Search (45 results, page 2 of 3)

  • × language_ss:"e"
  • × theme_ss:"Formale Begriffsanalyse"
  1. Wille, R.: Knowledge acquisition by methods of formal concept analysis (1989) 0.02 
    0.015105689 = product of:
  0.055387523 = sum of:
    0.005467103 = weight(_text_:a in 594) [ClassicSimilarity], result of:
      0.005467103 = score(doc=594,freq=2.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.17835285 = fieldWeight in 594, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.109375 = fieldNorm(doc=594)
    0.045059573 = weight(_text_:r in 594) [ClassicSimilarity], result of:
      0.045059573 = score(doc=594,freq=2.0), product of:
        0.088001914 = queryWeight, product of:
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.026584605 = queryNorm
        0.51202947 = fieldWeight in 594, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.109375 = fieldNorm(doc=594)
    0.0048608496 = weight(_text_:s in 594) [ClassicSimilarity], result of:
      0.0048608496 = score(doc=594,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.16817348 = fieldWeight in 594, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.109375 = fieldNorm(doc=594)
  0.27272728 = coord(3/11)
    Pages
    S.365-380
    Type
    a
  2. Priss, U.: Formal concept analysis in information science (2006) 0.01 
    0.010297547 = product of:
  0.056636505 = sum of:
    0.0062481174 = weight(_text_:a in 4305) [ClassicSimilarity], result of:
      0.0062481174 = score(doc=4305,freq=2.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.20383182 = fieldWeight in 4305, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.125 = fieldNorm(doc=4305)
    0.05038839 = weight(_text_:u in 4305) [ClassicSimilarity], result of:
      0.05038839 = score(doc=4305,freq=2.0), product of:
        0.08704981 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.026584605 = queryNorm
        0.57884544 = fieldWeight in 4305, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.125 = fieldNorm(doc=4305)
  0.18181819 = coord(2/11)
    Type
    a
  3. Lex, W.: ¬A representation of concepts for their computerization (1987) 0.01 
    0.009866811 = product of:
  0.036178306 = sum of:
    0.0076523502 = weight(_text_:a in 618) [ClassicSimilarity], result of:
      0.0076523502 = score(doc=618,freq=12.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.24964198 = fieldWeight in 618, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=618)
    0.025748327 = weight(_text_:r in 618) [ClassicSimilarity], result of:
      0.025748327 = score(doc=618,freq=2.0), product of:
        0.088001914 = queryWeight, product of:
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.026584605 = queryNorm
        0.29258826 = fieldWeight in 618, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.0625 = fieldNorm(doc=618)
    0.0027776284 = weight(_text_:s in 618) [ClassicSimilarity], result of:
      0.0027776284 = score(doc=618,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.09609913 = fieldWeight in 618, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0625 = fieldNorm(doc=618)
  0.27272728 = coord(3/11)
    Abstract
    A lattice theoretical description of concept hierarchies is developed using for attributes the terms "given", "negated", "open" and "impossible" as the truth-values of a four-valued logic. Similar to the theory of B. Ganter and R. Wille so does this framework permit a precise representation of the usual interdependences in a field of related concepts - such as superconcepts, subconcept, contrary concepts etc. -, whenever the concepts under consideration can be sufficiently described by the presence or absence of certain attributes ...
