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  • × theme_ss:"Semantic Web"
  • × theme_ss:"Wissensrepräsentation"
  • × type_ss:"a"
  1. Zhitomirsky-Geffet, M.; Bar-Ilan, J.: Towards maximal unification of semantically diverse ontologies for controversial domains (2014) 0.02
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    Abstract
    Purpose - Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations. Design/methodology/approach - Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies' semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies. Findings - To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies. Research limitations/implications - This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research. Practical implications - This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results. Originality/value - To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.
    Date
    20. 1.2015 18:30:22
  2. Davies, J.; Fensel, D.; Harmelen, F. van: Conclusions: ontology-driven knowledge management : towards the Semantic Web? (2004) 0.01
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    Abstract
    The global economy is rapidly becoming more and more knowledge intensive. Knowledge is now widely recognized as the fourth production factor, on an equal footing with the traditional production factors of labour, capital and materials. Managing knowledge is as important as the traditional management of labour, capital and materials. In this book, we have shown how Semantic Web technology can make an important contribution to knowledge management.
  3. Zeng, M.L.; Fan, W.; Lin, X.: SKOS for an integrated vocabulary structure (2008) 0.01
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    Abstract
    In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  4. Fluit, C.; Horst, H. ter; Meer, J. van der; Sabou, M.; Mika, P.: Spectacle (2004) 0.01
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    Abstract
    Many Semantic Web initiatives improve the capabilities of machines to exchange the meaning of information with other machines. These efforts lead to an increased quality of the application's results, but their user interfaces take little or no advantage of the semantic richness. For example, an ontology-based search engine will use its ontology when evaluating the user's query (e.g. for query formulation, disambiguation or evaluation), but fails to use it to significantly enrich the presentation of the results to a human user. For example, one could imagine replacing the endless list of hits with a structured presentation based on the semantic properties of the hits. Another problem is that the modelling of a domain is done from a single perspective (most often that of the information provider). Therefore, presentation based on the resulting ontology is unlikely to satisfy the needs of all the different types of users of the information. So even assuming an ontology for the domain is in place, mapping that ontology to the needs of individual users - based on their tasks, expertise and personal preferences - is not trivial.
  5. Lassalle, E.; Lassalle, E.: Semantic models in information retrieval (2012) 0.01
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    Abstract
    Robertson and Spärck Jones pioneered experimental probabilistic models (Binary Independence Model) with both a typology generalizing the Boolean model, a frequency counting to calculate elementary weightings, and their combination into a global probabilistic estimation. However, this model did not consider indexing terms dependencies. An extension to mixture models (e.g., using a 2-Poisson law) made it possible to take into account these dependencies from a macroscopic point of view (BM25), as well as a shallow linguistic processing of co-references. New approaches (language models, for example "bag of words" models, probabilistic dependencies between requests and documents, and consequently Bayesian inference using Dirichlet prior conjugate) furnished new solutions for documents structuring (categorization) and for index smoothing. Presently, in these probabilistic models the main issues have been addressed from a formal point of view only. Thus, linguistic properties are neglected in the indexing language. The authors examine how a linguistic and semantic modeling can be integrated in indexing languages and set up a hybrid model that makes it possible to deal with different information retrieval problems in a unified way.
  6. Soergel, D.: SemWeb: proposal for an open, multifunctional, multilingual system for integrated access to knowledge about concepts and terminology (1996) 0.00
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    Source
    Knowledge organization and change: Proceedings of the Fourth International ISKO Conference, 15-18 July 1996, Library of Congress, Washington, DC. Ed.: R. Green
  7. Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007) 0.00
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    Source
    Proceeding ISWC'07/ASWC'07 : Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Ed.: K. Aberer et al
  8. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.00
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    Date
    31. 7.2010 16:58:22
  9. Gendt, M. van; Isaac, I.; Meij, L. van der; Schlobach, S.: Semantic Web techniques for multiple views on heterogeneous collections : a case study (2006) 0.00
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    Source
    Research and advanced technology for digital libraries : 10th European conference, proceedings / ECDL 2006, Alicante, Spain, September 17 - 22, 2006
  10. Prud'hommeaux, E.; Gayo, E.: RDF ventures to boldly meet your most pedestrian needs (2015) 0.00
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    Source
    Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22
  11. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.00
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    Date
    16.11.2018 14:22:01