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Dissertations / Theses on the topic 'Ontology based information retrieval'
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1
Addy, Nicholas G. "Ontology driven geographic information retrieval." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/2526.
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The theory of modern information retrieval processes must be improved to meet parallel growth and efficiency in its dependent hardware architectures. The growth in data sources facilitated by hardware improvements must be conversant with parallel growth at the user end of the information retrieval paradigm, encompassing both an increasing demand for data services and a widening user base. Contemporary sources refer to such growth as three dimensional, in reference to the expected and parallel growth in the key areas of hardware processing power, demand from current users of information services and an increase in demand via an extended user base consisting of institutions and organizations who are not characteristically defined by their use of geographic information. This extended user base is expected to grow due to the demand to utilise and incorporate geographic information as part of competitive business processes, to fill the need for advertising and spatial marketing demographics. The vision of the semantic web as such is the challenge of managing integration between diverse and increasing data sources and diverse and increasing end users of information. Whilst data standardisation is one means of achieving this vision at the source end of the information flow, it is not a solution in a free market of ideas. Information in its elemental form should be accessible regardless of the domain of its creation.In an environment where the users and sources are continually growing in scope and depth, the management of data via precise and relevant information retrieval requires techniques which can integrate information seamlessly between machines and users regardless of the domain of application or data storage methods. This research is the study of a theory of geographic information structure which can be applied to all aspects of information systems development, governing at a conceptual level the representation of information to meet the requirements of inter machine operability as well as inter user operability. This research entails a thorough study of the use of ontology from theoretical definition to modern use in information systems development and retrieval, in the geographic domain. This is a study examining how the use of words to describe geographic features are elements which can form a geographic ontology and evaluates WordNet, an English language ontology in the form of a lexical database as a structure for improving geographic information recall on Gazetteers. The results of this research conclude that WordNet can be utilised to as a methodology for improving search results in geographic information retrieval processes as a source for additional query terms, but only on a narrow user domain.
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Mehalingam, Senthilkumar. "Ontology based code generation for datalogger." Diss., Online access via UMI:, 2006.
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3
Fischer, Wolf [Verfasser], and Bernhard [Akademischer Betreuer] Bauer. "Linguistically Motivated Ontology-Based Information Retrieval / Wolf Fischer. Betreuer: Bernhard Bauer." Augsburg : Universität Augsburg, 2013. http://d-nb.info/1077702795/34.
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Bhogal, Jagdev. "Investigating ontology based query expansion using a probabilistic retrieval model." Thesis, City University London, 2011. http://openaccess.city.ac.uk/2946/.
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This research briefly outlines the problems of traditional information retrieval systems and discusses the different approaches to inferring context in document retrieval. By context we mean word disambiguation which is achieved by exploring the generalisation-specialisation hierarchies within a given ontology. Specifically, we examine the use of ontology based query expansion for defining query context. Query expansion can be done in many ways and in this work we consider the use of relevance feedback and pseudo-relevance feedback for query expansion. We examine relevance feedback and pseudo-relevance to ascertain the existence of performance differences between relevance feedback and pseudo-relevance feedback. The information retrieval system used is based on the probabilistic retrieval model and the query expansion method is extended using information from a news domain ontology. The aim of this project is to assess the impact of the use of the ontology on the query expansion results. Our results show that ontology based query expansion has resulted in a higher number of relevant documents being retrieved compared to the standard relevance feedback process. Overall, ontology based query expansion improves recall but does not produce any significant improvements for the precision results. Pseudo-relevance feedback has achieved better results than relevance feedback. We also found that reducing or increasing the relevance feedback parameters (number of terms or number of documents) does not correlate with the results. When comparing the effect of varying the number of terms parameter with the number of documents parameter, the former benefits the pseudo-relevance feedback results but the latter has an additional effect on the relevance feedback results. There are many factors which influence the success of ontology based query expansion. The thesis discusses these factors and gives some guidelines on using ontologies for the purpose of query expansion.
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Chartrand, Tim. "Ontology-based extraction of RDF data from the World Wide Web /." Diss., CLICK HERE for online access, 2003. http://contentdm.lib.byu.edu/ETD/image/etd168.pdf.
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Wang, Xinkai. "Chinese-English cross-lingual information retrieval in biomedicine using ontology-based query expansion." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/chineseenglish-crosslingual-information-retrieval-in-biomedicine-using-ontologybased-query-expansion(1b7443d3-3baf-402b-83bb-f45e78876404).html.
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In this thesis, we propose a new approach to Chinese-English Biomedical cross-lingual information retrieval (CLIR) using query expansion based on the eCMeSH Tree, a Chinese-English ontology extended from the Chinese Medical Subject Headings (CMeSH) Tree. The CMeSH Tree is not designed for information retrieval (IR), since it only includes heading terms and has no term weighting scheme for these terms. Therefore, we design an algorithm, which employs a rule-based parsing technique combined with the C-value term extraction algorithm and a filtering technique based on mutual information, to extract Chinese synonyms for the corresponding heading terms. We also develop a term-weighting mechanism. Following the hierarchical structure of CMeSH, we extend the CMeSH Tree to the eCMeSH Tree with synonymous terms and their weights. We propose an algorithm to implement CLIR using the eCMeSH Tree terms to expand queries. In order to evaluate the retrieval improvements obtained from our approach, the results of the query expansion based on the eCMeSH Tree are individually compared with the results of the experiments of query expansion using the CMeSH Tree terms, query expansion using pseudo-relevance feedback, and document translation. We also evaluate the combinations of these three approaches. This study also investigates the factors which affect the CLIR performance, including a stemming algorithm, retrieval models, and word segmentation.
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Deniz, Onur. "Ontology Based Text Mining In Turkish Radiology Reports." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614145/index.pdf.
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Vast amount of radiology reports are produced in hospitals. Being in free text format and having errors due to rapid production, it continuously gets more complicated for radiologists and physicians to reach meaningful information. Though application of ontologies into bio-medical text mining has gained increasing interest in recent years, less work has been offered for ontology based retrieval tasks in Turkish language. In this work, an information extraction and retrieval system based on SNOMED-CT ontology has been proposed for Turkish radiology reports. Main purpose of this work is to utilize semantic relations in ontology to improve precision and recall rates of search results in domain. Practical problems encountered such as spelling errors, segmentation and tokenization of unstructured medical reports has also been addressed during the work.
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Skovronski, John. "An ontology-based publish-subscribe framework." Diss., Online access via UMI:, 2006.
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9
Ismail, Muhammad, and Attuallah Jan. "Context-based supply of documents in a healthcare process." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-18513.
