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  • × theme_ss:"Wissensrepräsentation"
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  1. Bringsjord, S.; Clark, M.; Taylor, J.: Sophisticated knowledge representation and reasoning requires philosophy (2014) 0.05
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    Abstract
    What is knowledge representation and reasoning (KR&R)? Alas, a thorough account would require a book, or at least a dedicated, full-length paper, but here we shall have to make do with something simpler. Since most readers are likely to have an intuitive grasp of the essence of KR&R, our simple account should suffice. The interesting thing is that this simple account itself makes reference to some of the foundational distinctions in the field of philosophy. These distinctions also play a central role in artificial intelligence (AI) and computer science. To begin with, the first distinction in KR&R is that we identify knowledge with knowledge that such-and-such holds (possibly to a degree), rather than knowing how. If you ask an expert tennis player how he manages to serve a ball at 130 miles per hour on his first serve, and then serve a safer, topspin serve on his second should the first be out, you may well receive a confession that, if truth be told, this athlete can't really tell you. He just does it; he does something he has been doing since his youth. Yet, there is no denying that he knows how to serve. In contrast, the knowledge in KR&R must be expressible in declarative statements. For example, our tennis player knows that if his first serve lands outside the service box, it's not in play. He thus knows a proposition, conditional in form.
    Date
    9. 2.2017 19:22:14
    Source
    Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss
  2. 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) 0.03
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  3. Eito-Brun, R.: Ontologies and the exchange of technical information : building a knowledge repository based on ECSS standards (2014) 0.02
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  4. Witte, R.; Krestel, R.; Kappler, T.; Lockemann, P.C.: Converting a historical architecture encyclopedia into a semantic knowledge base 0.02
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  5. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.02
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    Pages
    S.11-22
  6. Nielsen, M.: Neuronale Netze : Alpha Go - Computer lernen Intuition (2018) 0.02
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    Source
    Spektrum der Wissenschaft. 2018, H.1, S.22-27
  7. Weller, K.: Ontologien (2013) 0.01
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    Source
    Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
  8. 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
  9. Giunchiglia, F.; Villafiorita, A.; Walsh, T.: Theories of abstraction (1997) 0.01
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    Date
    1.10.2018 14:13:22
  10. 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
  11. Gayathri, R.; Uma, V.: Ontology based knowledge representation technique, domain modeling languages and planners for robotic path planning : a survey (2018) 0.01
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    Abstract
    Knowledge Representation and Reasoning (KR & R) has become one of the promising fields of Artificial Intelligence. KR is dedicated towards representing information about the domain that can be utilized in path planning. Ontology based knowledge representation and reasoning techniques provide sophisticated knowledge about the environment for processing tasks or methods. Ontology helps in representing the knowledge about environment, events and actions that help in path planning and making robots more autonomous. Knowledge reasoning techniques can infer new conclusion and thus aids planning dynamically in a non-deterministic environment. In the initial sections, the representation of knowledge using ontology and the techniques for reasoning that could contribute in path planning are discussed in detail. In the following section, we also provide comparison of various planning domain modeling languages, ontology editors, planners and robot simulation tools.
  12. Priss, U.: Faceted information representation (2000) 0.01
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    Date
    22. 1.2016 17:47:06
  13. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.01
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    Date
    11. 2.2011 18:22:58
  14. 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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    Date
    22. 7.2010 19:41:16
  15. Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (2010) 0.01
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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
  16. Madalli, D.P.; Balaji, B.P.; Sarangi, A.K.: Music domain analysis for building faceted ontological representation (2014) 0.01
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  17. Priss, U.: Faceted knowledge representation (1999) 0.01
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    Date
    22. 1.2016 17:30:31
  18. Pepper, S.; Arnaud, P.J.L.: Absolutely PHAB : toward a general model of associative relations (2020) 0.01
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    Abstract
    There have been many attempts at classifying the semantic modification relations (R) of N + N compounds but this work has not led to the acceptance of a definitive scheme, so that devising a reusable classification is a worthwhile aim. The scope of this undertaking is extended to other binominal lexemes, i.e. units that contain two thing-morphemes without explicitly stating R, like prepositional units, N + relational adjective units, etc. The 25-relation taxonomy of Bourque (2014) was tested against over 15,000 binominal lexemes from 106 languages and extended to a 29-relation scheme ("Bourque2") through the introduction of two new reversible relations. Bourque2 is then mapped onto Hatcher's (1960) four-relation scheme (extended by the addition of a fifth relation, similarity , as "Hatcher2"). This results in a two-tier system usable at different degrees of granularities. On account of its semantic proximity to compounding, metonymy is then taken into account, following Janda's (2011) suggestion that it plays a role in word formation; Peirsman and Geeraerts' (2006) inventory of 23 metonymic patterns is mapped onto Bourque2, confirming the identity of metonymic and binominal modification relations. Finally, Blank's (2003) and Koch's (2001) work on lexical semantics justifies the addition to the scheme of a third, superordinate level which comprises the three Aristotelean principles of similarity, contiguity and contrast.
  19. Harbig, D.; Schneider, R.: Ontology Learning im Rahmen von MyShelf (2006) 0.01
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  20. Fensel, D.; Staab, S.; Studer, R.; Harmelen, F. van; Davies, J.: ¬A future perspective : exploiting peer-to-peer and the Semantic Web for knowledge management (2004) 0.01
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Years

Languages

  • e 86
  • d 13