work. Most of the contributions are already summarized above. One particular aspect worth mentioning is the increasing number of NIF-formated corpora for Named Entity Recognition (NER) that have come into existence after the publication of the main NIF paper Integrating NLP using Linked Data at ISWC 2013. These include the corpora converted by Steinmetz, Knuth and Sack for the NLP & DBpedia workshop and an OpenNLP-based CoNLL converter by Brümmer. Furthermore, we are aware of three LREC 2014 submissions that leverage NIF: NIF4OGGD - NLP Interchange Format for Open German Governmental Data, N^3 – A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format and Global Intelligent Content: Active Curation of Language Resources using Linked Data as well as an early implementation of a GATE-based NER/NEL evaluation framework by Dojchinovski and Kliegr. Further funding for the maintenance, interlinking and publication of Linguistic Linked Data as well as support and improvements of NIF is available via the expiring LOD2 EU project, as well as the CSA EU project called LIDER, which started in November 2013. Based on the evidence of successful adoption presented in this thesis, we can expect a decent to high chance of reaching critical mass of Linked Data technology as well as the NIF standard in the field of Natural Language Processing and Language Resources.:CONTENTS i introduction and background 1 1 introduction 3 1.1 Natural Language Processing . . . . . . . . . . . . . . . 3 1.2 Open licenses, open access and collaboration . . . . . . 5 1.3 Linked Data in Linguistics . . . . . . . . . . . . . . . . . 6 1.4 NLP for and by the Semantic Web – the NLP Inter- change Format (NIF) . . . . . . . . . . . . . . . . . . . . 8 1.5 Requirements for NLP Integration . . . . . . . . . . . . 10 1.6 Overview and Contributions . . . . . . . . . . . . . . . 11 2 background 15 2.1 The Working Group on Open Data in Linguistics (OWLG) 15 2.1.1 The Open Knowledge Foundation . . . . . . . . 15 2.1.2 Goals of the Open Linguistics Working Group . 16 2.1.3 Open linguistics resources, problems and chal- lenges . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.4 Recent activities and on-going developments . . 18 2.2 Technological Background . . . . . . . . . . . . . . . . . 18 2.3 RDF as a data model . . . . . . . . . . . . . . . . . . . . 21 2.4 Performance and scalability . . . . . . . . . . . . . . . . 22 2.5 Conceptual interoperability . . . . . . . . . . . . . . . . 22 ii language resources as linked data 25 3 linked data in linguistics 27 3.1 Lexical Resources . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Linguistic Corpora . . . . . . . . . . . . . . . . . . . . . 30 3.3 Linguistic Knowledgebases . . . . . . . . . . . . . . . . 31 3.4 Towards a Linguistic Linked Open Data Cloud . . . . . 32 3.5 State of the Linguistic Linked Open Data Cloud in 2012 33 3.6 Querying linked resources in the LLOD . . . . . . . . . 36 3.6.1 Enriching metadata repositories with linguistic features (Glottolog → OLiA) . . . . . . . . . . . 36 3.6.2 Enriching lexical-semantic resources with lin- guistic information (DBpedia (→ POWLA) → OLiA) . . . . . . . . . . . . . . . . . . . . . . . . 38 4 DBpedia as a multilingual language resource: the case of the greek dbpedia edition. 39 4.1 Current state of the internationalization effort . . . . . 40 4.2 Language-specific design of DBpedia resource identifiers 41 4.3 Inter-DBpedia linking . . . . . . . . . . . . . . . . . . . 42 4.4 Outlook on DBpedia Internationalization . . . . . . . . 44 5 leveraging the crowdsourcing of lexical resources for bootstrapping a linguistic linked data cloud 47 5.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2 Problem Description . . . . . . . . . . . . . . . . . . . . 50 5.2.1 Processing Wiki Syntax . . . . . . . . . . . . . . 50 5.2.2 Wiktionary . . . . . . . . . . . . . . . . . . . . . . 52 5.2.3 Wiki-scale Data Extraction . . . . . . . . . . . . . 53 5.3 Design and Implementation . . . . . . . . . . . . . . . . 54 5.3.1 Extraction Templates . . . . . . . . . . . . . . . . 