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The more enhanced and reliable healthcare facilities, depend partly on accumulated organizational knowledge. Ontology and semantic web are the key factors in long-term sustainability towards the improvement of patient treatment process. Generally, researchers have the common consensus that knowledge is hard to capture due to its implicit nature, making it hard to manage. Medical professionals spend more time on getting the right information at the right moment, which is already available on intranet/internet. Evaluating the literature is controversial but interesting debates on ontology and semantic web encouraged us to propose a method and 4-Tier Architecture for retrieving context-based document according to user’s information in healthcare organization. Medical professionals are facing problems to access relevant information and documents for performing different tasks in the patient-treatment process. We have focused to provide context-based retrieval of documents for medical professionals by developing a semantic web solution. We also developed different OWL ontology models, which are mainly used for semantic tagging in web pages and generating context to retrieve the relevant web page documents. In addition, we developed a prototype to testify our findings in health care sector with the goal of retrieving relevant documents in a practical manner.E-Health
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Kubilay, Mustafa. "Special Index And Retrieval Mechanism For Ontology Based Medical Domain Search Engines." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606470/index.pdf.
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This thesis focuses on index and retrieval mechanism of an ontology based medical domain search engine. First, indexing techniques and retrieval methods are reviewed. Then, a special indexing and retrieval mechanism are introduced. This thesis also specifies the functional requirements of these mechanisms. Finally, an evaluation is given by indicating the positive and negative aspects of mechanisms.
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Vickers, Mark S. "Ontology-Based Free-Form Query Processing for the Semantic Web." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1353.pdf.
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Hamilton, John, Ronald Fernandes, Timothy Darr, Michael Graul, Charles Jones, and Annette Weisenseel. "A Model-Based Methodology for Managing T&E Metadata." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606019.
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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, NevadaIn this paper, we present a methodology for managing diverse sources of T&E metadata. Central to this methodology is the development of a T&E Metadata Reference Model, which serves as the standard model for T&E metadata types, their proper names, and their relationships to each other. We describe how this reference model can be mapped to a range's own T&E data and process models to provide a standardized view into each organization's custom metadata sources and procedures. Finally, we present an architecture that uses these models and mappings to support cross-system metadata management tasks and makes these capabilities accessible across the network through a single portal interface.
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Zhan, Pei. "An ontology-based approach for semantic level information exchange and integration in applications for product lifecycle management." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Dissertations/Summer2007/P_Zhan_080607.pdf.
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Figueiras, Paulo Alves. "A framework for supporting knowledge representation – an ontological based approach." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/7576.
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Dissertação para obtenção do Grau de Mestre em
Engenharia Electrotécnica e de ComputadoresThe World Wide Web has had a tremendous impact on society and business in just a few years by making information instantly available. During this transition from physical to electronic means for information transport, the content and encoding of information has remained natural language and is only identified by its URL. Today, this is perhaps the most significant obstacle to streamlining business processes via the web. In order that processes may execute without human intervention, knowledge sources, such as documents, must become more machine understandable and must contain other information besides their main contents and URLs. The Semantic Web is a vision of a future web of machine-understandable data. On a machine understandable web, it will be possible for programs to easily determine what knowledge sources are about. This work introduces a conceptual framework and its implementation to support the classification and discovery of knowledge sources, supported by the above vision, where such sources’ information is structured and represented through a mathematical vector that semantically pinpoints the relevance of those knowledge sources within the domain of interest of each user. The presented work also addresses the enrichment of such knowledge representations, using the statistical relevance of keywords based on the classical vector space model concept, and extending it with ontological support, by using concepts and semantic relations, contained in a domain-specific ontology, to enrich knowledge sources’ semantic vectors. Semantic vectors are compared against each other, in order to obtain the similarity between them, and better support end users with knowledge source retrieval capabilities.
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Repchevskiy, Dmitry. "Ontology based data integration in life sciences." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/386411.
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The aim of this thesis is to develop standard and practical approaches for the semantic integration of biological data and services. The thesis considers various scenarios where ontologies may benefit bioinformatics web services development, integration and provenance. In spite of the broad use of ontologies in biology, their usage is usually limited to a definition of taxonomic hierarchies. This thesis examines the utility of ontologies for data integration in context of semantic web services development. The biological datatypes ontologies are very valuable for the data integration, especially in a context of continuous standards changes. The thesis evaluates the outdated BioMoby ontology for the generation of modern WS-I and RESTful web services. Another important aspect is the use of ontologies for the web services description. The thesis evaluates the W3C standard WSDL ontology for bioinformatics web services description and provenance. Finally, the integration with modern workflow execution platforms such as Taverna and Galaxy is also considered.
Despite the growing popularity of JSON format, web services vastly depend on XML type system. The OWL2XS tool facilitates semantic web services development providing the automatic XML Schema generation from an appropriate OWL 2 datatype ontology. Web services integration is hardly achievable without a broad standard adoption. The BioNemus application automatically generates standard-based web services from BioMoby ontologies. Semantic representation of web services description simplifies web services search and annotation. Semantic Web Services Registry (BioSWR) is based on W3C WSDL ontology and provides a multifaceted web services view in different formats: OWL 2, WSDL 1.1, WSDL 2.0 and WADL. To demonstrate benefits of ontology-based web services descriptions, BioSWR Taverna OSGI plug-in has been developed. The new, experimental, Taverna WSDL generic library has been used in Galaxy Gears tool which allows integrating web services into the Galaxy workflows.
The thesis explores the scopes of ontologies application for the biological data and services integration, providing a broad set of original tools.El objetivo de la tesis es el desarrollo de una solución práctica y estándar para la integración semántica de los datos y servicios biológicos. La tesis estudia escenarios diferentes en los cuales las ontologías pueden beneficiar el desarrollo de los servicios web, su búsqueda y su visibilidad. A pesar de que las ontologías son ampliamente utilizadas en la biología, su uso habitualmente se limita a la definición de las jerarquías taxonómicas. La tesis examina la utilidad de las ontologías para la integración de los datos en el desarrollo de los servicios web semánticos. Las ontologías que definen los tipos de datos biológicos tienen un gran valor para la integración de los datos, especialmente ante un cambio continuo de los estándares. La tesis evalúa la ontología BioMoby para la generación de los servicios web conforme con las especificaciones WS-I y los servicios REST. Otro aspecto muy importante de la tesis es el uso de las ontologías para la descripción de los servicios web. La tesis evalúa la ontología WSDL promovida por el consorcio W3C para la descripción de los servicios y su búsqueda. Finalmente, se considera la integración con las plataformas modernas de la ejecución de los flujos de trabajo como Taverna y Galaxy. A pesar de la creciente popularidad del formato JSON, los servicios web dependen mucho del XML. La herramienta OWL2XS facilita el desarrollo de los servicios web semánticos generando un esquema XML a partir de una ontología OWL 2. La integración de los servicios web es difícil de conseguir sin una adaptación de los estándares. La aplicación BioNemus genera de manera automática servicios web estándar a partir de las ontologías BioMoby. La representación semántica de los servicios web simplifica su búsqueda y anotación. El Registro Semántico de Servicios Web (BioSWR) está basado en la ontología WSDL del W3C y proporciona una representación en distintos formatos: OWL 2, WSDL 1.1, WSDL 2.0 y WADL. Para demostrar los beneficios de la descripción semántica de los servicios web se ha desarrollado un plugin para Taverna. También se ha implementado una nueva librería experimental que ha sido usada en la aplicación Galaxy Gears, la cual permite la integración de los servicios web en Galaxy. La tesis explora el alcance de la aplicación de las ontologías para la integración de los datos y los servicios biológicos, proporcionando un amplio conjunto de nuevas aplicaciones.
