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  • × theme_ss:"Begriffstheorie"
  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. Working with conceptual structures : contributions to ICCS 2000. 8th International Conference on Conceptual Structures: Logical, Linguistic, and Computational Issues. Darmstadt, August 14-18, 2000 (2000) 0.04 
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    Abstract
    The 8th International Conference on Conceptual Structures - Logical, Linguistic, and Computational Issues (ICCS 2000) brings together a wide range of researchers and practitioners working with conceptual structures. During the last few years, the ICCS conference series has considerably widened its scope on different kinds of conceptual structures, stimulating research across domain boundaries. We hope that this stimulation is further enhanced by ICCS 2000 joining the long tradition of conferences in Darmstadt with extensive, lively discussions. This volume consists of contributions presented at ICCS 2000, complementing the volume "Conceptual Structures: Logical, Linguistic, and Computational Issues" (B. Ganter, G.W. Mineau (Eds.), LNAI 1867, Springer, Berlin-Heidelberg 2000). It contains submissions reviewed by the program committee, and position papers. We wish to express our appreciation to all the authors of submitted papers, to the general chair, the program chair, the editorial board, the program committee, and to the additional reviewers for making ICCS 2000 a valuable contribution in the knowledge processing research field. Special thanks go to the local organizers for making the conference an enjoyable and inspiring event. We are grateful to Darmstadt University of Technology, the Ernst Schröder Center for Conceptual Knowledge Processing, the Center for Interdisciplinary Studies in Technology, the Deutsche Forschungsgemeinschaft, Land Hessen, and NaviCon GmbH for their generous support
    Content
    Concepts & Language: Knowledge organization by procedures of natural language processing. A case study using the method GABEK (J. Zelger, J. Gadner) - Computer aided narrative analysis using conceptual graphs (H. Schärfe, P. 0hrstrom) - Pragmatic representation of argumentative text: a challenge for the conceptual graph approach (H. Irandoust, B. Moulin) - Conceptual graphs as a knowledge representation core in a complex language learning environment (G. Angelova, A. Nenkova, S. Boycheva, T. Nikolov) - Conceptual Modeling and Ontologies: Relationships and actions in conceptual categories (Ch. Landauer, K.L. Bellman) - Concept approximations for formal concept analysis (J. Saquer, J.S. Deogun) - Faceted information representation (U. Priß) - Simple concept graphs with universal quantifiers (J. Tappe) - A framework for comparing methods for using or reusing multiple ontologies in an application (J. van ZyI, D. Corbett) - Designing task/method knowledge-based systems with conceptual graphs (M. Leclère, F.Trichet, Ch. Choquet) - A logical ontology (J. Farkas, J. Sarbo) - Algorithms and Tools: Fast concept analysis (Ch. Lindig) - A framework for conceptual graph unification (D. Corbett) - Visual CP representation of knowledge (H.D. Pfeiffer, R.T. Hartley) - Maximal isojoin for representing software textual specifications and detecting semantic anomalies (Th. Charnois) - Troika: using grids, lattices and graphs in knowledge acquisition (H.S. Delugach, B.E. Lampkin) - Open world theorem prover for conceptual graphs (J.E. Heaton, P. Kocura) - NetCare: a practical conceptual graphs software tool (S. Polovina, D. Strang) - CGWorld - a web based workbench for conceptual graphs management and applications (P. Dobrev, K. Toutanova) - Position papers: The edition project: Peirce's existential graphs (R. Mülller) - Mining association rules using formal concept analysis (N. Pasquier) - Contextual logic summary (R Wille) - Information channels and conceptual scaling (K.E. Wolff) - Spatial concepts - a rule exploration (S. Rudolph) - The TEXT-TO-ONTO learning environment (A. Mädche, St. Staab) - Controlling the semantics of metadata on audio-visual documents using ontologies (Th. Dechilly, B. Bachimont) - Building the ontological foundations of a terminology from natural language to conceptual graphs with Ribosome, a knowledge extraction system (Ch. Jacquelinet, A. Burgun) - CharGer: some lessons learned and new directions (H.S. Delugach) - Knowledge management using conceptual graphs (W.K. Pun)
  3. 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
  4. 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
  5. ¬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
  6. 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.
  7. 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
  8. 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
  9. Guarino, N.: Formal ontology, conceptual analysis and knowledge representation (1995) 0.01 
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    International journal of human-computer studies. 43(1995) nos.5/6, S.625-640
  10. 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.
  11. 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
  12. 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
  13. Dahlberg, I.: ¬Die gegenstandsbezogene, analytische Begriffstheorie und ihre Definitionsarten (1987) 0.01 
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    Pages
    S.9-22
  14. 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
  15. 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.
  16. 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)
  17. Conceptual structures : logical, linguistic, and computational issues. 8th International Conference on Conceptual Structures, ICCS 2000, Darmstadt, Germany, August 14-18, 2000 (2000) 0.01 
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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
  18. Seiler, T.B.: Begreifen und Verstehen (2001) 0.01 
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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.
  19. 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).
  20. Klix, F.: ¬Die Natur des Verstandes (1992) 0.00 
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    Content
    Kapitel 5: Die Dynamik des Verstandes 5.0. Erkenntnisprozesse in geistigen Vorgängen 5.1. Wechselwirkungen zwischen Begriffen und Operationen 5.2. Die Erkennung von Begriffsbeziehungen durch Vergleichsprozesse 5.3. Die Erkennung von Begriffsbeziehungen durch assoziative Anregungen 5.4. Ereignisbegriffe und die Stelligkeit von semantischen Relationen 5.5. Wechselwirkungen zwischen Wissensstrukturen 5.6. Über Einschlüsse von Emotionalität im Wissensbesitz und in mentalen Prozessen Kapitel 6: Verstandestätigkeit im Computer? 6.0. Computersimulation: Ein Irrweg oder Erkenntnismittel bei der Erforschung geistiger Vorgänge? 6.1. Computermodelle zur Wissensdeponierung und Wissensnutzung 6.2. Einige Probleme, die mit Spracherkennung zu tun haben 6.3. Was heißt Sprachverstehen und was bedeutet dann Computersimulation? Teil IV: Erkenntnis und Persönlichkeit Kapitel 7: Intelligenz, Begabung und Kreativität Kap. 8: An den Grenzen des menschlichen Verstandes

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