Search (29 results, page 1 of 2)

  • × theme_ss:"Begriffstheorie"
  1. Axelos, C.; Flasch, K.; Schepers, H.; Kuhlen, R.; Romberg, R.; Zimmermann, R.: Allgemeines/Besonderes (1971-2007) 0.16 
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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. Gilreath, C.T.: Merons, taxons, and qualities : a taxonomy of aspects (1995) 0.07 
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    Abstract
    A new comprehensive taxonomy of all kinds of aspects (such as attribute, characteristic, feature, property and quality) is proposed, and concise, uniform names are suggested for the respective concepts. Based on this taxonomy, a new semantic network notation called ETA is briefly introduced
  3. McCray, A.T.; Bodenreider, O.: ¬A conceptual framework for the biomedical domain (2002) 0.02 
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    Abstract
    Specialized domains often come with an extensive terminology, suitable for storing and exchanging information, but not necessarily for knowledge processing. Knowledge structures such as semantic networks, or ontologies, are required to explore the semantics of a domain. The UMLS project at the National Library of Medicine is a research effort to develop knowledge-based resources for the biomedical domain. The Metathesaurus is a large body of knowledge that defines and inter-relates 730,000 biomedical concepts, and the Semantic Network defines the semantic principles that apply to this domain. This chapter presents these two knowledge sources and illustrates through a research study how they can collaborate to further structure the domain. The limits of the approach are discussed.
  4. Jouis, C.: Logic of relationships (2002) 0.01 
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    Abstract
    A main goal of recent studies in semantics is to integrate into conceptual structures the models of representation used in linguistics, logic, and/or artificial intelligence. A fundamental problem resides in the need to structure knowledge and then to check the validity of constructed representations. We propose associating logical properties with relationships by introducing the relationships into a typed and functional system of specifcations. This makes it possible to compare conceptual representations against the relationships established between the concepts. The mandatory condition to validate such a conceptual representation is consistency. The semantic system proposed is based an a structured set of semantic primitives-types, relations, and properties-based an a global model of language processing, Applicative and Cognitive Grammar (ACG) (Desc16s, 1990), and an extension of this model to terminology (Jouis & Mustafa 1995, 1996, 1997). The ACG postulates three levels of representation of languages, including a cognitive level. At this level, the meanings of lexical predicates are represented by semantic cognitive schemes. From this perspective, we propose a set of semantic concepts, which defines an organized system of meanings. Relations are part of a specification network based an a general terminological scheure (i.e., a coherent system of meanings of relations). In such a system, a specific relation may be characterized as to its: (1) functional type (the semantic type of arguments of the relation); (2) algebraic properties (reflexivity, symmetry, transitivity, etc.); and (3) combinatorial relations with other entities in the same context (for instance, the part of the text where a concept is defined).
    Date
    1.12.2002 11:12:22
  5. Dahlberg, I.: Zur Theorie des Begriffs (1974) 0.01 
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    Abstract
    A concept is regarded as the common element of both classification systems and thesauri. Reality and knowledge are not represented by words or terms but by the meanings "behind" these tokens. A concept of, say, an object, a property of an object, a process, etc. is derived from verbal statements on these as subjects and may therefore be defined as the whole of true and possible predicates that can be collected on a given subject. It is from these predicates that the characteristics of the corresponding concepts can be derived. Common characteristics in different concepts lead to relationsbetween concepts, which relations in turn are factors for the formation of concept systems. Different kinds of relationships as well as different kinds of concepts are distinguished. It is pointed out that an orderly supply of the elements for propositions (informative statements) on new knowledge requires the construction and availability of such concept systems
  6. 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
    Theme
    Information Resources Management
  7. Dahlberg, I.: Concept and definition theory (1989) 0.01 
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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
  8. 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
  9. Fellbaum, C.: On the semantics of troponymy (2002) 0.01 
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    Abstract
    The principal relation linking verbs in a semantic network is the manner relation (or "troponymy"). We examine the nature of troponymy across different semantic domains and verb classes in an attempt to arrive at a more subtle understanding of this intuitive relation. Troponymy is not a semantically homogeneous relation; rather, it is polysemous and encompasses distinct sub-relations. We identify and discuss Manner, Function, and Result. Furthermore, different kinds of troponyms differ from their semantically less elaborated superordinates in their syntactic behavior. In some cases, troponyms exhibit a wider range of syntactic altemations; in other cases, the troponyms are more restricted in their argument-projecting properties.
  10. ¬The role of formal ontology in the information technology (1995) 0.01 
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    Source
    International journal of human-computer studies. 43(1995) nos.5/6, S.623-965
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. Dahlberg, I.: ¬Die gegenstandsbezogene, analytische Begriffstheorie und ihre Definitionsarten (1987) 0.00 
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    Pages
    S.9-22
  17. Khoo, C.; Myaeng, S.H.: Identifying semantic relations in text for information retrieval and information extraction (2002) 0.00 
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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.
