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  • × theme_ss:"Semantic Web"
  1. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.06 
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    Content
    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  2. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.02 
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    Date
    31. 7.2010 16:58:22
    Source
    Semantic digital libraries. Eds.: S.R. Kruk, B. McDaniel
  3. Thuraisingham, B.: XML databases and the semantic Web (2002) 0.01 
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  4. RDF Semantics (2004) 0.01 
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    Editor
    Hayes, P. u. B. McBride
  5. Call, A.; Gottlob, G.; Pieris, A.: ¬The return of the entity-relationship model : ontological query answering (2012) 0.01 
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    Abstract
    The Entity-Relationship (ER) model is a fundamental formalism for conceptual modeling in database design; it was introduced by Chen in his milestone paper, and it is now widely used, being flexible and easily understood by practitioners. With the rise of the Semantic Web, conceptual modeling formalisms have gained importance again as ontology formalisms, in the Semantic Web parlance. Ontologies and conceptual models are aimed at representing, rather than the structure of data, the domain of interest, that is, the fragment of the real world that is being represented by the data and the schema. A prominent formalism for modeling ontologies are Description Logics (DLs), which are decidable fragments of first-order logic, particularly suitable for ontological modeling and querying. In particular, DL ontologies are sets of assertions describing sets of objects and (usually binary) relations among such sets, exactly in the same fashion as the ER model. Recently, research on DLs has been focusing on the problem of answering queries under ontologies, that is, given a query q, an instance B, and an ontology X, answering q under B and amounts to compute the answers that are logically entailed from B by using the assertions of X. In this context, where data size is usually large, a central issue the data complexity of query answering, i.e., the computational complexity with respect to the data set B only, while the ontology X and the query q are fixed.
  6. Dextre Clarke, S.G.: Challenges and opportunities for KOS standards (2007) 0.01 
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    Date
    22. 9.2007 15:41:14
  7. Liang, A.; Salokhe, G.; Sini, M.; Keizer, J.: Towards an infrastructure for semantic applications : methodologies for semantic integration of heterogeneous resources (2006) 0.01 
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    Abstract
    The semantic heterogeneity presented by Web information in the Agricultural domain presents tremendous information retrieval challenges. This article presents work taking place at the Food and Agriculture Organizations (FAO) which addresses this challenge. Based on the analysis of resources in the domain of agriculture, this paper proposes (a) an application profile (AP) for dealing with the problem of heterogeneity originating from differences in terminologies, domain coverage, and domain modelling, and (b) a root application ontology (AAO) based on the application profile which can serve as a basis for extending knowledge of the domain. The paper explains how even a small investment in the enhancement of relations between vocabularies, both metadata and domain-specific, yields a relatively large return on investment.
  8. Vatant, B.: Porting library vocabularies to the Semantic Web, and back : a win-win round trip (2010) 0.01 
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  9. Wielinga, B.; Wielemaker, J.; Schreiber, G.; Assem, M. van: Methods for porting resources to the Semantic Web (2004) 0.01 
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  10. Glimm, B.; Hogan, A.; Krötzsch, M.; Polleres, A.: OWL: Yet to arrive on the Web of Data? (2012) 0.01 
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  11. Sartini, B.; Erp, M. van; Gangemi, A.: Marriage is a peach and a chalice : modelling cultural symbolism on the Semantic Web (2021) 0.01 
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  12. Broughton, V.: Automatic metadata generation : Digital resource description without human intervention (2007) 0.01 
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    Date
    22. 9.2007 15:41:14
  13. Tudhope, D.: Knowledge Organization System Services : brief review of NKOS activities and possibility of KOS registries (2007) 0.01 
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    Date
    22. 9.2007 15:41:14
  14. Sini, M.; Lauser, B.; Salokhe, G.; Keizer, J.; Katz, S.: ¬The AGROVOC concept server : rationale, goals and usage (2008) 0.00 
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  15. Breslin, J.G.: Social semantic information spaces (2009) 0.00 
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    Source
    Semantic digital libraries. Eds.: S.R. Kruk, B. McDaniel
  16. Maltese, V.; Farazi, F.: Towards the integration of knowledge organization systems with the linked data cloud (2011) 0.00 
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    Abstract
    In representing the shared view of all the people involved, building a knowledge organization system (KOS) from scratch is extremely costly, and it is therefore fundamental to reuse existing resources. This can be done by progressively extending the KOS with knowledge coming from similar KOSs and by promoting interoperability among them. The linked data initiative is indeed encouraging people to share and integrate their datasets into a giant network of interconnected resources. This enables different applications to interoperate and share their data. The integration should take into account the purpose of the datasets, however, and make explicit the semantics. In fact, the difference in the purpose is reflected in the difference in the semantics. With this paper we (a) highlight the potential problems that may arise by not taking into account purpose and semantics; (b) make clear how the difference in the purpose is reflected in totally different semantics and (c) provide an algorithm to translate from one semantics into another as a preliminary step towards the integration of ontologies designed for different purposes. This will allow reusing the ontologies even in contexts different from those in which they were designed.
  17. Djioua, B.; Desclés, J.-P.; Alrahabi, M.: Searching and mining with semantic categories (2012) 0.00 
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  18. Maltese, V.; Farazi, F.: Towards the integration of knowledge organization systems with the linked data cloud (2011) 0.00 
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    Abstract
    In representing the shared view of all the people involved, building a Knowledge Organization System (KOS) from scratch is extremely costly, and it is therefore fundamental to reuse existing resources. This can be done by progressively extending the KOS with knowledge coming from similar KOS and by promoting interoperability among them. The linked data initiative is indeed fostering people to share and integrate their datasets into a giant network of interconnected resources. This enables different applications to interoperate and share their data. However, the integration should take into account the purpose of the datasets and make explicit the semantics. In fact, the difference in the purpose is reflected in the difference in the semantics. With this paper we (a) highlight the potential problems that may arise by not taking into account purpose and semantics, (b) make clear how the difference in the purpose is reflected in totally different semantics and (c) provide an algorithm to translate from one semantic into another as a preliminary step towards the integration of ontologies designed for different purposes. This will allow reusing the ontologies even in contexts different from those in which they were designed.
  19. Papadakis, I. et al.: Highlighting timely information in libraries through social and semantic Web technologies (2016) 0.00 
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  20. Kiryakov, A.; Popov, B.; Terziev, I.; Manov, D.; Ognyanoff, D.: Semantic annotation, indexing, and retrieval (2004) 0.00 
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