    Source
    International classification. 14(1987), S.127-132
    Type
    a
  4. Eklund, P.; Groh, B.; Stumme, G.; Wille, R.: ¬A conceptual-logic extension of TOSCANA (2000) 0.01 
    0.009123195 = product of:
  0.033451714 = sum of:
    0.0040582716 = weight(_text_:a in 5082) [ClassicSimilarity], result of:
      0.0040582716 = score(doc=5082,freq=6.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.13239266 = fieldWeight in 5082, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=5082)
    0.027310224 = weight(_text_:r in 5082) [ClassicSimilarity], result of:
      0.027310224 = score(doc=5082,freq=4.0), product of:
        0.088001914 = queryWeight, product of:
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.026584605 = queryNorm
        0.3103367 = fieldWeight in 5082, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.046875 = fieldNorm(doc=5082)
    0.0020832212 = weight(_text_:s in 5082) [ClassicSimilarity], result of:
      0.0020832212 = score(doc=5082,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.072074346 = fieldWeight in 5082, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=5082)
  0.27272728 = coord(3/11)
    Abstract
    The aim of this paper is to indicate how TOSCANA may be extended to allow graphical representations not only of concept lattices but also of concept graphs in the sense of Contextual Logic. The contextual- logic extension of TOSCANA requires the logical scaling of conceptual and relational scales for which we propose the Peircean Algebraic Logic as reconstructed by R. W. Burch. As graphical representations we recommend, besides labelled line diagrams of concept lattices and Sowa's diagrams of conceptual graphs, particular information maps for utilizing background knowledge as much as possible. Our considerations are illustrated by a small information system about the domestic flights in Austria
    Pages
    S.453-467
    Type
    a
  5. Priss, U.: Comparing classification systems using facets (2000) 0.01 
    0.009104414 = product of:
  0.03338285 = sum of:
    0.005411029 = weight(_text_:a in 6485) [ClassicSimilarity], result of:
      0.005411029 = score(doc=6485,freq=6.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.17652355 = fieldWeight in 6485, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=6485)
    0.0027776284 = weight(_text_:s in 6485) [ClassicSimilarity], result of:
      0.0027776284 = score(doc=6485,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.09609913 = fieldWeight in 6485, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0625 = fieldNorm(doc=6485)
    0.025194194 = weight(_text_:u in 6485) [ClassicSimilarity], result of:
      0.025194194 = score(doc=6485,freq=2.0), product of:
        0.08704981 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.026584605 = queryNorm
        0.28942272 = fieldWeight in 6485, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.0625 = fieldNorm(doc=6485)
  0.27272728 = coord(3/11)
    Abstract
    This paper describes a qualitative methodology for comparing and analyzing classification schemes. Theoretical facets are modeled as concept lattices in the sense of formal concept analysis and are used as 'ground' on which the underlying conceptual facets of a classification scheme are visually represented as 'figures'.
    Pages
    S.170-175
    Type
    a
  6. Priss, U.: Lattice-based information retrieval (2000) 0.01 
    0.008775782 = product of:
  0.032177866 = sum of:
    0.006695806 = weight(_text_:a in 6055) [ClassicSimilarity], result of:
      0.006695806 = score(doc=6055,freq=12.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.21843673 = fieldWeight in 6055, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6055)
    0.00343714 = weight(_text_:s in 6055) [ClassicSimilarity], result of:
      0.00343714 = score(doc=6055,freq=4.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.118916616 = fieldWeight in 6055, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6055)
    0.02204492 = weight(_text_:u in 6055) [ClassicSimilarity], result of:
      0.02204492 = score(doc=6055,freq=2.0), product of:
        0.08704981 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.026584605 = queryNorm
        0.25324488 = fieldWeight in 6055, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6055)
  0.27272728 = coord(3/11)
    Abstract
    A lattice-based model for information retrieval was suggested in the 1960's but has been seen as a theoretical possibility hard to practically apply ever since. This paper attempts to revive the lattice model and demonstrate its applicability in an information retrieval system, FalR, that incorporates a graphical representation of a faceted thesaurus. It shows how Boolean queries can be lattice-theoretically related to the concepts of the thesaurus and visualized within the thesaurus display. An advantage of FaIR is that it allows for a high level of transparency of the system, which can be controlled by the user
    Source
    Knowledge organization. 27(2000) no.3, S.132-142
    Type
    a
  7. Priss, U.: ¬A graphical interface for conceptually navigating faceted thesauri (1998) 0.01 
    0.008501223 = product of:
  0.03117115 = sum of:
