Search (43 results, page 1 of 3)

  • × theme_ss:"Begriffstheorie"
  1. Axelos, C.; Flasch, K.; Schepers, H.; Kuhlen, R.; Romberg, R.; Zimmermann, R.: Allgemeines/Besonderes (1971-2007) 0.07 
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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. Khoo, C.; Myaeng, S.H.: Identifying semantic relations in text for information retrieval and information extraction (2002) 0.06 
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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.
  3. Casagrande, J.B.; Hale, K.L.: Semantic relations in Papago folk definitions (1967) 0.05 
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    Footnote
    Zitiert in: Evens, M.: Thesaural relations in information retrieval. In: The semantics of relationships: an interdisciplinary perspective. Eds: R. Green u.a. Dordrecht: Kluwer 2002. S.143-160.
  4. Principles of semantic networks : explorations in the representation of knowledge (1991) 0.05 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  5. Evens, M.: Thesaural relations in information retrieval (2002) 0.05 
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    Abstract
    Thesaural relations have long been used in information retrieval to enrich queries; they have sometimes been used to cluster documents as well. Sometimes the first query to an information retrieval system yields no results at all, or, what can be even more disconcerting, many thousands of hits. One solution is to rephrase the query, improving the choice of query terms by using related terms of different types. A collection of related terms is often called a thesaurus. This chapter describes the lexical-semantic relations that have been used in building thesauri and summarizes some of the effects of using these relational thesauri in information retrieval experiments
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  6. Jouis, C.: Logic of relationships (2002) 0.05 
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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
  7. Storms, G.; VanMechelen, I.; DeBoeck, P.: Structural-analysis of the intension and extension of semantic concepts (1994) 0.04 
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    Abstract
    A method (HICLAS, DeBoeck & Rosenberg, 1988) for studying the internal structure of semantic concepts is presented. The proposed method reveals the internal structure of the extension as well as the intesion of a concept, together with a correspondence relation that shows the mutual dependence of both structures. Its use is illustrated with the analysis of simple concepts (e.g. sports) and conjunctive concepts (e.g. birds that are also pets). The underlying structure that is revealed can be interpreted as a differentiation of the simple concepts studied and for conjunctive concepts the proposed method is able to extract non-inherited and emergent features (Hampton, 1988)
    Date
    22. 7.2000 19:17:40
  8. Hovy, E.: Comparing sets of semantic relations in ontologies (2002) 0.04 
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    Abstract
    A set of semantic relations is created every time a domain modeler wants to solve some complex problem computationally. These relations are usually organized into ontologies. But three is little standardization of ontologies today, and almost no discussion an ways of comparing relations, of determining a general approach to creating relations, or of modeling in general. This chapter outlines an approach to establishing a general methodology for comparing and justifying sets of relations (and ontologies in general). It first provides several dozen characteristics of ontologies, organized into three taxonomies of increasingly detailed features, by which many essential characteristics of ontologies can be described. These features enable one to compare ontologies at a general level, without studying every concept they contain. But sometimes it is necessary to make detailed comparisons of content. The chapter then illustrates one method for determining salient points for comparison, using algorithms that semi-automatically identify similarities and differences between ontologies.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  9. ¬The semantics of relationships : an interdisciplinary perspective (2002) 0.04 
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    Abstract
    Work on relationships takes place in many communities, including, among others, data modeling, knowledge representation, natural language processing, linguistics, and information retrieval. Unfortunately, continued disciplinary splintering and specialization keeps any one person from being familiar with the full expanse of that work. By including contributions form experts in a variety of disciplines and backgrounds, this volume demonstrates both the parallels that inform work on relationships across a number of fields and the singular emphases that have yet to be fully embraced, The volume is organized into 3 parts: (1) Types of relationships (2) Relationships in knowledge representation and reasoning (3) Applications of relationships
    Content
    Enthält die Beiträge: Pt.1: Types of relationships: CRUDE, D.A.: Hyponymy and its varieties; FELLBAUM, C.: On the semantics of troponymy; PRIBBENOW, S.: Meronymic relationships: from classical mereology to complex part-whole relations; KHOO, C. u.a.: The many facets of cause-effect relation - Pt.2: Relationships in knowledge representation and reasoning: GREEN, R.: Internally-structured conceptual models in cognitive semantics; HOVY, E.: Comparing sets of semantic relations in ontologies; GUARINO, N., C. WELTY: Identity and subsumption; JOUIS; C.: Logic of relationships - Pt.3: Applications of relationships: EVENS, M.: Thesaural relations in information retrieval; KHOO, C., S.H. MYAENG: Identifying semantic relations in text for information retrieval and information extraction; McCRAY, A.T., O. BODENREICHER: A conceptual framework for the biiomedical domain; HETZLER, B.: Visual analysis and exploration of relationships