56 5.3.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . 56 5.3.3 Language Mapping . . . . . . . . . . . . . . . . . 58 5.3.4 Schema Mediation by Annotation with lemon . 58 5.4 Resulting Data . . . . . . . . . . . . . . . . . . . . . . . . 58 5.5 Lessons Learned . . . . . . . . . . . . . . . . . . . . . . . 60 5.6 Discussion and Future Work . . . . . . . . . . . . . . . 60 5.6.1 Next Steps . . . . . . . . . . . . . . . . . . . . . . 61 5.6.2 Open Research Questions . . . . . . . . . . . . . 61 6 nlp & dbpedia, an upward knowledge acquisition spiral 63 6.1 Knowledge acquisition and structuring . . . . . . . . . 64 6.2 Representation of knowledge . . . . . . . . . . . . . . . 65 6.3 NLP tasks and applications . . . . . . . . . . . . . . . . 65 6.3.1 Named Entity Recognition . . . . . . . . . . . . 66 6.3.2 Relation extraction . . . . . . . . . . . . . . . . . 67 6.3.3 Question Answering over Linked Data . . . . . 67 6.4 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.4.1 Gold and silver standards . . . . . . . . . . . . . 69 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 iii the nlp interchange format (nif) 73 7 nif 2.0 core specification 75 7.1 Conformance checklist . . . . . . . . . . . . . . . . . . . 75 7.2 Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.2.1 Definition of Strings . . . . . . . . . . . . . . . . 78 7.2.2 Representation of Document Content with the nif:Context Class . . . . . . . . . . . . . . . . . . 80 7.3 Extension of NIF . . . . . . . . . . . . . . . . . . . . . . 82 7.3.1 Part of Speech Tagging with OLiA . . . . . . . . 83 7.3.2 Named Entity Recognition with ITS 2.0, DBpe- dia and NERD . . . . . . . . . . . . . . . . . . . 84 7.3.3 lemon and Wiktionary2RDF . . . . . . . . . . . 86 8 nif 2.0 resources and architecture 89 8.1 NIF Core Ontology . . . . . . . . . . . . . . . . . . . . . 89 8.1.1 Logical Modules . . . . . . . . . . . . . . . . . . 90 8.2 Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . 91 8.2.1 Access via REST Services . . . . . . . . . . . . . 92 8.2.2 NIF Combinator Demo . . . . . . . . . . . . . . 92 8.3 Granularity Profiles . . . . . . . . . . . . . . . . . . . . . 93 8.4 Further URI Schemes for NIF . . . . . . . . . . . . . . . 95 8.4.1 Context-Hash-based URIs . . . . . . . . . . . . . 99 9 evaluation and related work 101 9.1 Questionnaire and Developers Study for NIF 1.0 . . . . 101 9.2 Qualitative Comparison with other Frameworks and Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 9.3 URI Stability Evaluation . . . . . . . . . . . . . . . . . . 103 9.4 Related URI Schemes . . . . . . . . . . . . . . . . . . . . 104 iv the nlp interchange format in use 109 10 use cases and applications for nif 111 10.1 Internationalization Tag Set 2.0 . . . . . . . . . . . . . . 111 10.1.1 ITS2NIF and NIF2ITS conversion . . . . . . . . . 112 10.2 OLiA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 10.3 RDFaCE . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 10.4 Tiger Corpus Navigator . . . . . . . . . . . . . . . . . . 121 10.4.1 Tools and Resources . . . . . . . . . . . . . . . . 122 10.4.2 NLP2RDF in 2010 . . . . . . . . . . . . . . . . . . 123 10.4.3 Linguistic Ontologies . . . . . . . . . . . . . . . . 124 10.4.4 Implementation . . . . . . . . . . . . . . . . . . . 125 10.4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . 126 10.4.6 Related Work and Outlook . . . . . . . . . . . . 129 10.5 OntosFeeder – a Versatile Semantic Context Provider for Web Content Authoring . . . . . . . . . . . . . . . . 131 10.5.1 Feature Description and User Interface Walk- through . . . . . . . . . . . . . . . . . . . . . . . 132 10.5.2 Architecture . . . . . . . . . . . . . . . . . . . . . 134 10.5.3 Embedding Metadata . . . . . . . . . . . . . . . 135 10.5.4 Related Work and Summary . . . . . . . . . . . 135 10.6 RelFinder: Revealing Relationships in RDF Knowledge Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 10.6.1 Implementation . . . . . . . . . . . . . . . . . . . 137 10.6.2 Disambiguation . . . . . . . . . . . . . . . . . . . 