    0.006695806 = weight(_text_:a in 6658) [ClassicSimilarity], result of:
      0.006695806 = score(doc=6658,freq=12.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.21843673 = fieldWeight in 6658, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6658)
    0.0024304248 = weight(_text_:s in 6658) [ClassicSimilarity], result of:
      0.0024304248 = score(doc=6658,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.08408674 = fieldWeight in 6658, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6658)
    0.02204492 = weight(_text_:u in 6658) [ClassicSimilarity], result of:
      0.02204492 = score(doc=6658,freq=2.0), product of:
        0.08704981 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.026584605 = queryNorm
        0.25324488 = fieldWeight in 6658, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6658)
  0.27272728 = coord(3/11)
    Abstract
    This paper describes a graphical interface for the navigation and construction of faceted thesauri that is based on formal concept analysis. Each facet of a thesaurus is represented as a mathematical lattice that is further subdivided into components. Users can graphically navigate through the Java implementation of the interface by clicking on terms that connect facets and components. Since there are many applications for thesauri in the knowledge representation field, such a graphical interface has the potential of being very useful
    Pages
    S.184-190
    Type
    a
  8. Priss, U.; Old, L.J.: Concept neighbourhoods in knowledge organisation systems (2010) 0.01 
    0.008480695 = product of:
  0.031095881 = sum of:
    0.0031240587 = weight(_text_:a in 3527) [ClassicSimilarity], result of:
      0.0031240587 = score(doc=3527,freq=2.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.10191591 = fieldWeight in 3527, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=3527)
    0.0027776284 = weight(_text_:s in 3527) [ClassicSimilarity], result of:
      0.0027776284 = score(doc=3527,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.09609913 = fieldWeight in 3527, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0625 = fieldNorm(doc=3527)
    0.025194194 = weight(_text_:u in 3527) [ClassicSimilarity], result of:
      0.025194194 = score(doc=3527,freq=2.0), product of:
        0.08704981 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.026584605 = queryNorm
        0.28942272 = fieldWeight in 3527, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.0625 = fieldNorm(doc=3527)
  0.27272728 = coord(3/11)
    Pages
    S.165-170
    Type
    a
  9. Conceptual structures : logical, linguistic, and computational issues. 8th International Conference on Conceptual Structures, ICCS 2000, Darmstadt, Germany, August 14-18, 2000 (2000) 0.01 
    0.007143808 = product of:
  0.019645471 = sum of:
    0.0027194852 = product of:
      0.0054389704 = sum of:
        0.0054389704 = weight(_text_:h in 691) [ClassicSimilarity], result of:
          0.0054389704 = score(doc=691,freq=2.0), product of:
            0.0660481 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.026584605 = queryNorm
            0.08234863 = fieldWeight in 691, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0234375 = fieldNorm(doc=691)
      0.5 = coord(1/2)
    0.004223983 = weight(_text_:a in 691) [ClassicSimilarity], result of:
      0.004223983 = score(doc=691,freq=26.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.13779864 = fieldWeight in 691, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0234375 = fieldNorm(doc=691)
    0.00147306 = weight(_text_:s in 691) [ClassicSimilarity], result of:
      0.00147306 = score(doc=691,freq=4.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.050964262 = fieldWeight in 691, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0234375 = fieldNorm(doc=691)
    0.011228944 = weight(_text_:k in 691) [ClassicSimilarity], result of:
      0.011228944 = score(doc=691,freq=2.0), product of:
        0.09490114 = queryWeight, product of:
          3.569778 = idf(docFreq=3384, maxDocs=44218)
          0.026584605 = queryNorm
        0.118322544 = fieldWeight in 691, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.569778 = idf(docFreq=3384, maxDocs=44218)
          0.0234375 = fieldNorm(doc=691)
  0.36363637 = coord(4/11)
    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
    Content
    Concepts and Language: The Role of Conceptual Structure in Human Evolution (Keith Devlin) - Concepts in Linguistics - Concepts in Natural Language (Gisela Harras) - Patterns, Schemata, and Types: Author Support through Formalized Experience (Felix H. Gatzemeier) - Conventions and Notations for Knowledge Representation and Retrieval (Philippe Martin) - Conceptual Ontology: Ontology, Metadata, and Semiotics (John F. Sowa) - Pragmatically Yours (Mary Keeler) - Conceptual Modeling for Distributed Ontology Environments (Deborah L. McGuinness) - Discovery of Class Relations in Exception Structured Knowledge Bases (Hendra Suryanto, Paul Compton) - Conceptual Graphs: Perspectives: CGs Applications: Where Are We 7 Years after the First ICCS ? (Michel Chein, David Genest) - The Engineering of a CC-Based System: Fundamental Issues (Guy