Knowledge acquisition under incomplete knowledge using methods from formal concept analysis: Part I

被引:0
|
作者
Holzer, Richard [1 ]
机构
[1] Department of Mathematics, AG 1, Darmstadt University of Technology, Schloßgartenstr. 7, D-64289 Darmstadt, Germany
关键词
Algorithms; -; Semantics;
D O I
暂无
中图分类号
学科分类号
摘要
Formal contexts with unknown entries can be represented by three-valued contexts K=(G, M, {×, o, ?}, I), where a question mark indicates that it is not known whether the object g∈G has the attribute m∈M. To describe logical formulas between columns of such incomplete contexts the Kripke-semantics are used for propositional formulas over the set M of attributes. Attribute implications are considered as special propositional formulas. If a context is too large to be fully represented, an interactive computer algorithm may help the user to get maximal information (with respect to his knowledge) about the valid attribute implications of the unknown context. This computer algorithm is called attribute exploration.
引用
收藏
页码:17 / 39
相关论文
共 50 条
  • [1] Knowledge acquisition under incomplete knowledge using methods from formal concept analysis: Part II
    Holzer, R
    FUNDAMENTA INFORMATICAE, 2004, 63 (01) : 41 - 63
  • [2] KNOWLEDGE ACQUISITION BY METHODS OF FORMAL CONCEPT ANALYSIS
    WILLE, R
    DATA ANALYSIS, LEARNING SYMBOLIC AND NUMERIC KNOWLEDGE, 1989, : 365 - 380
  • [3] Treating incomplete knowledge in formal concept analysis
    Burmeister, P
    Holzer, R
    FORMAL CONCEPT ANALYSIS: FORMAL CONCEPT ANALYSIS, 2005, 3626 : 114 - 126
  • [4] On the treatment of incomplete knowledge in formal concept analysis
    Burmeister, P
    Holzer, R
    CONCEPTUAL STRUCTURES: LOGICAL, LINGUISTIC, AND COMPUTATIONAL ISSUES, PROCEEDINGS, 2000, 1867 : 385 - 398
  • [5] Distributive concept exploration - A knowledge acquisition tool in formal concept analysis
    Stumme, G
    KI-98: ADVANCES IN ARTIFICIAL INTELLIGENCE, 1998, 1504 : 117 - 128
  • [6] Conceptual Knowledge Discovery in Databases using Formal Concept Analysis methods
    Stumme, G
    Wille, R
    Wille, U
    PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 1510 : 450 - 458
  • [7] Methods of Incomplete and Uncertain Knowledge Acquisition in the Knowledge Processing Environment
    Orlowski, Cezary
    Sitek, Tomasz
    Rybacki, Rafal
    AGENT AND MULTI-AGENT SYSTEMS: TECHNOLOGIES AND APPLICATIONS, PT II, PROCEEDINGS, 2010, 6071 : 340 - 350
  • [8] Discovering Knowledge in Data Using Formal Concept Analysis
    Andrews, Simon
    Orphanides, Constantinos
    INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES, 2013, 4 (02) : 31 - 50
  • [9] A Knowledge Acquisition Model Based on Formal Concept Analysis in Complex Information Systems
    Kang, Xiangping
    Miao, Duoqian
    Jiao, Na
    ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, RSFDGRC 2015, 2015, 9437 : 286 - 297
  • [10] Representing and processing medical knowledge using formal concept analysis
    Schnabel, M
    METHODS OF INFORMATION IN MEDICINE, 2002, 41 (02) : 160 - 167