  18. Wüster, E.: Begriffs- und Themaklassifikation : Unterschiede in ihrem Wesen und in ihrer Anwendung (1971) 0.00 
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    Nachrichten für Dokumentation. 22(1971) H.3, S.98-104 (T.1); H.4, S.143-150 (T.2)
  19. Conceptual structures : logical, linguistic, and computational issues. 8th International Conference on Conceptual Structures, ICCS 2000, Darmstadt, Germany, August 14-18, 2000 (2000) 0.00 
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    Abstract
    Computer scientists create models of a perceived reality. Through AI techniques, these models aim at providing the basic support for emulating cognitive behavior such as reasoning and learning, which is one of the main goals of the Al research effort. Such computer models are formed through the interaction of various acquisition and inference mechanisms: perception, concept learning, conceptual clustering, hypothesis testing, probabilistic inference, etc., and are represented using different paradigms tightly linked to the processes that use them. Among these paradigms let us cite: biological models (neural nets, genetic programming), logic-based models (first-order logic, modal logic, rule-based systems), virtual reality models (object systems, agent systems), probabilistic models (Bayesian nets, fuzzy logic), linguistic models (conceptual dependency graphs, language-based rep resentations), etc. One of the strengths of the Conceptual Graph (CG) theory is its versatility in terms of the representation paradigms under which it falls. It can be viewed and therefore used, under different representation paradigms, which makes it a popular choice for a wealth of applications. Its full coupling with different cognitive processes lead to the opening of the field toward related research communities such as the Description Logic, Formal Concept Analysis, and Computational Linguistic communities. We now see more and more research results from one community enrich the other, laying the foundations of common philosophical grounds from which a successful synergy can emerge. ICCS 2000 embodies this spirit of research collaboration. It presents a set of papers that we believe, by their exposure, will benefit the whole community. For instance, the technical program proposes tracks on Conceptual Ontologies, Language, Formal Concept Analysis, Computational Aspects of Conceptual Structures, and Formal Semantics, with some papers on pragmatism and human related aspects of computing. Never before was the program of ICCS formed by so heterogeneously rooted theories of knowledge representation and use. We hope that this swirl of ideas will benefit you as much as it already has benefited us while putting together this program
    Series
    Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
  20. Seiler, T.B.: Begreifen und Verstehen (2001) 0.00 
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    Abstract
    Wissen ist wichtig. Heutzutage sind es gerade Wirtschaftsunternehmen, die erkannt haben, dass sie auf Kenntnisse und Bildung ihrer Mitarbeiter nicht verzichten können. Wissen tritt gleichberechtigt an die Seite von Arbeit und Kapital Gemeinsam bilden sie das Fundament für moderne Industrieunternehmen. Aber was ist eigentlich Wissen? Wie wird Wissen erworben und weitergegeben? Dies sind Fragen, auf die schon viele sehr unterschiedliche Antworten gegeben worden sind. Scheinbar selbstverständliche Vorgänge, wie Verstehen und Erkennen berühren in Wahrheit die Grundlagen unseres Denkens, und wie Denken eigenlich vor sich geht; ist trotz aller Erklärungsversuche der Biochemiker nicht zufrieden stellend beantwortet. Der Psychologe Thomas Bernhard Seiler lässt denn auch in seinem Buch "Begreifen und Verstehen" die biologischen Modelle außen vor. Er geht davon aus, dass Verstehen der Vorgang des Erkennens ist. 'Erkennen' aber in eine Vielzahl von einzelnen Prozessen zerfällt. Die Stücke und Einheiten, aus denen der Erkenntnisvorgang besteht, nennt Seiler "Begriffe". Wissen besteht demnach aus Begriffen. "Begriff" ist sein zentraler Begriff, und an diesem Satz wird deutlich, wie schwierig das Terrain ist, auf dem Seiler sich bewegt, denn die Erklärung solcher Worte wie "Begriff" enthält oft das zu erklärende Wort selbst. Er meistert diese Aufgabe in bewundernswert klarer und verständlicher Sprache, wobei sein Buch aber durchaus nicht einfach zu lesen ist - konzentriertes Mitdenken ist gefordert, wenn Seller seine Leser von überschaubaren ersten Definitionen zum Zeichencharakter von Sprache und dann zu den Begriffstheorien der Philosophie und Psychologie führt. Populärwissenschaft ist das nicht, wohl aber Wissenschaft für Leute mit solider Schulbildung. Trotz aller Theorie stellt Seiler auch immer wieder den Menschen in den Mittelpunkt und macht deutlich, dass dieser eben nicht programmierbar Ist wie ein Computer. Begriffsbildung, also die Aneignung von Wissen, ist in Wahrheit höchst komplex und sehr individuell.

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