W. Mineau) - Conceptual Graphs, Metamodeling, and Notation of Concepts (Olivier Gerbé, Guy W. Mineau, Rudolf K. Keller) - Knowledge Representation and Reasonings: Based on Graph Homomorphism (Marie-Laure Mugnier) - User Modeling Using Conceptual Graphs for Intelligent Agents (James F. Baldwin, Trevor P. Martin, Aimilia Tzanavari) - Towards a Unified Querying System of Both Structured and Semi-structured Imprecise Data Using Fuzzy View (Patrice Buche, Ollivier Haemmerlé) - Formal Semantics of Conceptual Structures: The Extensional Semantics of the Conceptual Graph Formalism (Guy W. Mineau) - Semantics of Attribute Relations in Conceptual Graphs (Pavel Kocura) - Nested Concept Graphs and Triadic Power Context Families (Susanne Prediger) - Negations in Simple Concept Graphs (Frithjof Dau) - Extending the CG Model by Simulations (Jean-François Baget) - Contextual Logic and Formal Concept Analysis: Building and Structuring Description Logic Knowledge Bases: Using Least Common Subsumers and Concept Analysis (Franz Baader, Ralf Molitor) - On the Contextual Logic of Ordinal Data (Silke Pollandt, Rudolf Wille) - Boolean Concept Logic (Rudolf Wille) - Lattices of Triadic Concept Graphs (Bernd Groh, Rudolf Wille) - Formalizing Hypotheses with Concepts (Bernhard Ganter, Sergei 0. Kuznetsov) - Generalized Formal Concept Analysis (Laurent Chaudron, Nicolas Maille) - A Logical Generalization of Formal Concept Analysis (Sébastien Ferré, Olivier Ridoux) - On the Treatment of Incomplete Knowledge in Formal Concept Analysis (Peter Burmeister, Richard Holzer) - Conceptual Structures in Practice: Logic-Based Networks: Concept Graphs and Conceptual Structures (Peter W. Eklund) - Conceptual Knowledge Discovery and Data Analysis (Joachim Hereth, Gerd Stumme, Rudolf Wille, Uta Wille) - CEM - A Conceptual Email Manager (Richard Cole, Gerd Stumme) - A Contextual-Logic Extension of TOSCANA (Peter Eklund, Bernd Groh, Gerd Stumme, Rudolf Wille) - A Conceptual Graph Model for W3C Resource Description Framework (Olivier Corby, Rose Dieng, Cédric Hébert) - Computational Aspects of Conceptual Structures: Computing with Conceptual Structures (Bernhard Ganter) - Symmetry and the Computation of Conceptual Structures (Robert Levinson) An Introduction to SNePS 3 (Stuart C. Shapiro) - Composition Norm Dynamics Calculation with Conceptual Graphs (Aldo de Moor) - From PROLOG++ to PROLOG+CG: A CG Object-Oriented Logic Programming Language (Adil Kabbaj, Martin Janta-Polczynski) - A Cost-Bounded Algorithm to Control Events Generalization (Gaël de Chalendar, Brigitte Grau, Olivier Ferret)
    Pages
    XI,568 S
    Type
    s
  10. Burmeister, P.; Holzer, R.: On the treatment of incomplete knowledge in formal concept analysis (2000) 0.01 
    0.007112879 = product of:
  0.026080554 = sum of:
    0.0046860883 = weight(_text_:a in 5085) [ClassicSimilarity], result of:
      0.0046860883 = score(doc=5085,freq=8.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.15287387 = fieldWeight in 5085, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=5085)
    0.019311246 = weight(_text_:r in 5085) [ClassicSimilarity], result of:
      0.019311246 = score(doc=5085,freq=2.0), product of:
        0.088001914 = queryWeight, product of:
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.026584605 = queryNorm
        0.2194412 = fieldWeight in 5085, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.046875 = fieldNorm(doc=5085)
    0.0020832212 = weight(_text_:s in 5085) [ClassicSimilarity], result of:
      0.0020832212 = score(doc=5085,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.072074346 = fieldWeight in 5085, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=5085)
  0.27272728 = coord(3/11)
    Abstract
    Some possible treatments of incomplete knowledge in conceptual data representation, data analysis and knowledge acquisition are presented. In particular, some ways of conceptual scalings as well as the role of the three-valued KLEENE-logic are briefly investigated. This logic is also one background in attribute exploration, a conceptual tool for knowledge acquisition. For this method a strategy is given to obtain as much of (attribute) implicational knowledge about a given "universe" as possible; and we show how to represent incomplete knowledge in order to be able to pin down the questions still to be answered in order to obtain complete knowledge in this situation
    Pages
    S.385-398
    Type
    a
  11. Kaytoue, M.; Kuznetsov, S.O.; Assaghir, Z.; Napoli, A.: Embedding tolerance relations in concept lattices : an application in information fusion (2010) 0.01 
    0.006782371 = product of:
  0.024868693 = sum of:
    0.0070399716 = weight(_text_:a in 4843) [ClassicSimilarity], result of:
      0.0070399716 = score(doc=4843,freq=26.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.22966442 = fieldWeight in 4843, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4843)
    0.016092705 = weight(_text_:r in 4843) [ClassicSimilarity], result of:
      0.016092705 = score(doc=4843,freq=2.0), product of:
        0.088001914 = queryWeight, product of:
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.026584605 = queryNorm