138 10.6.3 Searching for Relationships . . . . . . . . . . . . 139 10.6.4 Graph Visualization . . . . . . . . . . . . . . . . 140 10.6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . 141 11 publication of corpora using nif 143 11.1 Wikilinks Corpus . . . . . . . . . . . . . . . . . . . . . . 143 11.1.1 Description of the corpus . . . . . . . . . . . . . 143 11.1.2 Quantitative Analysis with Google Wikilinks Cor- pus . . . . . . . . . . . . . . . . . . . . . . . . . . 144 11.2 RDFLiveNews . . . . . . . . . . . . . . . . . . . . . . . . 144 11.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . 145 11.2.2 Mapping to RDF and Publication on the Web of Data . . . . . . . . . . . . . . . . . . . . . . . . . 146 v conclusions 149 12 lessons learned, conclusions and future work 151 12.1 Lessons Learned for NIF . . . . . . . . . . . . . . . . . . 151 12.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 151 12.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 153

SweetDeal is a rule-based approach to representation of business contracts that enables software agents to create, evaluate, negotiate, and execute contracts with substantial automation and modularity. It builds upon the situated courteous logic programs knowledge representation in RuleML, the emerging standard for Semantic Web XML rules. Here, we newly extend the SweetDeal approach by also incorporating process knowledge descriptions whose ontologies are represented in DAML+OIL (the close predecessor of W3C's OWL, the emerging standard for Semantic Web ontologies), thereby enabling more complex contracts with behavioral provisions, especially for handling exception conditions (e.g., late delivery or non-payment) that might arise during the execution of the contract. This provides a foundation for representing and automating deals about services – in particular, about Web Services, so as to help search, select, and compose them. We give a detailed application scenario of late delivery in manufacturing supply chain management (SCM). In doing so, we draw upon our new formalization of process ontology knowledge from the MIT Process Handbook, a large, previously-existing repository used by practical industrial process designers. Our system is the first to combine emerging Semantic Web standards for knowledge representation of rules (RuleML) with ontologies (DAML+OIL/OWL) with each other, and moreover for a practical e-business application domain, and further to do so with process knowledge. This also newly fleshes out the evolving concept of Semantic Web Services. A prototype (soon public) i

Data masses on the World Wide Web can hardly be managed by humans or machines. One option is the formal description and linking of data sources using Semantic Web and Linked Data technologies. Ontologies written in standardised languages foster the sharing and linking of data as they provide a means to formally define concepts and relations between these concepts. A second option is visualisation. The visual representation allows humans to perceive information more directly, using the highly developed visual sense. Relatively few efforts have been made on combining both options, although the formality and rich semantics of ontological data make it an ideal candidate for visualisation. Advanced visualisation design systems support the visualisation of tabular, typically statistical data. However, visualisations of ontological data still have to be created manually, since automated solutions are often limited to generic lists or node-link diagrams. Also, the semantics of ontological data are not exploited for guiding users through visualisation tasks. Finally, once a good visualisation setting has been created, it cannot easily be reused and shared. Trying to tackle these problems, we had to answer how to define composable and shareable mappings from ontological data to visual means and how to guide the visual mapping of ontological data. We present an approach that allows for the guided visualisation of ontological data, the creation of effective graphics and the reuse of visualisation