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Wessman, Alan E. "A Framework for Extraction Plans and Heuristics in an Ontology-Based Data-Extraction System." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd684.pdf.
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Gängler, Thomas. "Semantic Federation of Musical and Music-Related Information for Establishing a Personal Music Knowledge Base." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-72434.
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Music is perceived and described very subjectively by every individual. Nowadays, people often get lost in their steadily growing, multi-placed, digital music collection. Existing music player and management applications get in trouble when dealing with poor metadata that is predominant in personal music collections. There are several music information services available that assist users by providing tools for precisely organising their music collection, or for presenting them new insights into their own music library and listening habits. However, it is still not the case that music consumers can seamlessly interact with all these auxiliary services directly from the place where they access their music individually. To profit from the manifold music and music-related knowledge that is or can be available via various information services, this information has to be gathered up, semantically federated, and integrated into a uniform knowledge base that can personalised represent this data in an appropriate visualisation to the users. This personalised semantic aggregation of music metadata from several sources is the gist of this thesis. The outlined solution particularly concentrates on users’ needs regarding music collection management which can strongly alternate between single human beings. The author’s proposal, the personal music knowledge base (PMKB), consists of a client-server architecture with uniform communication endpoints and an ontological knowledge representation model format that is able to represent the versatile information of its use cases. The PMKB concept is appropriate to cover the complete information flow life cycle, including the processes of user account initialisation, information service choice, individual information extraction, and proactive update notification. The PMKB implementation makes use of SemanticWeb technologies. Particularly the knowledge representation part of the PMKB vision is explained in this work. Several new Semantic Web ontologies are defined or existing ones are massively modified to meet the requirements of a personalised semantic federation of music and music-related data for managing personal music collections. The outcome is, amongst others, • a new vocabulary for describing the play back domain, • another one for representing information service categorisations and quality ratings, and • one that unites the beneficial parts of the existing advanced user modelling ontologies. The introduced vocabularies can be perfectly utilised in conjunction with the existing Music Ontology framework. Some RDFizers that also make use of the outlined ontologies in their mapping definitions, illustrate the fitness in practise of these specifications. A social evaluation method is applied to carry out an examination dealing with the reutilisation, application and feedback of the vocabularies that are explained in this work. This analysis shows that it is a good practise to properly publish Semantic Web ontologies with the help of some Linked Data principles and further basic SEO techniques to easily reach the searching audience, to avoid duplicates of such KR specifications, and, last but not least, to directly establish a \"shared understanding\". Due to their project-independence, the proposed vocabularies can be deployed in every knowledge representation model that needs their knowledge representation capacities. This thesis added its value to make the vision of a personal music knowledge base come true.
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Kamongi, Patrick. "Ontology Based Security Threat Assessment and Mitigation for Cloud Systems." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404576/.
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A malicious actor often relies on security vulnerabilities of IT systems to launch a cyber attack. Most cloud services are supported by an orchestration of large and complex systems which are prone to vulnerabilities, making threat assessment very challenging. In this research, I developed formal and practical ontology-based techniques that enable automated evaluation of a cloud system's security threats. I use an architecture for threat assessment of cloud systems that leverages a dynamically generated ontology knowledge base. I created an ontology model and represented the components of a cloud system. These ontologies are designed for a set of domains that covers some cloud's aspects and information technology products' cyber threat data. The inputs to our architecture are the configurations of cloud assets and components specification (which encompass the desired assessment procedures) and the outputs are actionable threat assessment results. The focus of this work is on ways of enumerating, assessing, and mitigating emerging cyber security threats. A research toolkit system has been developed to evaluate our architecture. We expect our techniques to be leveraged by any cloud provider or consumer in closing the gap of identifying and remediating known or impending security threats facing their cloud's assets.
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Carstens, Carola [Verfasser], and Christa [Akademischer Betreuer] Womser-Hacker. "Ontology Based Query Expansion - Retrieval Support for the Domain of Educational Research / Carola Carstens. Betreuer: Christa Womser-Hacker." Hildesheim : Universitätsbibliothek Hildesheim, 2012. http://d-nb.info/1023809400/34.
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Zhu, Dengya. "Improving the relevance of search results via search-term disambiguation and ontological filtering." Thesis, Curtin University, 2007. http://hdl.handle.net/20.500.11937/2486.
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With the exponential growth of the Web and the inherent polysemy and synonymy problems of the natural languages, search engines are facing many challenges such as information overload, mismatch of search results, missing relevant documents, poorly organized search results, and mismatch of human mental model of clustering engines. To address these issues, much effort including employing different information retrieval (IR) models, information categorization/clustering, personalization, semantic Web, ontology-based IR, and so on, has been devoted to improve the relevance of search results. The major focus of this study is to dynamically re-organize Web search results under a socially constructed hierarchical knowledge structure, to facilitate information seekers to access and manipulate the retrieved search results, and consequently to improve the relevance of search results.To achieve the above research goal, a special search-browser is developed, and its retrieval effectiveness is evaluated. The hierarchical structure of the Open Directory Project (ODP) is employed as the socially constructed knowledge structure which is represented by the Tree component of Java. Yahoo! Search Web Services API is utilized to obtain search results directly from Yahoo! search engine databases. The Lucene text search engine calculates similarities between each returned search result and the semantic characteristics of each category in the ODP; and thus to assign the search results to the corresponding ODP categories by Majority Voting algorithm. When an interesting category is selected by a user, only search results categorized under the category are presented to the user, and the quality of the search results is consequently improved.Experiments demonstrate that the proposed approach of this research can improve the precision of Yahoo! search results at the 11 standard recall levels from an average 41.7 per cent to 65.2 per cent; the improvement is as high as 23.5 per cent. This conclusion is verified by comparing the improvements of the P@5 and P@10 of Yahoo! search results and the categorized search results of the special search-browser. The improvement of P@5 and P@10 are 38.3 per cent (85 per cent - 46.7 per cent) and 28 per cent (70 per cent - 42 per cent) respectively. The experiment of this research is well designed and controlled. To minimize the subjectiveness of relevance judgments, in this research five judges (experts) are asked to make their relevance judgments independently, and the final relevance judgment is a combination of the five judges’ judgments. The judges are presented with only search-terms, information needs, and the 50 search results of Yahoo! Search Web Service API. They are asked to make relevance judgments based on the information provided above, there is no categorization information provided.The first contribution of this research is to use an extracted category-document to represent the semantic characteristics of each of the ODP categories. A category-document is composed of the topic of the category, description of the category, the titles and the brief descriptions of the submitted Web pages under this category. Experimental results demonstrate the category-documents of the ODP can represent the semantic characteristics of the ODP in most cases. Furthermore, for machine learning algorithms, the extracted category-documents can be utilized as training data which otherwise demand much human labor to create to ensure the learning algorithm to be properly trained. The second contribution of this research is the suggestion of the new concepts of relevance judgment convergent degree and relevance judgment divergent degree that are used to measure how well different judges agree with each other when they are asked to judge the relevance of a list of search results. When the relevance judgment convergent degree of a search-term is high, an IR algorithm should obtain a higher precision as well. On the other hand, if the relevance judgment convergent degree is low, or the relevance judgment divergent degree is high, it is arguable to use the data to evaluate the IR algorithm. This intuition is manifested by the experiment of this research. The last contribution of this research is that the developed search-browser is the first IR system (IRS) to utilize the ODP hierarchical structure to categorize and filter search results, to the best of my knowledge.