        0.18286766 = fieldWeight in 4843, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4843)
    0.0017360178 = weight(_text_:s in 4843) [ClassicSimilarity], result of:
      0.0017360178 = score(doc=4843,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.060061958 = fieldWeight in 4843, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4843)
  0.27272728 = coord(3/11)
    Abstract
    Formal Concept Analysis (FCA) is a well founded mathematical framework used for conceptual classication and knowledge management. Given a binary table describing a relation between objects and attributes, FCA consists in building a set of concepts organized by a subsumption relation within a concept lattice. Accordingly, FCA requires to transform complex data, e.g. numbers, intervals, graphs, into binary data leading to loss of information and poor interpretability of object classes. In this paper, we propose a pre-processing method producing binary data from complex data taking advantage of similarity between objects. As a result, the concept lattice is composed of classes being maximal sets of pairwise similar objects. This method is based on FCA and on a formalization of similarity as a tolerance relation (reexive and symmetric). It applies to complex object descriptions and especially here to interval data. Moreover, it can be applied to any kind of structured data for which a similarity can be dened (sequences, graphs, etc.). Finally, an application highlights that the resulting concept lattice plays an important role in information fusion problem, as illustrated with a real-world example in agronomy.
    Pages
    23 S
    Type
    r
  12. Negm, E.; AbdelRahman, S.; Bahgat, R.: PREFCA: a portal retrieval engine based on formal concept analysis (2017) 0.01 
    0.0054597026 = product of:
  0.02001891 = sum of:
    0.0051806658 = weight(_text_:a in 3291) [ClassicSimilarity], result of:
      0.0051806658 = score(doc=3291,freq=22.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.16900843 = fieldWeight in 3291, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.03125 = fieldNorm(doc=3291)
    0.012874164 = weight(_text_:r in 3291) [ClassicSimilarity], result of:
      0.012874164 = score(doc=3291,freq=2.0), product of:
        0.088001914 = queryWeight, product of:
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.026584605 = queryNorm
        0.14629413 = fieldWeight in 3291, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.03125 = fieldNorm(doc=3291)
    0.00196408 = weight(_text_:s in 3291) [ClassicSimilarity], result of:
      0.00196408 = score(doc=3291,freq=4.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.06795235 = fieldWeight in 3291, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.03125 = fieldNorm(doc=3291)
  0.27272728 = coord(3/11)
    Abstract
    The web is a network of linked sites whereby each site either forms a physical portal or a standalone page. In the former case, the portal presents an access point to its embedded web pages that coherently present a specific topic. In the latter case, there are millions of standalone web pages, that are scattered throughout the web, having the same topic and could be conceptually linked together to form virtual portals. Search engines have been developed to help users in reaching the adequate pages in an efficient and effective manner. All the known current search engine techniques rely on the web page as the basic atomic search unit. They ignore the conceptual links, that reveal the implicit web related meanings, among the retrieved pages. However, building a semantic model for the whole portal may contain more semantic information than a model of scattered individual pages. In addition, user queries can be poor and contain imprecise terms that do not reflect the real user intention. Consequently, retrieving the standalone individual pages that are directly related to the query may not satisfy the user's need. In this paper, we propose PREFCA, a Portal Retrieval Engine based on Formal Concept Analysis that relies on the portal as the main search unit. PREFCA consists of three phases: First, the information extraction phase that is concerned with extracting portal's semantic data. Second, the formal concept analysis phase that utilizes formal concept analysis to discover the conceptual links among portal and attributes. Finally, the information retrieval phase where we propose a portal ranking method to retrieve ranked pairs of portals and embedded pages. Additionally, we apply the network analysis rules to output some portal characteristics. We evaluated PREFCA using two data sets, namely the Forum for Information Retrieval Evaluation 2010 and ClueWeb09 (category B) test data, for physical and virtual portals respectively. PREFCA proves higher F-measure accuracy, better Mean Average Precision ranking and comparable network analysis and efficiency results than other search engine approaches, namely Term Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), and BM25 techniques. As well, it gains high Mean Average Precision in comparison with learning to rank techniques. Moreover, PREFCA also gains better reach time than Carrot as a well-known topic-based search engine.
