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  1. Dahlberg, I.: Concept and definition theory (1989) 0.02
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    Source
    Classification theory in the computer age: conversations across the disciplines. Proc. from the Conf. 18.-19.11.1988, Albany, NY
  2. Nelson, S.J.: From meaning to term : semantic locality in the UMLS metathesaurus (1992) 0.01
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    Source
    Assessing the value of medical informatics: Proc. of the 15th Annual Symposium on Computer Applications in Medical Care, Washington, DC, Nov.1991
  3. 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.
  4. 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
  5. 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
  6. 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.
  7. 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
  8. Harras, G.: Concepts in linguistics : concepts in natural language (2000) 0.01
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    Series
    Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
  9. Gerbé, O.; Mineau, G.W.; Keller, R.K.: Conceptual graphs, metamodelling, and notation of concepts : fundamental issues (2000) 0.01
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    Series
    Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
  10. 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.
  11. 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
  12. Jouis, C.: Logic of relationships (2002) 0.00
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    Date
    1.12.2002 11:12:22
  13. Marradi, A.: ¬The concept of concept : concepts and terms (2012) 0.00
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    Date
    22. 1.2012 13:11:25
  14. Besler, G.; Szulc, J.: Gottlob Frege's theory of definition as useful tool for knowledge organization : definition of 'context' - case study (2014) 0.00
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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
  15. Olson, H.A.: How we construct subjects : a feminist analysis (2007) 0.00
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    Date
    11.12.2019 19:00:22