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Zhu, Dengya. "Improving the relevance of search results via search-term disambiguation and ontological filtering." Curtin University of Technology, School of Information Systems, 2007. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=9348.
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With the exponential growth of the Web and the inherent polysemy and synonymy problems of the natural languages, search engines are facing many challenges such as information overload, mismatch of search results, missing relevant documents, poorly organized search results, and mismatch of human mental model of clustering engines. To address these issues, much effort including employing different information retrieval (IR) models, information categorization/clustering, personalization, semantic Web, ontology-based IR, and so on, has been devoted to improve the relevance of search results. The major focus of this study is to dynamically re-organize Web search results under a socially constructed hierarchical knowledge structure, to facilitate information seekers to access and manipulate the retrieved search results, and consequently to improve the relevance of search results.To achieve the above research goal, a special search-browser is developed, and its retrieval effectiveness is evaluated. The hierarchical structure of the Open Directory Project (ODP) is employed as the socially constructed knowledge structure which is represented by the Tree component of Java. Yahoo! Search Web Services API is utilized to obtain search results directly from Yahoo! search engine databases. The Lucene text search engine calculates similarities between each returned search result and the semantic characteristics of each category in the ODP; and thus to assign the search results to the corresponding ODP categories by Majority Voting algorithm. When an interesting category is selected by a user, only search results categorized under the category are presented to the user, and the quality of the search results is consequently improved.
Experiments demonstrate that the proposed approach of this research can improve the precision of Yahoo! search results at the 11 standard recall levels from an average 41.7 per cent to 65.2 per cent; the improvement is as high as 23.5 per cent. This conclusion is verified by comparing the improvements of the P@5 and P@10 of Yahoo! search results and the categorized search results of the special search-browser. The improvement of P@5 and P@10 are 38.3 per cent (85 per cent - 46.7 per cent) and 28 per cent (70 per cent - 42 per cent) respectively. The experiment of this research is well designed and controlled. To minimize the subjectiveness of relevance judgments, in this research five judges (experts) are asked to make their relevance judgments independently, and the final relevance judgment is a combination of the five judges’ judgments. The judges are presented with only search-terms, information needs, and the 50 search results of Yahoo! Search Web Service API. They are asked to make relevance judgments based on the information provided above, there is no categorization information provided.
The first contribution of this research is to use an extracted category-document to represent the semantic characteristics of each of the ODP categories. A category-document is composed of the topic of the category, description of the category, the titles and the brief descriptions of the submitted Web pages under this category. Experimental results demonstrate the category-documents of the ODP can represent the semantic characteristics of the ODP in most cases. Furthermore, for machine learning algorithms, the extracted category-documents can be utilized as training data which otherwise demand much human labor to create to ensure the learning algorithm to be properly trained. The second contribution of this research is the suggestion of the new concepts of relevance judgment convergent degree and relevance judgment divergent degree that are used to measure how well different judges agree with each other when they are asked to judge the relevance of a list of search results. When the relevance judgment convergent degree of a search-term is high, an IR algorithm should obtain a higher precision as well. On the other hand, if the relevance judgment convergent degree is low, or the relevance judgment divergent degree is high, it is arguable to use the data to evaluate the IR algorithm. This intuition is manifested by the experiment of this research. The last contribution of this research is that the developed search-browser is the first IR system (IRS) to utilize the ODP hierarchical structure to categorize and filter search results, to the best of my knowledge.
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Tomassen, Stein L. "Conceptual Ontology Enrichment for Web Information Retrieval." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14270.
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Searching for information on the Web can be frustrating. One of the reasons is the ambiguity of words. The work presented in this thesis concentrates on how the effectiveness of standard information retrieval systems can be enhanced with semantic technologies like ontologies. Ontologies are knowledge models that can represent knowledge of any universe of discourse by describing how concepts of a domain are related. Creating and maintaining ontologies can be tedious and costly. However, we focus on reusing ontologies, rather than engineering, and on their applicability to improve the retrieval effectiveness of existing search systems. The aim of this work is to find an effective approach for applying ontologies to existing search systems. The basic idea is that these ontologies can be used to tackle the problem of ambiguous words and hence improve the retrieval effectiveness. Our approach to semantic search builds on feature vectors (FV). The basic idea is to connect the (standardised) domain terminology encoded in an ontology to the actual terminology used in a text corpus. Therefore, we propose to associate every ontology entity (classes and individuals are called entities in this work) with a FV that is tailored to the actual terminology used in a text corpus like the Web. These FVs are created off-line and later used on-line to filter (i.e. to disambiguate search) and re-rank the search results from an underlying search system. This pragmatic approach is applicable to existing search systems since it only depends on extending the query and presentation components, in other words there is no need to alter either the indexing or the ranking components of the existing systems. A set of experiments have been carried out and the results report on improvement by more than 10%. Furthermore, we have shown that the approach is neither dependent on highly specific queries nor on a collection comprised only of relevant documents. In addition, we have shown that the FVs are relatively persistent, i.e. little maintenance of the FVs is required. In this work, we focus on the creation and evaluation of these feature vectors. As a result, a part of the contribution of this work is a framework for the construction of FVs. Furthermore, we have proposed a set of metrics to measure the quality of the created FVs. We have also provided a set of guidelines for optimal construction of feature vectors for different categories of ontologies.
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Kara, Soner. "An Ontology-based Retrieval System Using Semantic Indexing." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612110/index.pdf.
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In this thesis, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using
domain-specific information extraction, inference and rules. Scalability is achieved by adapting a semantic indexing approach. The system is implemented using the state-of-the-art technologies in SemanticWeb and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inference. Finally, we show how we use semantic indexing to solve simple structural ambiguities.
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Syed, Abdullah Engku. "Automated mood boards : ontology-based semantic image retrieval." Thesis, Cardiff University, 2012. http://orca.cf.ac.uk/43542/.
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The main goal of this research is to support concept designers’ search for inspirational and meaningful images in developing mood boards. Finding the right images has become a well-known challenge as the amount of images stored and shared on the Internet and elsewhere keeps increasing steadily and rapidly. The development of image retrieval technologies, which collect, store and pre-process image information to return relevant images instantly in response to users’ needs, have achieved great progress in the last decade. However, the keyword-based content description and query processing techniques for Image Retrieval (IR) currently used have their limitations. Most of these techniques are adapted from the Information Retrieval research, and therefore provide limited capabilities to grasp and exploit conceptualisations due to their inability to handle ambiguity, synonymy, and semantic constraints. Conceptual search (i.e. searching by meaning rather than literal strings) aims to solve the limitations of the keyword-based models. Starting from this point, this thesis investigates the existing IR models, which are oriented to the exploitation of domain knowledge in support of semantic search capabilities, with a focus on the use of lexical ontologies to improve the semantic perspective. It introduces a technique for extracting semantic DNA (SDNA) from textual image annotations and constructing semantic image signatures. The semantic signatures are called semantic chromosomes; they contain semantic information related to the images. Central to the method of constructing semantic signatures is the concept disambiguation technique developed, which identifies the most relevant SDNA by measuring the semantic importance of each word/phrase in the image annotation. In addition, a conceptual model of an ontology-based system for generating visual mood boards is proposed. The proposed model, which is adapted from the Vector Space Model, exploits the use of semantic chromosomes in semantic indexing and assessing the semantic similarity of images within a collection.