    Source
    Information processing and management. 53(2017) no.1, S.203-222
    Type
    a
  13. Priss, U.; Jacob, E.: Utilizing faceted structures for information systems design (1999) 0.01 
    0.005092364 = product of:
  0.018672 = sum of:
    0.0046860883 = weight(_text_:a in 2470) [ClassicSimilarity], result of:
      0.0046860883 = score(doc=2470,freq=18.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.15287387 = fieldWeight in 2470, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.03125 = fieldNorm(doc=2470)
    0.0013888142 = weight(_text_:s in 2470) [ClassicSimilarity], result of:
      0.0013888142 = score(doc=2470,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.048049565 = fieldWeight in 2470, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.03125 = fieldNorm(doc=2470)
    0.012597097 = weight(_text_:u in 2470) [ClassicSimilarity], result of:
      0.012597097 = score(doc=2470,freq=2.0), product of:
        0.08704981 = queryWeight, product of:
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.026584605 = queryNorm
        0.14471136 = fieldWeight in 2470, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2744443 = idf(docFreq=4547, maxDocs=44218)
          0.03125 = fieldNorm(doc=2470)
  0.27272728 = coord(3/11)
    Abstract
    Even for the experienced information professional, designing an efficient multi-purpose information access structure can be a very difficult task. This paper argues for the use of a faceted thesaurus as the basis for organizing a small-scale institutional website. We contend that a faceted approach to knowledge organization can make the process of organization less random and more manageable. We begin by reporting on an informal survey of three institutional websites. This study underscores the problems of organization that can impact access to information. We then formalize the terminology of faceted thesauri and demonstrate its application with several examples.
    The writers show that a faceted navigation structure makes web sites easier to use. They begin by analyzing the web sites of three library and information science faculties, and seeing if the sites easily provide the answers to five specific questions, e.g., how the school ranks in national evaluations. (It is worth noting that the web site of the Faculty of Information Studies and the University of Toronto, where this bibliography is being written, would fail on four of the five questions.) Using examples from LIS web site content, they show how facets can be related and constructed, and use concept diagrams for illustration. They briefly discuss constraints necessary when joining facets: for example, enrolled students can be full- or part-time, but prospective and alumni students cannot. It should not be possible to construct terms such as "part-time alumni" (see Yannis Tzitzikas et al, below in Background). They conclude that a faceted approach is best for web site navigation, because it can clearly show where the user is in the site, what the related pages are, and how to get to them. There is a short discussion of user interfaces, and the diagrams in the paper will be of interest to anyone making a facet-based web site. This paper is clearly written, informative, and thought-provoking. Uta Priss's web site lists her other publications, many of which are related and some of which are online: http://www.upriss.org.uk/top/research.html.
