Search (104 results, page 1 of 6)

  • × theme_ss:"Wissensrepräsentation"
  1. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.03
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    Date
    13.12.2017 14:17:22
  2. Giunchiglia, F.; Villafiorita, A.; Walsh, T.: Theories of abstraction (1997) 0.03
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    Date
    1.10.2018 14:13:22
  3. Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (2010) 0.02
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    Source
    Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
  4. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.02
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  5. Beppler, F.D.; Fonseca, F.T.; Pacheco, R.C.S.: Hermeneus: an architecture for an ontology-enabled information retrieval (2008) 0.02
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    Date
    28.11.2016 12:43:22
    Source
    http://www.personal.psu.edu/faculty/f/u/fuf1/hermeneus/Hermeneus_architecture.pdf
  6. Kiren, T.; Shoaib, M.: ¬A novel ontology matching approach using key concepts (2016) 0.02
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    Abstract
    Purpose Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concepts that have less likelihood of being matched. Design/methodology/approach Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson's correlation coefficient and IR measures precision, recall and F-measure. Findings Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed. Originality/value On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.
    Date
    20. 1.2015 18:30:22
  7. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.02
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    Date
    16.11.2018 14:22:01
  8. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.01
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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.
  9. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.01
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    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
  10. Kutschera, F. von: ¬Der erkenntnistheoretische Realismus (1992) 0.01
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  11. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.01
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    Pages
    S.11-22
  12. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.01
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    Date
    26.12.2011 13:22:07
  13. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.01
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    Date
    30. 5.2010 16:22:35
  14. Nielsen, M.: Neuronale Netze : Alpha Go - Computer lernen Intuition (2018) 0.01
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    Source
    Spektrum der Wissenschaft. 2018, H.1, S.22-27
  15. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.01
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    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
  16. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.01
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    Date
    31. 7.2010 16:58:22
  17. OWL Web Ontology Language Test Cases (2004) 0.01
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    Date
    14. 8.2011 13:33:22
  18. Hauff-Hartig, S.: Wissensrepräsentation durch RDF: Drei angewandte Forschungsbeispiele : Bitte recht vielfältig: Wie Wissensgraphen, Disco und FaBiO Struktur in Mangas und die Humanities bringen (2021) 0.01
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    Date
    22. 5.2021 12:43:05
  19. Assem, M. van; Rijgersberg, H.; Wigham, M.; Top, J.: Converting and annotating quantitative data tables (2010) 0.01
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    Abstract
    Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to RDF or other suitable format diffiult. For example, the table header cell "f(Hz)" refers to frequency measured in Hertz, but the symbol "f" can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ontology, which allows to improve performance on "sloppy" datasets not yet targeted by existing systems.
    Source
    The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Eds.: Peter F. Patel-Schneider et al
  20. Giunchiglia, F.; Zaihrayeu, I.; Farazi, F.: Converting classifications into OWL ontologies (2009) 0.01
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Authors

Years

Languages

  • e 85
  • d 18
  • f 1
  • More… Less…

Types

  • a 78
  • el 25
  • x 7
  • m 5
  • n 3
  • r 2
  • s 2
  • More… Less…