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Arapakis, Ioannis. "Affect-based information retrieval." Thesis, University of Glasgow, 2010. http://theses.gla.ac.uk/1867/.
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One of the main challenges Information Retrieval (IR) systems face nowadays originates from the semantic gap problem: the semantic difference between a user’s query representation and the internal representation of an information item in a collection. The gap is further widened when the user is driven by an ill-defined information need, often the result of an anomaly in his/her current state of knowledge. The formulated search queries, which are submitted to the retrieval systems to locate relevant items, produce poor results that do not address the users’ information needs. To deal with information need uncertainty IR systems have employed in the past a range of feedback techniques, which vary from explicit to implicit. The first category of feedback techniques necessitates the communication of explicit relevance judgments, in return for better query reformulations and recommendations of relevant results. However, the latter happens at the expense of users’ cognitive resources and, furthermore, introduces an additional layer of complexity to the search process. On the other hand, implicit feedback techniques make inferences on what is relevant based on observations of user search behaviour. By doing so, they disengage users from the cognitive burden of document rating and relevance assessments. However, both categories of RF techniques determine topical relevance with respect to the cognitive and situational levels of interaction, failing to acknowledge the importance of emotions in cognition and decision making. In this thesis I investigate the role of emotions in the information seeking process and develop affective feedback techniques for interactive IR. This novel feedback framework aims to aid the search process and facilitate a more natural and meaningful interaction. I develop affective models that determine topical relevance based on information gathered from various sensory channels, and enhance their performance using personalisation techniques. Furthermore, I present an operational video retrieval system that employs affective feedback to enrich user profiles and offers meaningful recommendations of unseen videos. The use of affective feedback as a surrogate for the information need is formalised as the Affective Model of Browsing. This is a cognitive model that motivates the use of evidence extracted from the psycho-somatic mobilisation that occurs during cognitive appraisal. Finally, I address some of the ethical and privacy issues that arise from the social-emotional interaction between users and computer systems. This study involves questionnaire data gathered over three user studies, from 74 participants of different educational background, ethnicity and search experience. The results show that affective feedback is a promising area of research and it can improve many aspects of the information seeking process, such as indexing, ranking and recommendation. Eventually, it may be that relevance inferences obtained from affective models will provide a more robust and personalised form of feedback, which will allow us to deal more effectively with issues such as the semantic gap.
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Jimeno, Yepes Antonio José. "Ontology refinement for improved information retrieval in the biomedical domain." Doctoral thesis, Universitat Jaume I, 2009. http://hdl.handle.net/10803/384552.
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Este trabajo de tesis doctoral se centra en el uso de ontologías de dominio y su refinamiento enfocado a la recuperación de la información. El dominio seleccionado ha sido el de la Biomedicina, que dispone de una extensa colección de resúmenes en la base de datos Medline y recursos que facilitan la creación de ontologías muy extensas, tales como MeSH o UMLS.
En este trabajo se ha desarrollado también un modelo de formulación de consulta que permite relacionar un modelo de documento con una ontología dentro de los modelos de lenguaje. Además hemos desarrollado un algoritmo que permite mejorar la ontología para la tarea de recuperación de la información a partir de recursos no estructurados.
Los resultados muestran que el refinamiento de las ontologías aplicado a la recuperación de la información mejora el rendimiento, identificando automáticamente información no presente en la ontología. Además hemos comprobado que el tipo de contenido relevante para las consultas depende de propiedades relacionadas con el tipo de consulta y la colección de documentos. Los resultados están acordes con resultados existentes en el campo de la recuperación de la información.
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Graf, Erik. "Human information processing based information retrieval." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/5188/.
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This work focused on the investigation of the question how the concept of relevance in Information Retrieval can be validated. The work is motivated by the consistent difficulties of defining the meaning of the concept, and by advances in the field of cognitive science. Analytical and empirical investigations are carried out with the aim of devising a principled approach to the validation of the concept. The foundation for this work was set by interpreting relevance as a phenomenon occurring within the context of two systems: An IR system and the cognitive processing system of the user. In light of the cognitive interpretation of relevance, an analysis of the learnt lessons in cognitive science with regard to the validation of cognitive phenomena was conducted. It identified that construct validity constitutes the dominant approach to the validation of constructs in cognitive science. Construct validity constitutes a proposal for the conduction of validation in scenarios, where no direct observation of a phenomenon is possible. With regard to the limitations on direct observation of a construct (i.e. a postulated theoretic concept), it bases validation on the evaluation of its relations to other constructs. Based on the interpretation of relevance as a product of cognitive processing it was concluded, that the limitations with regard to direct observation apply to its investigation. The evaluation of its applicability to an IR context, focused on the exploration of the nomological network methodology. A nomological network constitutes an analytically constructed set of constructs and their relations. The construction of such a network forms the basis for establishing construct validity through investigation of the relations between constructs. An analysis focused on contemporary insights to the nomological network methodology identified two important aspects with regard to its application in IR. The first aspect is given by a choice of context and the identification of a pool of candidate constructs for the inclusion in the network. The second consists of identifying criteria for the selection of a set of constructs from the candidate pool. The identification of the pertinent constructs for the network was based on a review of the principles of cognitive exploration, and an analysis of the state of the art in text based discourse processing and reasoning. On that basis, a listing of known sub-processes contributing to the pertinent cognitive processing was presented. Based on the identification of a large number of potential candidates, the next step consisted of the inference of criteria for the selection of an initial set of constructs for the network. The investigation of these criteria focused on the consideration of pragmatic and meta-theoretical aspects. Based on a survey of experimental means in cognitive science and IR, five pragmatic criteria for the selection of constructs were presented. Consideration of meta-theoretically motivated criteria required to investigate what the specific challenges with regard to the validation of highly abstract constructs are. This question was explored based on the underlying considerations of the Information Processing paradigm and Newell’s (1994) cognitive bands. This led to the identification of a set of three meta-theoretical criteria for the selection of constructs. Based on the criteria and the demarcated candidate pool, an IR focused nomological network was defined. The network consists of the constructs of relevance and type and grade of word relatedness. A necessary prerequisite for making inferences based on a nomological network consists of the availability of validated measurement instruments for the constructs. To that cause, two validation studies targeting the measurement of the type and grade of relations between words were conducted. The clarification of the question of the validity of the measurement instruments enabled the application of the nomological network. A first step of the application consisted of testing if the constructs in the network are related to each other. Based on the alignment of measurements of relevance and the word related constructs it was concluded to be true. The relation between the constructs was characterized by varying the word related constructs over a large parameter space and observing the effect of this variation on relevance. Three hypotheses relating to different aspects of the relations between the word related constructs and relevance. It was concluded, that the conclusive confirmation of the hypotheses requires an extension of the experimental means underlying the study. Based on converging observations from the empirical investigation of the three hypotheses it was concluded, that semantic and associative relations distinctly differ with regard to their impact on relevance estimation.
