Search (45 results, page 2 of 3)

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  1. Wille, R.: Knowledge acquisition by methods of formal concept analysis (1989) 0.00 
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    Source
    Data analysis, learning symbolic and numeric knowledge. Hrsg.: E. Diday
  2. Wille, R.: Line diagrams of hierarchical concept systems (1984) 0.00 
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  3. Wille, R.: Geometric representations of concept lattices (1989) 0.00 
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  4. Wille, R.: Lattices in data analysis : how to draw them with a computer (1989) 0.00 
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  5. Wille, R.: Concept lattices and conceptual knowledge systems (1992) 0.00 
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  6. Ganter, B.; Wille, R.: Conceptual scaling (1989) 0.00 
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  7. Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1992) 0.00 
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  8. Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995) 0.00 
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  9. Sowa, J.F.: Knowledge representation : logical, philosophical, and computational foundations (2000) 0.00 
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  10. Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1993) 0.00 
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  11. De Maio, C.; Fenza, G.; Loia, V.; Senatore, S.: Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis (2012) 0.00 
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    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.
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  12. Sedelow, S.Y.; Sedelow, W.A.: Thesauri and concept-lattice semantic nets (1994) 0.00 
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  13. Lex, W.: ¬A representation of concepts for their computerization (1987) 0.00 
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  14. Sedelow, W.A.: ¬The formal analysis of concepts (1993) 0.00 
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  15. Neuss, C.; Kent, R.E.: Conceptual analysis of resource meta-information (1995) 0.00 
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  16. Kent, R.E.: Implications and rules in thesauri (1994) 0.00 
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  17. Kollewe, W.: Data representation by nested line diagrams illustrated by a survey of pensioners (1991) 0.00 
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  18. Kumar, C.A.; Radvansky, M.; Annapurna, J.: Analysis of Vector Space Model, Latent Semantic Indexing and Formal Concept Analysis for information retrieval (2012) 0.00 
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  19. Eklund, P.; Groh, B.; Stumme, G.; Wille, R.: ¬A conceptual-logic extension of TOSCANA (2000) 0.00 
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  20. Eklund. P.W.: Logic-based networks : concept graphs and conceptual structures (2000) 0.00 
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