settings. Instead of generic graphics, we aim at tailor-made graphics, produced using the whole palette of visual means in a flexible, bottom-up approach. It not only allows for visualising ontologies, but uses ontologies to guide users when visualising data and to drive the visualisation process at various places: First, as a rich source of information on data characteristics, second, as a means to formally describe the vocabulary for building abstract graphics, and third, as a knowledge base of facts on visualisation. This is why we call our approach ontology-driven. We suggest generating an Abstract Visual Model (AVM) to represent and »synthesise« a graphic following a role-based approach, inspired by the one used by J. v. Engelhardt for the analysis of graphics. It consists of graphic objects and relations formalised in the Visualisation Ontology (VISO). A mappings model, based on the declarative RDFS/OWL Visualisation Language (RVL), determines a set of transformations from the domain data to the AVM. RVL allows for composable visual mappings that can be shared and reused across platforms. To guide the user, for example, we discourage the construction of mappings that are suboptimal according to an effectiveness ranking formalised in the fact base and suggest more effective mappings instead. The guidance process is flexible, since it is based on exchangeable rules. VISO, RVL and the AVM are additional contributions of this thesis. Further, we initially analysed the state of the art in visualisation and RDF-presentation comparing 10 approaches by 29 criteria. Our approach is unique because it combines ontology-driven guidance with composable visual mappings. Finally, we compare three prototypes covering the essential parts of our approach to show its feasibility. We show how the mapping process can be supported by tools displaying warning messages for non-optimal visual mappings, e.g., by considering relation characteristics such as »symmetry«. In a constructive evaluation, we challenge both the RVL language and the latest prototype trying to regenerate sketches of graphics we created manually during analysis. We demonstrate how graphics can be varied and complex mappings can be composed from simple ones. Two thirds of the sketches can be almost or completely specified and half of them can be almost or completely implemented.:Legend and Overview of Prefixes xiii 1 Introduction 1 2 Background 11 2.1 Visualisation 11 2.1.1 What is Visualisation? 11 2.1.2 What are the Benefits of Visualisation? 12 2.1.3 Visualisation Related Terms Used in this Thesis 12 2.1.4 Visualisation Models and Architectural Patterns 12 2.1.5 Visualisation Design Systems 14 2.1.6 What is the Difference between Visual Mapping and Styling? 14 2.1.7 Lessons Learned from Style Sheet Languages 15 2.2 Data 16 2.2.1 Data – Information – Knowledge 17 2.2.2 Structured Data 17 2.2.3 Ontologies in Computer Science 19 2.2.4 The Semantic Web and its Languages 19 2.2.5 Linked Data and Open Data 20 2.2.6 The Metamodelling Technological Space 21 2.2.7 SPIN 21 2.3 Guidance 22 2.3.1 Guidance in Visualisation 22 3 Problem Analysis 23 3.1 Problems of Ontology Visualisation Approaches 24 3.2 Research Questions 25 3.3 Set up of the Case Studies 25 3.3.1 Case Studies in the Life Sciences Domain 26 3.3.2 Case Studies in the Publishing Domain 26 3.3.3 Case Studies in the Software Technology Domain 27 3.4 Analysis of the Case Studies’ Ontologies 27 3.5 Manual Sketching of Graphics 29 3.6 Analysis of the Graphics for Typical Visualisation Cases 29 3.7 Requirements 33 3.7.1 Requirements for Visualisation and Interaction 34 3.7.2 Requirements for Data Awareness 34 3.7.3 Requirements for Reuse and Composition 34 3.7.4 Requirements for Variability 35 3.7.5 Requirements for Tooling Support and Guidance 35 3.7.6 Optional Features and Limitations 36 4 Analysis of the State of the Art 37 4.1 Related Visualisation Approaches 38 4.1.1 Short Overview of the Approaches 38 4.1.2 Detailed Comparison by Criteria 46 4.1.3 Conclusion – What Is Still Missing? 