Search (22 results, page 1 of 2)

  • × theme_ss:"Begriffstheorie"
  • × type_ss:"a"
  1. Axelos, C.; Flasch, K.; Schepers, H.; Kuhlen, R.; Romberg, R.; Zimmermann, R.: Allgemeines/Besonderes (1971-2007) 0.06 
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    Footnote
    DOI: 10.24894/HWPh.5033. Vgl. unter: https://www.schwabeonline.ch/schwabe-xaveropp/elibrary/start.xav#__elibrary__%2F%2F*%5B%40attr_id%3D%27verw.allgemeinesbesonderes%27%5D__1515856414979.
  2. Dahlberg, I.: Concept and definition theory (1989) 0.02 
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    Classification theory in the computer age: conversations across the disciplines. Proc. from the Conf. 18.-19.11.1988, Albany, NY
  3. Nelson, S.J.: From meaning to term : semantic locality in the UMLS metathesaurus (1992) 0.01 
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    Assessing the value of medical informatics: Proc. of the 15th Annual Symposium on Computer Applications in Medical Care, Washington, DC, Nov.1991
  4. Khoo, C.; Chan, S.; Niu, Y.: ¬The many facets of the cause-effect relation (2002) 0.01 
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    Abstract
    This chapter presents a broad survey of the cause-effect relation, with particular emphasis an how the relation is expressed in text. Philosophers have been grappling with the concept of causation for centuries. Researchers in social psychology have found that the human mind has a very complex mechanism for identifying and attributing the cause for an event. Inferring cause-effect relations between events and statements has also been found to be an important part of reading and text comprehension, especially for narrative text. Though many of the cause-effect relations in text are implied and have to be inferred by the reader, there is also a wide variety of linguistic expressions for explicitly indicating cause and effect. In addition, it has been found that certain words have "causal valence"-they bias the reader to attribute cause in certain ways. Cause-effect relations can also be divided into several different types.
  5. Klein, W.: Organisation des Wissens durch Sprache : Konsequenzen für die maschinelle Sprachanalyse (1977) 0.01 
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    Abstract
    Das Wissen, das sich die Menschen zu einer bestimmten Zeit erworben haben glauben, wird weiterhin mit Hilfe der natürlichen Sprache festgehalten ("kodifiziert") und weitervermittelt. Zu diesem in natürlich-sprachlichen Äußerungen kodifizierten Wissen hat man jedoch mit einem Computer kaum direkten Zugang. Zwar bemüht man sich seit vielen Jahren mit zum Teil erheblichem Aufwand um beispielsweise automatische Informationserschließung, maschinelle Sprachübersetzung und Mensch-Maschine-Dialoge in natürlicher Sprache, aber die Ergebnisse sind bescheiden. Verantwortlich für den in diesen Bereichen vergleichsweise geringen Erfolg sind verschiedene Eigenschaften der natürlichen Sprachen, die - im Gegensatz zu formalen Sprachen (wie Programmiersprachen, gängige logische Sprachen) - die maschinelle Informationserschließung erschweren
  6. Sowa, J.F.: Top-level ontological categories (1995) 0.01 
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    Source
    International journal of human-computer studies. 43(1995) nos.5/6, S.669-685
  7. Guarino, N.: Formal ontology, conceptual analysis and knowledge representation (1995) 0.01 
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    Source
    International journal of human-computer studies. 43(1995) nos.5/6, S.625-640
  8. Barsalou, L.W.: Frames, concepts, and conceptual fields (1992) 0.01 
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    Abstract
    In this chapter I propose that frames provide the fundamental representation of knowledge in human cognition. In the first section, I raise problems with the feature list representations often found in theories of knowledge, and I sketch the solutions that frames provide to them. In the second section, I examine the three fundamental concepts of frames: attribute-value sets, structural invariants, and constraints. Because frames also represents the attributes, values, structural invariants, and constraints within a frame, the mechanism that constructs frames builds them recursively. The frame theory I propose borrows heavily from previous frame theories, although its collection of representational components is somewhat unique. Furthermore, frame theorists generally assume that frames are rigid configurations of independent attributes, whereas I propose that frames are dynamic relational structures whose form is flexible and context dependent. In the third section, I illustrate how frames support a wide variety of representational tasks central to conceptual processing in natural and artificial intelligence. Frames can represent exemplars and propositions, prototypes and membership, subordinates and taxonomies. Frames can also represent conceptual combinations, event sequences, rules, and plans. In the fourth section, I show how frames define the extent of conceptual fields and how they provide a powerful productive mechanism for generating specific concepts within a field.
  9. Sowa, J.F.: Ontology, metadata, and semiotics (2000) 0.01 
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    Abstract