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Heravi, Bahareh Rahmanzadeh. "Ontology-based information standards development." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/6267.
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Standards may be argued to be important enablers for achieving interoperability as they aim to provide unambiguous specifications for error-free exchange of documents and information. By implication, therefore, it is important to model and represent the concept of a standard in a clear, precise and unambiguous way. Although standards development organisations usually provide guidelines for the process of developing and approving standards, they are usually more concerned with administrative aspect of the process. As a consequence, the state-of-the-art lacks practical support for developing the structure and content of a standard specification. In short, there is no systematic development method currently available: (a) For developing the conceptual model underpinning a standard; and/or (b) to guide a group of stakeholders to develop a standard specification. Semantic interoperability is considered to be an essential factor for effective interoperation – the ability to achieve semantic interoperability effectively and efficiently being strongly equated with quality by some. Semantics require that the meaning of terms, their relationships and also the restrictions and rules in the standards should be clearly defined in the early stages of standard development and act as a basis for the latter stages. This research proposes that ontology can help standards developers and stakeholders to address the issues of improving conceptual models and providing a robust and shared understanding of the domain. This thesis presents OntoStanD, a comprehensive ontology-based standards development methodology, which utilises the best practices of the existing ontology creation methods. The potential value of OntoStanD is in providing a comprehensive, clear and unambiguous method for developing robust information standards, which are more test friendly and of higher quality. OntoStanD also facilitates standards conformance testing and change management, impacts interoperability and also assists in improved communication among the standards development team. Last, OntoStanD provides an approach that is repeatable, teachable and potentially general enough for creating any kinds of information standard.
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Town, Christopher Phillip. "Ontology based visual information processing." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614908.
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Akpinar, Samet. "Ontology Based Semantic Retrieval Of Video Contents Using Metadata." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608772/index.pdf.
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The aim of this thesis is the development of an infrastructure which is used for semantic retrieval of multimedia contents. Motivated by the needs of semantic search and retrieval of multimedia contents, operating directly on the MPEG-7 based annotations can be thought as a reasonable way for meeting these needs as MPEG-7 is a common standard providing a wide multimedia content description schema. However, it is clear that the MPEG-7 formalism is deficient about the semantics and reasoning support. From this perspective, additionally, we need to represent MPEG-7 descriptions in a new formalism in order to fill the gap about semantics and reasoning. Then, the semantic web and multimedia technologies intercept at this point of multimedia semantics.
In this thesis, OWL Web Ontology Language, which is based on description logic has been utilized to model a connection between the ontology semantics and video metadata. Modeling the domain of the videos using ontologies and the MPEG-7 descriptions, and reasoning on the videos by the help of the logical formalism of these ontologies are the main objectives of the thesis.
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Yuee, Liu. "Ontology-based image annotation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/39611/1/Liu_Yuee_Thesis.pdf.
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With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.
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Kramer, Joshua David. "Agent based personalized information retrieval." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43539.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 69-74).
by Joshua David Kramer.
M.Eng.
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Weng, Zumao. "Distributed knowledge based image contents retrieval and exploration." Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370088.
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Chang, Jia Kang. "Investigation on applying modular ontology to statistical language model for information retrieval." Thesis, University of Central Lancashire, 2015. http://clok.uclan.ac.uk/11803/.
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The objective of this research is to provide a novel approach to improving retrieval performance by exploiting Ontology with the statistical language model (SLM). The proposed methods consist of two major processes, namely ontology-based query expansion (OQE) and ontology-based document classification (ODC). Research experiments have required development of an independent search tool that can combine the OQE and ODC in a traditional SLM-based information retrieval (IR) process using a Web document collection. This research considers the ongoing challenges of modular ontology enhanced SLM-based search and addresses three contribution aspects. The first concerns how to apply modular ontology to query expansion, in a bespoke language model search tool (LMST). The second considers how to incorporate OQE with the language model to improve the search performance. The third examines how to manipulate such semantic-based document classification to improve the smoothing accuracy. The role of ontology in the research is to provide formally described domains of interest that serve as context, to enhance system query effectiveness.
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YARDI, APARNA ARVIND. "CONCEPT BASED INFORMATION ORGANIZATION AND RETRIEVAL." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1152832274.
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Tarakci, Hilal. "An Ontology-based Multimedia Information Management System." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609865/index.pdf.
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In order to manage the content of multimedia data, the content must be annotated. Although any user-defined annotation is acceptable, it is preferable if systems agree on the same annotation format. MPEG-7 is a widely accepted standard for multimedia content annotation. However, in MPEG-7, semantically identical metadata can be represented in multiple ways due to lack of precise semantics in its XML-based syntax. Unfortunately this prevents metadata interoperability. To overcome this problem, MPEG-7 standard is translated into an ontology. In this thesis, MPEG-7 ontology is used on top and the given user-defined ontologies are attached to the MPEG-7 ontology via a user friendly interface, thus building MPEG-7 based ontologies automatically. Our proposed system is an ontology-based multimedia information management framework due to its modular
architecture, ease of integrating with domain specific ontologies naturally and automatic harmonization of MPEG-7 ontology and domain-specific ontologies. Integration with domain specific ontologies is carried out by enabling import of
domain ontologies via a user-friendly interface which makes the system independent of application domains.
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Demirdizen, Goncagul. "An Ontology-driven Video Annotation And Retrieval System." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612592/index.pdf.
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In this thesis, a system, called Ontology-Driven Video Annotation and Retrieval System (OntoVARS) is developed in order to provide a video management system which is used for ontology-driven semantic content annotation and querying. The proposed system is based on MPEG-7 ontology which provides interoperability and common communication platform with other MPEG-7 ontology compatible systems. The Rhizomik MPEG-7 ontology is used as the core ontology and domain specific ontologies are integrated to the core ontology in order to provide ontology-based video content annotation and querying capabilities to the user. The proposed system supports content-based annotation and spatio-temporal data modeling in video databases by using the domain ontology concepts. Moreover, the system enables ontology-driven query formulation and processing according to the domain ontology instances and concepts. In the developed system, ontology-driven concept querying, spatio-temporal querying, region-based and time-based querying capabilities are performed as simple querying types. Besides these simple query types, compound queries are also generated by combining simple queries with "("
, "
)"
, "
AND"
and "
OR"
operators. For all these query types, the system supports both general and video specific query processing. By this means, the user is able to pose queries on all videos in the video databases as well as the details of a specific video of interest.
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Modica, Giovanni. "A framework for automatic ontology generation from autonomous web applications." Master's thesis, Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-09032002-165210.
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Muthaiyah, Saravanan. "A framework and methodology for ontology mediation through semantic and syntactic mapping." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3070.