60 4.2 Visualisation Languages 62 4.2.1 Short Overview of the Compared Languages 62 4.2.2 Detailed Comparison by Language Criteria 66 4.2.3 Conclusion – What Is Still Missing? 71 4.3 RDF Presentation Languages 72 4.3.1 Short Overview of the Compared Languages 72 4.3.2 Detailed Comparison by Language Criteria 76 4.3.3 Additional Criteria for RDF Display Languages 87 4.3.4 Conclusion – What Is Still Missing? 89 4.4 Model-Driven Interfaces 90 4.4.1 Metamodel-Driven Interfaces 90 4.4.2 Ontology-Driven Interfaces 92 4.4.3 Combined Usage of the Metamodelling and Ontology Technological Space 94 5 A Visualisation Ontology – VISO 97 5.1 Methodology Used for Ontology Creation 100 5.2 Requirements for a Visualisation Ontology 100 5.3 Existing Approaches to Modelling in the Field of Visualisation 101 5.3.1 Terminologies and Taxonomies 101 5.3.2 Existing Visualisation Ontologies 102 5.3.3 Other Visualisation Models and Approaches to Formalisation 103 5.3.4 Summary 103 5.4 Technical Aspects of VISO 103 5.5 VISO/graphic Module – Graphic Vocabulary 104 5.5.1 Graphic Representations and Graphic Objects 105 5.5.2 Graphic Relations and Syntactic Structures 107 5.6 VISO/data Module – Characterising Data 110 5.6.1 Data Structure and Characteristics of Relations 110 5.6.2 The Scale of Measurement and Units 112 5.6.3 Properties for Characterising Data Variables in Statistical Data 113 5.7 VISO/facts Module – Facts for Vis. Constraints and Rules 115 5.7.1 Expressiveness of Graphic Relations 116 5.7.2 Effectiveness Ranking of Graphic Relations 118 5.7.3 Rules for Composing Graphics 119 5.7.4 Other Rules to Consider for Visual Mapping 124 5.7.5 Providing Named Value Collections 124 5.7.6 Existing Approaches to the Formalisation of Visualisation Knowledge . . 126 5.7.7 The VISO/facts/empiric Example Knowledge Base 126 5.8 Other VISO Modules 126 5.9 Conclusions and Future Work 127 5.10 Further Use Cases for VISO 127 5.11 VISO on the Web – Sharing the Vocabulary to Build a Community 128 6 A VISO-Based Abstract Visual Model – AVM 129 6.1 Graphical Notation Used in this Chapter 129 6.2 Elementary Graphic Objects and Graphic Attributes 131 6.3 N-Ary Relations 131 6.4 Binary Relations 131 6.5 Composition of Graphic Objects Using Roles 132 6.6 Composition of Graphic Relations Using Roles 132 6.7 Composition of Visual Mappings Using the AVM 135 6.8 Tracing 135 6.9 Is it Worth Having an Abstract Visual Model? 135 6.10 Discussion of Fresnel as a Related Language 137 6.11 Related Work 139 6.12 Limitations 139 6.13 Conclusions 140 7 A Language for RDFS/OWL Visualisation – RVL 141 7.1 Language Requirements 142 7.2 Main RVL Constructs 145 7.2.1 Mapping 145 7.2.2 Property Mapping 146 7.2.3 Identity Mapping 146 7.2.4 Value Mapping 147 7.2.5 Inheriting RVL Settings 147 7.2.6 Resource Mapping 148 7.2.7 Simplifications 149 7.3 Calculating Value Mappings 150 7.4 Defining Scale of Measurement 153 7.4.1 Determining the Scale of Measurement 154 7.5 Addressing Values in Value Mappings 156 7.5.1 Determining the Set of Addressed Source Values 156 7.5.2 Determining the Set of Addressed Target Values 157 7.6 Overlapping Value Mappings 158 7.7 Default Value Mapping 158 7.8 Default Labelling 159 7.9 Defining Interaction 159 7.10 Mapping Composition and Submappings 160 7.11 A Schema Language for RVL 160 7.11.1 Concrete Examples of the RVL Schema 163 7.12 Conclusions and Future Work 166 8 The OGVIC Approach 169 8.1 Ontology-Driven, Guided Editing of Visual Mappings 172 8.1.1 Classification of Constraints 172 8.1.2 Levels of Guidance 173 8.1.3 Implementing Constraint-Based Guidance 173 8.2 Support of Explicit and Composable Visual Mappings 177 8.2.1 Mapping Composition Cases 178 8.2.2 Selecting a Context 180 8.2.3 Using the Same Graphic Relation Multiple Times 181 8.3 Prototype P1 (TopBraid-Composer-based) 182 8.4 Prototype P2 (OntoWiki-based) 184 8.5 Prototype P3 (Java Implementation of RVL) 187 8.6 Lessons Learned from Prototypes & Future Work 190 8.6.1 Checking RVL Constraints and Visualisation Rules 190 8.6.2 A User Interface for Editing RVL Mappings 190 8.6.3 Graph Transformations with SPIN and SPARQL 