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  • × theme_ss:"Wissensrepräsentation"
  1. 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) 0.04 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  2. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.02 
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    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  3. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.02 
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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.
  4. Román, J.H.; Hulin, K.J.; Collins, L.M.; Powell, J.E.: Entity disambiguation using semantic networks (2012) 0.02 
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    Abstract
    A major stumbling block preventing machines from understanding text is the problem of entity disambiguation. While humans find it easy to determine that a person named in one story is the same person referenced in a second story, machines rely heavily on crude heuristics such as string matching and stemming to make guesses as to whether nouns are coreferent. A key advantage that humans have over machines is the ability to mentally make connections between ideas and, based on these connections, reason how likely two entities are to be the same. Mirroring this natural thought process, we have created a prototype framework for disambiguating entities that is based on connectedness. In this article, we demonstrate it in the practical application of disambiguating authors across a large set of bibliographic records. By representing knowledge from the records as edges in a graph between a subject and an object, we believe that the problem of disambiguating entities reduces to the problem of discovering the most strongly connected nodes in a graph. The knowledge from the records comes in many different forms, such as names of people, date of publication, and themes extracted from the text of the abstract. These different types of knowledge are fused to create the graph required for disambiguation. Furthermore, the resulting graph and framework can be used for more complex operations.
  5. Chaudhury, S.; Mallik, A.; Ghosh, H.: Multimedia ontology : representation and applications (2016) 0.02 
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    Abstract
    The book covers multimedia ontology in heritage preservation with intellectual explorations of various themes of Indian cultural heritage. The result of more than 15 years of collective research, Multimedia Ontology: Representation and Applications provides a theoretical foundation for understanding the nature of media data and the principles involved in its interpretation. The book presents a unified approach to recent advances in multimedia and explains how a multimedia ontology can fill the semantic gap between concepts and the media world. It relays real-life examples of implementations in different domains to illustrate how this gap can be filled. The book contains information that helps with building semantic, content-based search and retrieval engines and also with developing vertical application-specific search applications. It guides you in designing multimedia tools that aid in logical and conceptual organization of large amounts of multimedia data. As a practical demonstration, it showcases multimedia applications in cultural heritage preservation efforts and the creation of virtual museums. The book describes the limitations of existing ontology techniques in semantic multimedia data processing, as well as some open problems in the representations and applications of multimedia ontology. As an antidote, it introduces new ontology representation and reasoning schemes that overcome these limitations. The long, compiled efforts reflected in Multimedia Ontology: Representation and Applications are a signpost for new achievements and developments in efficiency and accessibility in the field.
  6. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.01 
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    Pages
    S.11-22
  7. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.01 
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    Date
    13.12.2017 14:17:22
  8. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.01 
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    Date
    26.12.2011 13:22:07
  9. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.01 
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    Date
    30. 5.2010 16:22:35
  10. 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
  11. 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
  12. 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
  13. OWL Web Ontology Language Test Cases (2004) 0.01 
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    Date
    14. 8.2011 13:33:22
  14. Giunchiglia, F.; Villafiorita, A.; Walsh, T.: Theories of abstraction (1997) 0.01 
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    Date
    1.10.2018 14:13:22
  15. 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
  16. Priss, U.: Faceted information representation (2000) 0.01 
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    Date
    22. 1.2016 17:47:06
  17. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.01 
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    Date
    11. 2.2011 18:22:58
  18. Deokattey, S.; Neelameghan, A.; Kumar, V.: ¬A method for developing a domain ontology : a case study for a multidisciplinary subject (2010) 0.01 
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  19. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.01 
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    11. 2.2011 18:22:25
  20. Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval 0.01 
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    Date
    12. 2.2011 17:35:22

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