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Thesis (Ph. D.)--George Mason University, 2008.Vita: p. 177. Thesis director: Larry Kerschberg. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed July 3, 2008). Includes bibliographical references (p. 169-176). Also issued in print.
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Ngo, Duy Hoa. "Enhancing Ontology Matching by Using Machine Learning, Graph Matching and Information Retrieval Techniques." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20096/document.
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Ces dernières années, les ontologies ont suscité de nombreux travaux dans le domaine du web sémantique. Elles sont utilisées pour fournir le vocabulaire sémantique permettant de rendre la connaissance du domaine disponible pour l'échange et l'interprétation au travers des systèmes d'information. Toutefois, en raison de la nature décentralisée du web sémantique, les ontologies sont très hétérogènes. Cette hétérogénéité provoque le problème de la variation de sens ou ambiguïté dans l'interprétation des entités et, par conséquent, elle empêche le partage des connaissances du domaine. L'alignement d'ontologies, qui a pour but la découverte des correspondances sémantiques entre des ontologies, devient une tâche cruciale pour résoudre ce problème d'hétérogénéité dans les applications du web sémantique. Les principaux défis dans le domaine de l'alignement d'ontologies ont été décrits dans des études récentes. Parmi eux, la sélection de mesures de similarité appropriées ainsi que le réglage de la configuration de leur combinaison sont connus pour être des problèmes fondamentaux que la communauté doit traiter. En outre, la vérification de la cohérence sémantique des correspondances est connue pour être une tâche importante. Par ailleurs, la difficulté du problème augmente avec la taille des ontologies. Pour faire face à ces défis, nous proposons dans cette thèse une nouvelle approche, qui combine différentes techniques issues des domaines de l'apprentissage automatique, d'appariement de graphes et de recherche d'information en vue d'améliorer la qualité de l'alignement d'ontologies. En effet, nous utilisons des techniques de recherche d'information pour concevoir de nouvelles mesures de similarité efficaces afin de comparer les étiquettes et les profils d'entités de contexte au niveau des entités. Nous appliquons également une méthode d'appariement de graphes appelée propagation de similarité au niveau de la structure qui découvre effectivement des correspondances en exploitant des informations structurelles des entités. Pour combiner les mesures de similarité au niveau des entités, nous transformons la tâche de l'alignement d'ontologie en une tâche de classification de l'apprentissage automatique. Par ailleurs, nous proposons une méthode dynamique de la somme pondérée pour combiner automatiquement les correspondances obtenues au niveau des entités et celles obtenues au niveau de la structure. Afin d'écarter les correspondances incohérentes, nous avons conçu une nouvelle méthode de filtrage sémantique. Enfin, pour traiter le problème de l'alignement d'ontologies à large échelle, nous proposons deux méthodes de sélection des candidats pour réduire l'espace de calcul.Toutes ces contributions ont été mises en œuvre dans un prototype nommé YAM++. Pour évaluer notre approche, nous avons utilisé des données du banc d'essai de la compétition OAEI : Benchmark, Conference, Multifarm, Anatomy, Library and Large Biomedical Ontologies. Les résultats expérimentaux montrent que les méthodes proposées sont très efficaces. De plus, en comparaison avec les autres participants à la compétition OAEI, YAM++ a montré sa compétitivité et a acquis une position de haut rangIn recent years, ontologies have attracted a lot of attention in the Computer Science community, especially in the Semantic Web field. They serve as explicit conceptual knowledge models and provide the semantic vocabularies that make domain knowledge available for exchange and interpretation among information systems. However, due to the decentralized nature of the semantic web, ontologies are highlyheterogeneous. This heterogeneity mainly causes the problem of variation in meaning or ambiguity in entity interpretation and, consequently, it prevents domain knowledge sharing. Therefore, ontology matching, which discovers correspondences between semantically related entities of ontologies, becomes a crucial task in semantic web applications.Several challenges to the field of ontology matching have been outlined in recent research. Among them, selection of the appropriate similarity measures as well as configuration tuning of their combination are known as fundamental issues that the community should deal with. In addition, verifying the semantic coherent of the discovered alignment is also known as a crucial task. Furthermore, the difficulty of the problem grows with the size of the ontologies. To deal with these challenges, in this thesis, we propose a novel matching approach, which combines different techniques coming from the fields of machine learning, graph matching and information retrieval in order to enhance the ontology matching quality. Indeed, we make use of information retrieval techniques to design new effective similarity measures for comparing labels and context profiles of entities at element level. We also apply a graph matching method named similarity propagation at structure level that effectively discovers mappings by exploring structural information of entities in the input ontologies. In terms of combination similarity measures at element level, we transform the ontology matching task into a classification task in machine learning. Besides, we propose a dynamic weighted sum method to automatically combine the matching results obtained from the element and structure level matchers. In order to remove inconsistent mappings, we design a new fast semantic filtering method. Finally, to deal with large scale ontology matching task, we propose two candidate selection methods to reduce computational space.All these contributions have been implemented in a prototype named YAM++. To evaluate our approach, we adopt various tracks namely Benchmark, Conference, Multifarm, Anatomy, Library and Large BiomedicalOntologies from the OAEI campaign. The experimental results show that the proposed matching methods work effectively. Moreover, in comparison to other participants in OAEI campaigns, YAM++ showed to be highly competitive and gained a high ranking position
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Aghajani, Nooshin. "Semoogle - An Ontology Based Search Engine." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-19086.
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In this thesis, we present a prototype for search engine to show how such a semantic search application based on ontology techniques contributes to save time for user, and improve the quality of relevant search results compared to a traditional search engine. This system is built as a query improvement module, which uses ontology and sorts the results search based on four predefined categories. The first and important part of the implementation of search engine prototype is to apply ontology to define the meaning and the relations between the queries in default domain of the study. Next, categorization of the results is carried out in order to improve the quality of result search presentation based on categorization-list. The ontology used in this search engine prototype includes sample of terms in safety and security domain, which is capable to be modified in this domain, or can be substituted by another ontology in the other fields of study. The process is continued by searching the enriched query through the Web using Google interface application search engine. The application uses ranking algorithms to categorize and organize the results of Google search in four categories, i.e. History, Mechanism, Prevention, and Case study. The predefined categories can be substituted to the other categories based on user preferences in other studies using different categorizes.
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Krishnan, Sharenya. "Text-Based Information Retrieval Using Relevance Feedback." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-53603.
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Europeana, a freely accessible digital library with an idea to make Europe's cultural and scientific heritage available to the public was founded by the European Commission in 2008. The goal was to deliver a semantically enriched digital content with multilingual access to it. Even though they managed to increase the content of data they slowly faced the problem of retrieving information in an unstructured form. So to complement the Europeana portal services, ASSETS (Advanced Search Service and Enhanced Technological Solutions) was introduced with services that sought to improve the usability and accessibility of Europeana. My contribution is to study different text-based information retrieval models, their relevance feedback techniques and to implement one simple model. The thesis explains a detailed overview of the information retrieval process along with the implementation of the chosen strategy for relevance feedback that generates automatic query expansion. Finally, the thesis concludes with the analysis made using relevance feedback, discussion on the model implemented and then an assessment on future use of this model both as a continuation of my work and using this model in ASSETS.