1.1 Update 192 8.6.4 Selection and Filtering of Data 193 8.6.5 Interactivity and Incremental Processing 193 8.6.6 Rendering the Final Platform-Specific Code 196 9 Application 197 9.1 Coverage of Case Study Sketches and Necessary Features 198 9.2 Coverage of Visualisation Cases 201 9.3 Coverage of Requirements 205 9.4 Full Example 206 10 Conclusions 211 10.1 Contributions 211 10.2 Constructive Evaluation 212 10.3 Research Questions 213 10.4 Transfer to Other Models and Constraint Languages 213 10.5 Limitations 214 10.6 Future Work 214 Appendices 217 A Case Study Sketches 219 B VISO – Comparison of Visualisation Literature 229 C RVL 231 D RVL Example Mappings and Application 233 D.1 Listings of RVL Example Mappings as Required by Prototype P3 233 D.2 Features Required for Implementing all Sketches 235 D.3 JSON Format for Processing the AVM with D3 – Hierarchical Variant 238 Bibliography 238 List of Figures 251 List of Tables 254 List of Listings 257
Datenmassen im World Wide Web können kaum von Menschen oder Maschinen erfasst werden. Eine Option ist die formale Beschreibung und Verknüpfung von Datenquellen mit Semantic-Web- und Linked-Data-Technologien. Ontologien, in standardisierten Sprachen geschrieben, befördern das Teilen und Verknüpfen von Daten, da sie ein Mittel zur formalen Definition von Konzepten und Beziehungen zwischen diesen Konzepten darstellen. Eine zweite Option ist die Visualisierung. Die visuelle Repräsentation ermöglicht es dem Menschen, Informationen direkter wahrzunehmen, indem er seinen hochentwickelten Sehsinn verwendet. Relativ wenige Anstrengungen wurden unternommen, um beide Optionen zu kombinieren, obwohl die Formalität und die reichhaltige Semantik ontologische Daten zu einem idealen Kandidaten für die Visualisierung machen. Visualisierungsdesignsysteme unterstützen Nutzer bei der Visualisierung von tabellarischen, typischerweise statistischen Daten. Visualisierungen ontologischer Daten jedoch müssen noch manuell erstellt werden, da automatisierte Lösungen häufig auf generische Listendarstellungen oder Knoten-Kanten-Diagramme beschränkt sind. Auch die Semantik der ontologischen Daten wird nicht ausgenutzt, um Benutzer durch Visualisierungsaufgaben zu führen. Einmal erstellte Visualisierungseinstellungen können nicht einfach wiederverwendet und geteilt werden. Um diese Probleme zu lösen, mussten wir eine Antwort darauf finden, wie die Definition komponierbarer und wiederverwendbarer Abbildungen von ontologischen Daten auf visuelle Mittel geschehen könnte und wie Nutzer bei dieser Abbildung geführt werden könnten. Wir stellen einen Ansatz vor, der die geführte Visualisierung von ontologischen Daten, die Erstellung effektiver Grafiken und die Wiederverwendung von Visualisierungseinstellungen ermöglicht. Statt auf generische Grafiken zielt der Ansatz auf maßgeschneiderte Grafiken ab, die mit der gesamten Palette visueller Mittel in einem flexiblen Bottom-Up-Ansatz erstellt werden. Er erlaubt nicht nur die Visualisierung von Ontologien, sondern verwendet auch Ontologien, um Benutzer bei der Visualisierung von Daten zu führen und den Visualisierungsprozess an verschiedenen Stellen zu steuern: Erstens als eine reichhaltige Informationsquelle zu Datencharakteristiken, zweitens als Mittel zur formalen Beschreibung des Vokabulars für den Aufbau von abstrakten Grafiken und drittens als Wissensbasis von Visualisierungsfakten. Deshalb nennen wir unseren Ansatz ontologie-getrieben. Wir schlagen vor, ein Abstract Visual Model (AVM) zu generieren, um eine Grafik rollenbasiert zu synthetisieren, angelehnt an einen Ansatz der von J. v. Engelhardt verwendet wird, um Grafiken zu analysieren. Das AVM besteht aus grafischen Objekten und Relationen, die in der Visualisation Ontology (VISO) formalisiert sind. Ein Mapping-Modell, das auf der deklarativen RDFS/OWL Visualisation Language (RVL) basiert, bestimmt eine Menge von Transformationen von den Quelldaten zum AVM. RVL ermöglicht zusammensetzbare »Mappings«, visuelle Abbildungen, die über Plattformen hinweg geteilt und wiederverwendet werden können. Um den