    Pages
    S.203-212
    Type
    a
  14. Sedelow, S.Y.; Sedelow, W.A.: Thesauri and concept-lattice semantic nets (1994) 0.00 
    0.004211078 = product of:
  0.015440619 = sum of:
    0.007251961 = product of:
      0.014503922 = sum of:
        0.014503922 = weight(_text_:h in 7733) [ClassicSimilarity], result of:
          0.014503922 = score(doc=7733,freq=2.0), product of:
            0.0660481 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.026584605 = queryNorm
            0.21959636 = fieldWeight in 7733, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0625 = fieldNorm(doc=7733)
      0.5 = coord(1/2)
    0.005411029 = weight(_text_:a in 7733) [ClassicSimilarity], result of:
      0.005411029 = score(doc=7733,freq=6.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.17652355 = fieldWeight in 7733, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=7733)
    0.0027776284 = weight(_text_:s in 7733) [ClassicSimilarity], result of:
      0.0027776284 = score(doc=7733,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.09609913 = fieldWeight in 7733, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0625 = fieldNorm(doc=7733)
  0.27272728 = coord(3/11)
    Abstract
    Formal concept lattices are a promising vehicle for the construction of rigorous and empirically accurate semantic nets. Presented here are results of initial experiments with concept lattices as representations of semantic relationships in the implicit structure of a large database (e.g. Roget's thesaurus)
    Pages
    S.350-357
    Source
    Knowledge organization and quality management: Proc. of the 3rd International ISKO Conference, 20-24 June 1994, Copenhagen, Denmark. Ed.: H. Albrechtsen et al
    Type
    a
  15. Kent, R.E.: Implications and rules in thesauri (1994) 0.00 
    0.004211078 = product of:
  0.015440619 = sum of:
    0.007251961 = product of:
      0.014503922 = sum of:
        0.014503922 = weight(_text_:h in 3457) [ClassicSimilarity], result of:
          0.014503922 = score(doc=3457,freq=2.0), product of:
            0.0660481 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.026584605 = queryNorm
            0.21959636 = fieldWeight in 3457, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0625 = fieldNorm(doc=3457)
      0.5 = coord(1/2)
    0.005411029 = weight(_text_:a in 3457) [ClassicSimilarity], result of:
      0.005411029 = score(doc=3457,freq=6.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.17652355 = fieldWeight in 3457, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=3457)
    0.0027776284 = weight(_text_:s in 3457) [ClassicSimilarity], result of:
      0.0027776284 = score(doc=3457,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.09609913 = fieldWeight in 3457, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0625 = fieldNorm(doc=3457)
  0.27272728 = coord(3/11)
    Abstract
    A central consideration in the study of whole language semantic space as encoded in thesauri is word sense comparability. Shows how word sense comparability can be adequately expressed by the logical implications and rules from Formal Concept Analysis. Formal concept analysis, a new approach to formal logic initiated by Rudolf Wille, has been used for data modelling, analysis and interpretation, and also for knowledge representation and knowledge discovery
    Pages
    S.154-160
    Source
    Knowledge organization and quality management: Proc. of the 3rd International ISKO Conference, 20-24 June 1994, Copenhagen, Denmark. Ed.: H. Albrechtsen et al
    Type
    a
  16. Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1992) 0.00 
    0.004096325 = product of:
  0.045059573 = sum of:
    0.045059573 = weight(_text_:r in 3147) [ClassicSimilarity], result of:
      0.045059573 = score(doc=3147,freq=2.0), product of:
        0.088001914 = queryWeight, product of:
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.026584605 = queryNorm
        0.51202947 = fieldWeight in 3147, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3102584 = idf(docFreq=4387, maxDocs=44218)
          0.109375 = fieldNorm(doc=3147)
  0.09090909 = coord(1/11)
  17. Sedelow, W.A.: ¬The formal analysis of concepts (1993) 0.00 
    0.0016410447 = product of:
  0.009025746 = sum of:
    0.0062481174 = weight(_text_:a in 620) [ClassicSimilarity], result of:
      0.0062481174 = score(doc=620,freq=8.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.20383182 = fieldWeight in 620, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=620)
    0.0027776284 = weight(_text_:s in 620) [ClassicSimilarity], result of:
      0.0027776284 = score(doc=620,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.09609913 = fieldWeight in 620, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0625 = fieldNorm(doc=620)
  0.18181819 = coord(2/11)
    Abstract
    The present paper focuses on the extraction, by means of a formal logical/mathematical methodology (i.e. automatically, exclusively by rule), of concept content, as in, for example, continuous discourse. The approach to a fully formal defintion of concept content ultimately is owing to a German government initiative to establish 'standards' regarding concepts, in conjunction with efforts to stipulate precisely (and then, derivatively, through computer prgrams) data and information needs according to work role in certain government offices
    Source
    Behavior research methods instruments and computers. 25(1993) no.2, S.314-317
    Type
    a
  18. De Maio, C.; Fenza, G.; Loia, V.; Senatore, S.: Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis (2012) 0.00 
    0.0015791605 = product of:
  0.008685382 = sum of:
    0.0057392623 = weight(_text_:a in 2737) [ClassicSimilarity], result of:
      0.0057392623 = score(doc=2737,freq=12.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.18723148 = fieldWeight in 2737, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2737)
    0.00294612 = weight(_text_:s in 2737) [ClassicSimilarity], result of:
      0.00294612 = score(doc=2737,freq=4.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.101928525 = fieldWeight in 2737, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=2737)
  0.18181819 = coord(2/11)
    Abstract
    In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.
