Relevant bibliographies by topics / RDF-S semantics

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'RDF-S semantics'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "RDF-S semantics"

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De Bruijn, J., and S. Heymans. "Logical Foundations of RDF(S) with Datatypes." Journal of Artificial Intelligence Research 38 (August 20, 2010): 535–68. http://dx.doi.org/10.1613/jair.3088.

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The Resource Description Framework (RDF) is a Semantic Web standard that provides a data language, simply called RDF, as well as a lightweight ontology language, called RDF Schema. We investigate embeddings of RDF in logic and show how standard logic programming and description logic technology can be used for reasoning with RDF. We subsequently consider extensions of RDF with datatype support, considering D entailment, defined in the RDF semantics specification, and D* entailment, a semantic weakening of D entailment, introduced by ter Horst. We use the embeddings and properties of the logics
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Gangemi, Aldo, Valentina Presutti, Recupero Diego Reforgiato, Andrea Giovanni Nuzzolese, Francesco Draicchio, and Misael Mongiovì. "Semantic Web Machine Reading with FRED." Semantic Web 8, no. 6 (2017): 873–93. https://doi.org/10.3233/SW-160240.

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A formal machine reader is a tool able to transform natural language text into formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a formal machine reader for the semantic web: its output is a RDF/OWL graph, whose design is based on frame semantics. FRED’s graph are domain- and task-independent, making the tool suitable to be used as a semantic middleware for domain- or task-specific applications. To serve this purpose, it is available both as REST service and as Python library. This paper provides details about FRED&
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Analyti, A., G. Antoniou, C. V. Damasio, and G. Wagner. "Extended RDF as a Semantic Foundation of Rule Markup Languages." Journal of Artificial Intelligence Research 32 (May 15, 2008): 37–94. http://dx.doi.org/10.1613/jair.2425.

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Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong
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Gasevic, Dragan. "Petri nets on the semantic web guidelines and infrastructure." Computer Science and Information Systems 1, no. 2 (2004): 127–51. http://dx.doi.org/10.2298/csis0402127g.

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This paper gives the Petri net ontology as the most important element in providing Petri net support for the Semantic Web. Available Petri net formal descriptions are: metamodels, UML profiles, ontologies and syntax. Metamodels are useful, but their main purpose is for Petri net tools. Although the current Petri-net community effort Petri Net Markup Language (PNML) is XML-based, it lacks a precise definition of semantics. Existing Petri net ontologies are partial solutions specialized for a specific problem. In order to show current Petri net model sharing features we use P3 tool that uses PNM
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Sutejo, Agus, Rahmat Gernowo, and Michael Andreas Purwoadi. "Ontology learning from object-relational mapping metadata and relational database." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1116. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp1116-1125.

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Ontologies play an important role in representing the semantics of data sources. Building an ontology as a representation of domain knowledge from available data sources is not a simple process, particularly when dealing with relational data, which remains prevalent in existing knowledge systems. In this study, we create an ontology from a relational database using object-relational mapping (ORM) metadata as additional rules for mapping. Our method comprises two main phases: ontology schema construction using ORM metadata and the generation of ontology instances from the relational database. D
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Sutejo, Agus, Rahmat Gernowo, and Michael Andreas Purwoadi. "Ontology learning from object-relational mapping metadata and relational database." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1116–25. https://doi.org/10.11591/ijeecs.v33.i2.pp1116-1125.

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Ontologies play an important role in representing the semantics of data sources. Building an ontology as a representation of domain knowledge from available data sources is not a simple process, particularly when dealing with relational data, which remains prevalent in existing knowledge systems. In this study, we create an ontology from a relational database using object-relational mapping (ORM) metadata as additional rules for mapping. Our method comprises two main phases: ontology schema construction using ORM metadata and the generation of ontology instances from the relational database. D
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J, I. Christy Eunaicy, and Sundara Vadivelu V. "A SEMWORD based Semantic Secure Content Retrieval System in E-learning." Indian Journal of Science and Technology 16, no. 31 (2023): 2447–57. https://doi.org/10.17485/IJST/v16i31.833.

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Abstract <strong>Objectives:</strong>&nbsp;The semantic e-learning model aims to be the next generation of e-learning as it enables advanced representation, manipulation and re-use of learning materials and knowledge semantics in efficient online learning experiences. The modeling of semantic-based content retrieval for e-learning with secured content. This system will provide users with access to relevant educational resources in an easy-to-use, secure, and anonymous environment using semantic technologies.&nbsp;<strong>Methods:</strong>&nbsp;Semantic Web resource description format is propos
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Jansen, L., and S. Schulz. "Formal Ontologies in Biomedical Knowledge Representation." Yearbook of Medical Informatics 22, no. 01 (2013): 132–46. http://dx.doi.org/10.1055/s-0038-1638845.