Benutzer zu führen, bewerten wir Mappings anhand eines in der Faktenbasis formalisierten Effektivitätsrankings und schlagen ggf. effektivere Mappings vor. Der Beratungsprozess ist flexibel, da er auf austauschbaren Regeln basiert. VISO, RVL und das AVM sind weitere Beiträge dieser Arbeit. Darüber hinaus analysieren wir zunächst den Stand der Technik in der Visualisierung und RDF-Präsentation, indem wir 10 Ansätze nach 29 Kriterien vergleichen. Unser Ansatz ist einzigartig, da er eine ontologie-getriebene Nutzerführung mit komponierbaren visuellen Mappings vereint. Schließlich vergleichen wir drei Prototypen, welche die wesentlichen Teile unseres Ansatzes umsetzen, um seine Machbarkeit zu zeigen. Wir zeigen, wie der Mapping-Prozess durch Tools unterstützt werden kann, die Warnmeldungen für nicht optimale visuelle Abbildungen anzeigen, z. B. durch Berücksichtigung von Charakteristiken der Relationen wie »Symmetrie«. In einer konstruktiven Evaluation fordern wir sowohl die RVL-Sprache als auch den neuesten Prototyp heraus, indem wir versuchen Skizzen von Grafiken umzusetzen, die wir während der Analyse manuell erstellt haben. Wir zeigen, wie Grafiken variiert werden können und komplexe Mappings aus einfachen zusammengesetzt werden können. Zwei Drittel der Skizzen können fast vollständig oder vollständig spezifiziert werden und die Hälfte kann fast vollständig oder vollständig umgesetzt werden.:Legend and Overview of Prefixes xiii 1 Introduction 1 2 Background 11 2.1 Visualisation 11 2.1.1 What is Visualisation? 11 2.1.2 What are the Benefits of Visualisation? 12 2.1.3 Visualisation Related Terms Used in this Thesis 12 2.1.4 Visualisation Models and Architectural Patterns 12 2.1.5 Visualisation Design Systems 14 2.1.6 What is the Difference between Visual Mapping and Styling? 14 2.1.7 Lessons Learned from Style Sheet Languages 15 2.2 Data 16 2.2.1 Data – Information – Knowledge 17 2.2.2 Structured Data 17 2.2.3 Ontologies in Computer Science 19 2.2.4 The Semantic Web and its Languages 19 2.2.5 Linked Data and Open Data 20 2.2.6 The Metamodelling Technological Space 21 2.2.7 SPIN 21 2.3 Guidance 22 2.3.1 Guidance in Visualisation 22 3 Problem Analysis 23 3.1 Problems of Ontology Visualisation Approaches 24 3.2 Research Questions 25 3.3 Set up of the Case Studies 25 3.3.1 Case Studies in the Life Sciences Domain 26 3.3.2 Case Studies in the Publishing Domain 26 3.3.3 Case Studies in the Software Technology Domain 27 3.4 Analysis of the Case Studies’ Ontologies 27 3.5 Manual Sketching of Graphics 29 3.6 Analysis of the Graphics for Typical Visualisation Cases 29 3.7 Requirements 33 3.7.1 Requirements for Visualisation and Interaction 34 3.7.2 Requirements for Data Awareness 34 3.7.3 Requirements for Reuse and Composition 34 3.7.4 Requirements for Variability 35 3.7.5 Requirements for Tooling Support and Guidance 35 3.7.6 Optional Features and Limitations 36 4 Analysis of the State of the Art 37 4.1 Related Visualisation Approaches 38 4.1.1 Short Overview of the Approaches 38 4.1.2 Detailed Comparison by Criteria 46 4.1.3 Conclusion – What Is Still Missing? 60 4.2 Visualisation Languages 62 4.2.1 Short Overview of the Compared Languages 62 4.2.2 Detailed Comparison by Language Criteria 66 4.2.3 Conclusion – What Is Still Missing? 71 4.3 RDF Presentation Languages 72 4.3.1 Short Overview of the Compared Languages 72 4.3.2 Detailed Comparison by Language Criteria 76 4.3.3 Additional Criteria for RDF Display Languages 87 4.3.4 Conclusion – What Is Still Missing? 