    Source
    Information processing and management. 48(2012) no.3, S.399-418
    Type
    a
  19. Kollewe, W.: Data representation by nested line diagrams illustrated by a survey of pensioners (1991) 0.00 
    0.0014359142 = product of:
  0.007897528 = sum of:
    0.005467103 = weight(_text_:a in 5230) [ClassicSimilarity], result of:
      0.005467103 = score(doc=5230,freq=8.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.17835285 = fieldWeight in 5230, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5230)
    0.0024304248 = weight(_text_:s in 5230) [ClassicSimilarity], result of:
      0.0024304248 = score(doc=5230,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.08408674 = fieldWeight in 5230, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5230)
  0.18181819 = coord(2/11)
    Abstract
    With formal concept analysis surveys are analyzable in the way that a meaningful picture of the answers of the interviewed persons is available. Line diagrams of large concept lattices might become less readable up to the point that it is impossible to pursue the line segments with the eyes. Nested line diagrams give the opportunity to overcome these difficulties. The main idea of nested line diagrams is to partition the line diagram into boxes so that line segments between two boxes are all parallel and may be replaced by one line segment. The possibility to draw line diagrams with more than two factors does allow it to describe concept lattices with many hundred or thousand concepts in a clear structure. In practice it has often been proven useful to take standardized scales for the single levels
    Source
    International classification. 18(1991) no.1, S.26-34
    Type
    a
  20. Ganter, B.: Computing with conceptual structures (2000) 0.00 
    0.0013313505 = product of:
  0.007322428 = sum of:
    0.0052392064 = weight(_text_:a in 5088) [ClassicSimilarity], result of:
      0.0052392064 = score(doc=5088,freq=10.0), product of:
        0.030653298 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.026584605 = queryNorm
        0.1709182 = fieldWeight in 5088, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=5088)
    0.0020832212 = weight(_text_:s in 5088) [ClassicSimilarity], result of:
      0.0020832212 = score(doc=5088,freq=2.0), product of:
        0.028903782 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.026584605 = queryNorm
        0.072074346 = fieldWeight in 5088, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.046875 = fieldNorm(doc=5088)
  0.18181819 = coord(2/11)
    Abstract
    We give an overview over the computational tools for conceptional structures that have emerged from the theory of Formal Concept Analysis, with emphasis on basic ideas rather than technical details. We describe what we mean by conceptual computations, and try to convince the reader that an elaborate formalization is a necessary precondition. Claiming that Formal Concept Analysis provides such a formal background, we present as examples two well known algorithms in very simple pseudo code. These earl be used for navigating in a lattice, thereby supporting some prototypical tasks of conceptual computation. We refer to some of the many more advanced methods, discuss how to compute with limited precision and explain why in the case of incomplete knowledge the conceptual approach is more efficient than a combinatorial one. Utilizing this efficiency requires skillful use of the formalism. We present two results that lead in this direction
    Pages
    S.483-495
    Type
    a

Authors

Years

Types

  • a 38
  • m 5
  • s 2
  • el 1
  • p 1
  • r 1
  • More… Less…

Themes

Subjects