    The Internet is a giant semiotic system. It is a massive collection of Peirce's three kinds of signs: icons, which show the form of something; indices, which point to something; and symbols, which represent something according to some convention. But current proposals for ontologies and metadata have overlooked some of the most important features of signs. A sign has three aspects: it is (1) an entity that represents (2) another entity to (3) an agent. By looking only at the signs themselves, some metadata proposals have lost sight of the entities they represent and the agents - human, animal, or robot - which interpret them. With its three branches of syntax, semantics, and pragmatics, semiotics provides guidelines for organizing and using signs to represent something to someone for some purpose. Besides representation, semiotics also supports methods for translating patterns of signs intended for one purpose to other patterns intended for different but related purposes. This article shows how the fundamental semiotic primitives are represented in semantically equivalent notations for logic, including controlled natural languages and various computer languages
    Series
    Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
  10. Harras, G.: Concepts in linguistics : concepts in natural language (2000) 0.01 
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    Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
  11. Dahlberg, I.: ¬Die gegenstandsbezogene, analytische Begriffstheorie und ihre Definitionsarten (1987) 0.01 
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    Pages
    S.9-22
  12. Gerbé, O.; Mineau, G.W.; Keller, R.K.: Conceptual graphs, metamodelling, and notation of concepts : fundamental issues (2000) 0.01 
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    Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
  13. Khoo, C.; Myaeng, S.H.: Identifying semantic relations in text for information retrieval and information extraction (2002) 0.01 
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    Abstract
    Automatic identification of semantic relations in text is a difficult problem, but is important for many applications. It has been used for relation matching in information retrieval to retrieve documents that contain not only the concepts but also the relations between concepts specified in the user's query. It is an integral part of information extraction-extracting from natural language text, facts or pieces of information related to a particular event or topic. Other potential applications are in the construction of relational thesauri (semantic networks of related concepts) and other kinds of knowledge bases, and in natural language processing applications such as machine translation and computer comprehension of text. This chapter examines the main methods used for identifying semantic relations automatically and their application in information retrieval and information extraction.
  14. Wüster, E.: Begriffs- und Themaklassifikation : Unterschiede in ihrem Wesen und in ihrer Anwendung (1971) 0.01 
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    Nachrichten für Dokumentation. 22(1971) H.3, S.98-104 (T.1); H.4, S.143-150 (T.2)
  15. Stock, W.: Begriffe und semantische Relationen in der Wissensrepräsentation (2009) 0.01 
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    Abstract
    Begriffsorientiertes Information Retrieval bedarf einer informationswissenschaftlichen Theorie der Begriffe sowie der semantischen Relationen. Ein Begriff wird durch seine Intension und Extension sowie durch Definitionen bestimmt. Dem Problem der Vagheit begegnen wir durch die Einführung von Prototypen. Wichtige Definitionsarten sind die Begriffserklärung (nach Aristoteles) und die Definition über Familienähnlichkeiten (im Sinne Wittgensteins). Wir modellieren Begriffe als Frames (in der Version von Barsalou). Die zentrale paradigmatische Relation in Wissensordnungen ist die Hierarchie, die in verschiedene Arten zu gliedern ist: Hyponymie zerfällt in die Taxonomie und die einfache Hyponymie, Meronymie in eine ganze Reihe unterschiedlicher Teil-Ganzes-Beziehungen. Wichtig für praktische Anwendungen ist die Transitivität der jeweiligen Relation. Eine unspezifische Assoziationsrelation ist bei den angepeilten Anwendungen wenig hilfreich und wird durch ein Bündel von generalisierbaren und fachspezifischen Relationen ersetzt. Unser Ansatz fundiert neue Optionen der Anwendung von Wissensordnungen in der Informationspraxis neben ihrem "klassischen" Einsatz beim Information Retrieval: Erweiterung von Suchanfragen (Anwendung der semantischen Nähe), automatisches Schlussfolgern (Anwendung der terminologischen Logik in Vorbereitung eines semantischen Web) und automatische Berechnungen (bei Funktionalbegriffen mit numerischen Wertangaben).
  16. Dahlberg, I.: Begriffsarbeit in der Wissensorganisation (2010) 0.00 
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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
  17. Storms, G.; VanMechelen, I.; DeBoeck, P.: Structural-analysis of the intension and extension of semantic concepts (1994) 0.00 
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    Date
    22. 7.2000 19:17:40
  18. Bauer, G.: ¬Die vielseitigen Anwendungsmöglichkeiten des Kategorienprinzips bei der Wissensorganisation (2006) 0.00 
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    Pages
    S.22-33
  19. Jouis, C.: Logic of relationships (2002) 0.00 
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    Date
    1.12.2002 11:12:22
  20. Marradi, A.: ¬The concept of concept : concepts and terms (2012) 0.00 
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    Date
    22. 1.2012 13:11:25

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  • d 7

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