89 4.4 Model-Driven Interfaces 90 4.4.1 Metamodel-Driven Interfaces 90 4.4.2 Ontology-Driven Interfaces 92 4.4.3 Combined Usage of the Metamodelling and Ontology Technological Space 94 5 A Visualisation Ontology – VISO 97 5.1 Methodology Used for Ontology Creation 100 5.2 Requirements for a Visualisation Ontology 100 5.3 Existing Approaches to Modelling in the Field of Visualisation 101 5.3.1 Terminologies and Taxonomies 101 5.3.2 Existing Visualisation Ontologies 102 5.3.3 Other Visualisation Models and Approaches to Formalisation 103 5.3.4 Summary 103 5.4 Technical Aspects of VISO 103 5.5 VISO/graphic Module – Graphic Vocabulary 104 5.5.1 Graphic Representations and Graphic Objects 105 5.5.2 Graphic Relations and Syntactic Structures 107 5.6 VISO/data Module – Characterising Data 110 5.6.1 Data Structure and Characteristics of Relations 110 5.6.2 The Scale of Measurement and Units 112 5.6.3 Properties for Characterising Data Variables in Statistical Data 113 5.7 VISO/facts Module – Facts for Vis. Constraints and Rules 115 5.7.1 Expressiveness of Graphic Relations 116 5.7.2 Effectiveness Ranking of Graphic Relations 118 5.7.3 Rules for Composing Graphics 119 5.7.4 Other Rules to Consider for Visual Mapping 124 5.7.5 Providing Named Value Collections 124 5.7.6 Existing Approaches to the Formalisation of Visualisation Knowledge . . 126 5.7.7 The VISO/facts/empiric Example Knowledge Base 126 5.8 Other VISO Modules 126 5.9 Conclusions and Future Work 127 5.10 Further Use Cases for VISO 127 5.11 VISO on the Web – Sharing the Vocabulary to Build a Community 128 6 A VISO-Based Abstract Visual Model – AVM 129 6.1 Graphical Notation Used in this Chapter 129 6.2 Elementary Graphic Objects and Graphic Attributes 131 6.3 N-Ary Relations 131 6.4 Binary Relations 131 6.5 Composition of Graphic Objects Using Roles 132 6.6 Composition of Graphic Relations Using Roles 132 6.7 Composition of Visual Mappings Using the AVM 135 6.8 Tracing 135 6.9 Is it Worth Having an Abstract Visual Model? 135 6.10 Discussion of Fresnel as a Related Language 137 6.11 Related Work 139 6.12 Limitations 139 6.13 Conclusions 140 7 A Language for RDFS/OWL Visualisation – RVL 141 7.1 Language Requirements 142 7.2 Main RVL Constructs 145 7.2.1 Mapping 145 7.2.2 Property Mapping 146 7.2.3 Identity Mapping 146 7.2.4 Value Mapping 147 7.2.5 Inheriting RVL Settings 147 7.2.6 Resource Mapping 148 7.2.7 Simplifications 149 7.3 Calculating Value Mappings 150 7.4 Defining Scale of Measurement 153 7.4.1 Determining the Scale of Measurement 154 7.5 Addressing Values in Value Mappings 156 7.5.1 Determining the Set of Addressed Source Values 156 7.5.2 Determining the Set of Addressed Target Values 157 7.6 Overlapping Value Mappings 158 7.7 Default Value Mapping 158 7.8 Default Labelling 159 7.9 Defining Interaction 159 7.10 Mapping Composition and Submappings 160 7.11 A Schema Language for RVL 160 7.11.1 Concrete Examples of the RVL Schema 163 7.12 Conclusions and Future Work 166 8 The OGVIC Approach 169 8.1 Ontology-Driven, Guided Editing of Visual Mappings 172 8.1.1 Classification of Constraints 172 8.1.2 Levels of Guidance 173 8.1.3 Implementing Constraint-Based Guidance 173 8.2 Support of Explicit and Composable Visual Mappings 177 8.2.1 Mapping Composition Cases 178 8.2.2 Selecting a Context 180 8.2.3 Using the Same Graphic Relation Multiple Times 181 8.3 Prototype P1 (TopBraid-Composer-based) 182 8.4 Prototype P2 (OntoWiki-based) 184 8.5 Prototype P3 (Java Implementation of RVL) 187 8.6 Lessons Learned from Prototypes & Future Work 190 8.6.1 Checking RVL Constraints and Visualisation Rules 190 8.6.2 A User Interface for Editing RVL Mappings 190 8.6.3 Graph Transformations with SPIN and SPARQL 1.1 Update 192 8.6.4 Selection and Filtering of Data 193 8.6.5 Interactivity and Incremental Processing 193 8.6.6 Rendering the Final Platform-Specific Code 196 9 Application 197 9.1 Coverage of Case Study Sketches and Necessary Features 198 9.2 Coverage of Visualisation Cases 201 9.3 Coverage of Requirements 205 9.4 Full Example 206 10 Conclusions 211 10.1 Contributions 211 10.2 Constructive Evaluation 212 10.3 Research Questions 213 10.4 Transfer to Other Models and Constraint Languages 213 10.5 Limitations 214 10.6 Future Work 214 Appendices 217 A Case Study Sketches 219 B VISO – Comparison of Visualisation Literature 229 C RVL 231 D RVL Example Mappings and Application 233 D.1 Listings of RVL Example Mappings as Required by Prototype P3 233 D.2 Features Required for Implementing all Sketches 235 D.3 JSON Format for Processing the AVM with D3 – Hierarchical Variant 238 Bibliography 238 List of Figures 251 List of Tables 254 List of Listings 257