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Summary Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpre
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Ma, Zongmin, Daiyi Li, Jiawen Lu, Ruizhe Ma, and Li Yan. "RDF(S) Store in Object-Relational Databases." Journal of Database Management 35, no. 1 (2023): 1–32. http://dx.doi.org/10.4018/jdm.334710.

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The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object
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Fafalios, Pavlos, Manolis Baritakis, and Yannis Tzitzikas. "Exploiting Linked Data for Open and Configurable Named Entity Extraction." International Journal on Artificial Intelligence Tools 24, no. 02 (2015): 1540012. http://dx.doi.org/10.1142/s0218213015400126.

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Named Entity Extraction (NEE) is the process of identifying entities in texts and, very commonly, linking them to related (Web) resources. This task is useful in several applications, e.g. for question answering, annotating documents, post-processing of search results, etc. However, existing NEE tools lack an open or easy configuration although this is very important for building domain-specific applications. For example, supporting a new category of entities, or specifying how to link the detected entities with online resources, is either impossible or very laborious. In this paper, we show h
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Dissertations / Theses on the topic "RDF-S semantics"

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Ouksili, Hanane. "Exploration et interrogation de données RDF intégrant de la connaissance métier." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLV069.

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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 b
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Book chapters on the topic "RDF-S semantics"

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Troullinou, Georgia, Haridimos Kondylakis, Evangelia Daskalaki, and Dimitris Plexousakis. "RDF Digest: Efficient Summarization of RDF/S KBs." In The Semantic Web. Latest Advances and New Domains. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18818-8_8.

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Pappas, Alexandros, Georgia Troullinou, Giannis Roussakis, Haridimos Kondylakis, and Dimitris Plexousakis. "Exploring Importance Measures for Summarizing RDF/S KBs." In The Semantic Web. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58068-5_24.

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Theoharis, Yannis, Vassilis Christophides, and Grigoris Karvounarakis. "Benchmarking Database Representations of RDF/S Stores." In The Semantic Web – ISWC 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11574620_49.

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de Bruijn, Jos, and Stijn Heymans. "Logical Foundations of (e)RDF(S): Complexity and Reasoning." In The Semantic Web. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76298-0_7.

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Tzitzikas, Yannis, Christina Lantzaki, and Dimitris Zeginis. "Blank Node Matching and RDF/S Comparison Functions." In The Semantic Web – ISWC 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35176-1_37.

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Serfiotis, Giorgos, Ioanna Koffina, Vassilis Christophides, and Val Tannen. "Containment and Minimization of RDF/S Query Patterns." In The Semantic Web – ISWC 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11574620_44.

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Ognyanov, Damyan, and Atanas Kiryakov. "Tracking Changes in RDF(S) Repositories." In Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45810-7_33.

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Jonquet, Clement, John Graybeal, Syphax Bouazzouni, et al. "Ontology Repositories and Semantic Artefact Catalogues with the OntoPortal Technology." In The Semantic Web – ISWC 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47243-5_3.

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AbstractThere is an explosion in the number of ontologies and semantic artefacts being produced in science. This paper discusses the need for common platforms to receive, host, serve, align, and enable their reuse. Ontology repositories and semantic artefact catalogues are necessary to address this need and to make ontologies FAIR (Findable, Accessible, Interoperable, and Reusable). The OntoPortal Alliance (https://ontoportal.org) is a consortium of research and infrastructure teams dedicated to promoting the development of such repositories based on the open, collaboratively developed OntoPor
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Richards, Debbie, and Marta Sabou. "Semantic Markup for Semantic Web Tools: A DAML-S Description of an RDF-Store." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39718-2_18.

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Bryl Volha, Giuliano Claudio, Serafini Luciano, and Tymoshenko Kateryna. "Using Background Knowledge to Support Coreference Resolution." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-606-5-759.

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Systems based on statistical and machine learning methods have been shown to be extremely effective and scalable for the analysis of large amount of textual data. However, in the recent years, it becomes evident that one of the most important direction of improvement in natural language processing (NLP) tasks, like word sense disambiguation, coreference resolution, relation extraction, and other tasks related to knowledge extraction, is by exploiting semantics. While in the past, the unavailability of rich and complete semantic descriptions constituted a serious limitation of their applicabili
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Conference papers on the topic "RDF-S semantics"

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Raimbault, Thomas. "Overviewing the RDF(S) Semantic Web." In 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677074.

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Aguado de Cea, Guadalupe, Inmaculada Álvarez-de-Mon, Antonio Pareja-Lora, and Rosario Plaza-Arteche. "RDF(S)/XML linguistic annotation of semantic web pages." In the 2nd workshop. Association for Computational Linguistics, 2002. http://dx.doi.org/10.3115/1118808.1118809.

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Cappelli, Maria Assunta, Ashley Caselli, and Giovanna Di Marzo Serugendo. "Enriching RDF-based Document Management System with Semantoc-based Reasoning (S)." In The 29th International Conference on Distributed Multimedia Systems. KSI Research Inc., 2023. http://dx.doi.org/10.18293/dmsviva2023-034.

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