  10. Coltheart, V.; Evans, J.St.B.T.: ¬An investigation of semantic memory in individuals (1981) 0.03 
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  11. Hjoerland, B.: Concept theory (2009) 0.03 
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    Abstract
    Concept theory is an extremely broad, interdisciplinary and complex field of research related to many deep fields with very long historical traditions without much consensus. However, information science and knowledge organization cannot avoid relating to theories of concepts. Knowledge organizing systems (e.g., classification systems, thesauri, and ontologies) should be understood as systems basically organizing concepts and their semantic relations. The same is the case with information retrieval systems. Different theories of concepts have different implications for how to construe, evaluate, and use such systems. Based on a post-Kuhnian view of paradigms, this article put forward arguments that the best understanding and classification of theories of concepts is to view and classify them in accordance with epistemological theories (empiricism, rationalism, historicism, and pragmatism). It is also argued that the historicist and pragmatist understandings of concepts are the most fruitful views and that this understanding may be part of a broader paradigm shift that is also beginning to take place in information science. The importance of historicist and pragmatic theories of concepts for information science is outlined.
  12. Priß, U.: Relational concept analysis : semantic structures in dictionaries and lexical databases (1998) 0.02 
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  13. Stock, W.: Begriffe und semantische Relationen in der Wissensrepräsentation (2009) 0.02 
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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).
  14. Nelson, S.J.: From meaning to term : semantic locality in the UMLS metathesaurus (1992) 0.02 
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  15. 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.
  16. Gilreath, C.T.: Merons, taxons, and qualities : a taxonomy of aspects (1995) 0.02 
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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
  17. Fellbaum, C.: On the semantics of troponymy (2002) 0.02 
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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.
  18. 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) 0.02 
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    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)
  19. Bonnevie, E.: Dretske's semantic information theory and meta-theories in library and information science (2001) 0.02 
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    Abstract
    This article presents the semantic information theory, formulated by the philosopher Fred I. Dretske, as a contribution to the discussion of metatheories and their practical implications in the field of library and information science. Dretske's theory is described in Knowledge and the flow of information. It is founded on mathematical communication theory but developed and elaborated into a cognitive, functionalistic theory, is individually oriented, and deals with the content of information. The topics are: the information process from perception to cognition, and how concept formation takes place in terms of digitisation. Other important issues are the concepts of information and knowledge, truth and meaning. Semantic information theory can be used as a frame of reference in order to explain, clarify and refute concepts currently used in library and information science, and as the basis for critical reviews of elements of the cognitive viewpoint in IR, primarily the notion of "potential information". The main contribution of the theory lies in a clarification of concepts, but there are still problems regarding the practical applications. More research is needed to combine philosophical discussions with the practice of information and library science.
  20. Hjoerland, B.: Are relations in thesauri "context-free, definitional, and true in all possible worlds"? (2015) 0.02 
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    Abstract
    Much of the literature of information science and knowledge organization has accepted and built upon Elaine Svenonius's (2004) claim that "paradigmatic relationships are those that are context-free, definitional, and true in all possible worlds" (p. 583). At the same time, the literature demonstrates a common understanding that paradigmatic relations are the kinds of semantic relations used in thesauri and other knowledge organization systems (including equivalence relations, hierarchical relations, and associative relations). This understanding is problematic and harmful because it directs attention away from the empirical and contextual basis for knowledge-organizing systems. Whether A is a kind of X is certainly not context-free and definitional in empirical sciences or in much everyday information. Semantic relations are theory-dependent and, in biology, for example, a scientific revolution has taken place in which many relations have changed following the new taxonomic paradigm named "cladism." This biological example is not an exception, but the norm. Semantic relations including paradigmatic relations are not a priori but are dependent on subject knowledge, scientific findings, and paradigms. As long as information scientists and knowledge organizers isolate themselves from subject knowledge, knowledge organization cannot possibly progress.

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