Search (26 results, page 1 of 2)

  • × theme_ss:"Formale Begriffsanalyse"
  1. Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995) 0.04 
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    Abstract
    TOSCANA is a computer program which allows an online interaction with larger data bases to analyse and explore data conceptually. It uses labelled line diagrams of concept lattices to communicate knowledge coded in given data. The basic problem to create online presentations of concept lattices is solved by composing prepared diagrams to nested line diagrams. A larger number of applications in different areas have already shown that TOSCANA is a useful tool for many purposes
    Source
    Knowledge organization. 22(1995) no.2, S.78-81
  2. Vogt, F.; Wachter, C.; Wille, R.: Data analysis based on a conceptual file (1991) 0.02 
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    Source
    Classification, data analysis, and knowledge organization. Ed.: H.H. Bock u. P. Ihm
  3. Wille, R.: Geometric representations of concept lattices (1989) 0.02 
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    Source
    Conceptual and numerical analysis of data. Ed.: O. Opitz
  4. Wille, R.: Lattices in data analysis : how to draw them with a computer (1989) 0.02 
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  5. Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1992) 0.01 
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  6. Wille, R.: Knowledge acquisition by methods of formal concept analysis (1989) 0.01 
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    Source
    Data analysis, learning symbolic and numeric knowledge. Hrsg.: E. Diday
  7. Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995) 0.01 
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  8. Hereth, J.; Stumme, G.; Wille, R.; Wille, U.: Conceptual knowledge discovery and data analysis (2000) 0.01 
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    Abstract
    In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin
    Theme
    Data Mining
  9. Kaytoue, M.; Kuznetsov, S.O.; Assaghir, Z.; Napoli, A.: Embedding tolerance relations in concept lattices : an application in information fusion (2010) 0.01 
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    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.
    Series
    Knowledge and data representation and management; no.7353
  10. Kumar, C.A.; Radvansky, M.; Annapurna, J.: Analysis of Vector Space Model, Latent Semantic Indexing and Formal Concept Analysis for information retrieval (2012) 0.01 
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    Abstract
    Latent Semantic Indexing (LSI), a variant of classical Vector Space Model (VSM), is an Information Retrieval (IR) model that attempts to capture the latent semantic relationship between the data items. Mathematical lattices, under the framework of Formal Concept Analysis (FCA), represent conceptual hierarchies in data and retrieve the information. However both LSI and FCA uses the data represented in form of matrices. The objective of this paper is to systematically analyze VSM, LSI and FCA for the task of IR using the standard and real life datasets.
  11. Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1993) 0.01 
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  12. Rusch, A.; Wille, R.: Knowledge spaces and formal concept analysis (1996) 0.01 
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    Source
    Data analysis and information systems, statistical and conceptual approaches: Proceedings of the 19th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Basel, March 8-10, 1995. Ed.: H.-H. Bock u. W. Polasek
  13. Reinartz, T.P.; Zickwolff, M.: ¬Two conceptual approaches to acquire human expert knowledge in a complex real world domain (1996) 0.01 
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    Source
    Data analysis and information systems, statistical and conceptual approaches: Proceedings of the 19th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Basel, March 8-10, 1995. Ed.: H.-H. Bock u. W. Polasek
  14. Burmeister, P.; Holzer, R.: On the treatment of incomplete knowledge in formal concept analysis (2000) 0.01 
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    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
  15. Sedelow, W.A.: ¬The formal analysis of concepts (1993) 0.01 
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    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
  16. Kent, R.E.: Implications and rules in thesauri (1994) 0.01 
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    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
  17. Ganter, B.; Wille, R.: Formale Begriffsanalyse : Mathematische Grundlagen (1996) 0.01 
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    Abstract
    This first textbook in the field of formal concept analysis provides a systematic presentation of the mathematical foundations and their relation to applications in informatics, especially data analysis and knowledge processing
  18. Luksch, P.; Wille, R.: ¬A mathematical model for conceptual knowledge systems (1991) 0.01 
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    Source
    Classification, data analysis, and knowledge organization: models and methods with applications. Proc. of the 14th annual conf. of the Gesellschaft für Klassifikation, Univ. of Marburg, 12.-14.3.1990. Ed.: H.-H. Bock u. P. Ihm
  19. Kollewe, W.: Data representation by nested line diagrams illustrated by a survey of pensioners (1991) 0.01 
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  20. Negm, E.; AbdelRahman, S.; Bahgat, R.: PREFCA: a portal retrieval engine based on formal concept analysis (2017) 0.01 
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    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.

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