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Karamuftuoglu, H. Murat. "Knowledge based information retrieval : a semiotic approach." Thesis, City, University of London, 1998. http://openaccess.city.ac.uk/20112/.
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The overall objective of this study is to analyze the document retrieval process and the main information retrieval (IR) concepts from the point of view of semiotics and design retrieval mechanisms based on the findings of the semiotic analysis of the retrieval situation. Semiotics is a discipline which studies 'sign systems' and how signs are exchanged in communication. The semiotic view of IR interaction presented in this dissertation views document retrieval as a kind of human communication process taking place in a social and cultural realm. The most important result of the semiotic model developed is the explication of the distinction between the knowledge production and transfer functions of document retrieval. The consequence of this finding is the conceptualization of the retrieval process as a dynamic and complex interplay between knowledge production and transfer tasks. It is hypothesised that, in the case of knowledge production, users of retrieval systems are interested in exploring new areas of the document collection which are not a priori known. Two knowledge based systems are developed based on the Okapi probabilistic retrieval system. The purpose of the retrieval systems designed is posited, in general terms, as to suggest the users new search areas of potential interest. This is achieved by treating the Inspec thesaurus as a semantic network, and applying a heuristic spreading activation technique to generate clusters of terms linked in the Inspec thesaurus. Each cluster or batch of terms is conceived as representing a part of the general search area defined by the initial user search terms. The main design objective here is to enable the user to identify new search areas from the term information contained in the batches. Two evaluation experiments were carried out with real users who had real information needs to test whether the batches were actually effective in defining search areas related to the original user queries and whether they were useful in pointing new areas which were potentially relevant to the users. A number of hypotheses related to the retrieval effectiveness of the knowledge based systems designed were also tested in the experiments. The main findings of the experiments indicate that: • the batches were useful in representing search domains relevant to the users' queries • in many cases the batches represented new ideas or new search domains to the users • the knowledge based systems had similar retrieval effectiveness in terms of precision as the Okapi system.
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Mena, Eduardo Illarramendi Arantza. "Ontology-based query processing for global information systems /." Boston [u.a.] : Kluwer Acad. Publ, 2001. http://www.loc.gov/catdir/enhancements/fy0813/2001029621-d.html.
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Karpur, Anoop. "Ontology Information Processing toMatrix-Based Approaches for ConceptualDesign." Thesis, Linköpings universitet, Fluida och mekatroniska system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179662.
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This thesis aims to develop a methodology to translate an ontology to a matrixbased methods that is case independent. In the larger picture this is one of manycomplexities in systems engineering approach. The focus in this work is in the domainof conceptual design, hence, matrix based methods that are most suitable for it areinvestigated. The flexible nature of the ontology always allows for expansion withnew information about the concepts, and from that perspective a parsing scriptwas developed to extract information from the ontology, it was also designed to beflexible. The script can be used to extract any desired type of information by alteringa few keywords as inputs. The advantage is that the same structure of the script canbe used to extract information from many different ontologies.Using this method the matrices relevant for conceptual design were formulated.These matrices were further compared to each other. Also, a few recommendationswere given for the improvement of the method to adapt towards high fidelity. Thecurrent method is capable of solving problems with low fidelity, however this formsa basis to expand the method to high fidelity problems.
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Gutierrez, Fernando. "A Hybrid Approach for Ontology-based Information Extraction." Thesis, University of Oregon, 2016. http://hdl.handle.net/1794/19729.
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Information extraction (IE) is the process of automatically transforming written natural language (i.e., text) into structured information, such as a knowledge base. However, because natural language is inherently ambiguous, this transformation process is highly complex. On the other hand, as Information Extraction moves from the analysis of scientific documents to the analysis of Internet textual content, we cannot rely completely on the assumption that the content of the text is correct. Indeed, in contrast to scientific documents, which are peer reviewed, Internet content is not verified for the quality and correctness.
Thus, two main issues that affect the IE process are the complexity of the extraction process and the quality of the data.
In this dissertation, we propose an improved ontology-based IE (OBIE) by providing solutions to these issues of accuracy and content quality. Based on a hybrid strategy that combines aspects of IE that are usually considered as opposite to each other, or that are not even considered, we intend to improve IE by developing a more accurate extraction and new functionality (semantic error detection). Our approach is based on OBIE, a sub-area of IE, which reduces extraction complexity by including domain knowledge, in the form of concepts and relationships of the domain, to guide the extraction process.
We address the complexity of extraction by combining information extractors that have different implementations. By integrating different types of implementation into one extraction system, we can produce a more accurate extraction. For each concept or relationship in the ontology, we can select the best implementation for extraction, or we can combine both implementations under an ensemble learning schema. In tandem, we address the quality of information by determining its semantic correctness with regard to domain knowledge. We define two methods for semantic error detection: by predefining the types of errors expected in the text or by applying logic reasoning to the text.
This dissertation includes both published and unpublished coauthored material.
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Deyab, Rodwan Bakkar. "Ontology-based information extraction from learning management systems." Master's thesis, Universidade de Évora, 2017. http://hdl.handle.net/10174/20996.
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In this work we present a system for information extraction from Learning Management Systems. This system
is ontology-based. It retrieves information according to the structure of the ontology to populate the ontology.
We graphically present statistics about the ontology data. These statistics present latent knowledge which is
difficult to see in the traditional Learning Management System. To answer questions about the ontology, a
question answering system was developed using Natural Language Processing in the conversion of the natural
language question into an ontology query language; Sumário:
Extração de Informação de Sistemas de Gestão
para Educação Usando Ontologias
Neste dissertação apresentamos um sistema de extracção de informação de sistemas de gestão para educação
(Learning Management Systems). Este sistema é baseado em ontologias e extrai informação de acordo com a
estrutura da ontologia para a popular. Também permite apresentar graficamente algumas estatísticas sobre
os dados da ontologia. Estas estatísticas revelam o conhecimento latente que é difícil de ver num sistema
tradicional de gestão para a educação. Para poder responder a perguntas sobre os dados da ontologia, um
sistema de resposta automática a perguntas em língua natural foi desenvolvido usando Processamento de
Língua Natural para converter as perguntas para linguagem de interrogação de ontologias.
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George, David. "Examining the application of modular and contextualised ontology in query expansions for information retrieval." Thesis, University of Central Lancashire, 2010. http://clok.uclan.ac.uk/1865/.
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This research considers the ongoing challenge of semantics-based search from the perspective of how to exploit Semantic Web languages for search in the current Web environment. The purpose of the PhD was to use ontology-based query expansion (OQE) to improve search effectiveness by increasing search precision, i.e. retrieving relevant documents in the topmost ranked positions in a returned document list. Query experiments have required a novel search tool that can combine Semantic Web technologies in an otherwise traditional IR process using a Web document collection.
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Yeung, Chung Kei. "Ontological model for information systems development methodology." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/702.
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Bremer, Jan-Marco. "Next-generation information retrieval : integrating document and data retrieval based on XML /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2003. http://uclibs.org/PID/11984.
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