知識の関係構造を用いた新しい概念の生成  [in Japanese] Concept Generation from Relational Structure  [in Japanese]

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Author(s)

Abstract

Discovery learning, which acquires new concepts or knowledge, is one of the most advanced forms of machine learning. Few systems have been proposed for discovery learning in practical use, and most of them are based on various heuristics. Discovery learning is considered to consist of two processes: inductive acquisition of general structure(relational structure) from existing knowledge base, and application of the relational structure to a domain knowledge for acquiring new concepts in the domain. In this paper we mainly focus on the application process, and propose a method of generating new concepts, that is, new predicates which do not occur in the domain knowledge, by applying the relational structure. We prove that the new generated clauses including the new predicates are consistent with the domain knowledge, and propose an algorithm for approximate calculating the new clauses from the relational structure and the domain owledge in finite steps. We give proof of some useful theorems for this algorithm. In addition, we discuss the the method for the acquisition of relational structure from an existing knowledge base.

Journal

  • Transactions of the Japanese Society for Artificial Intelligence

    Transactions of the Japanese Society for Artificial Intelligence 21, 450-458, 2006-11-01

    The Japanese Society for Artificial Intelligence

References:  11

Codes

  • NII Article ID (NAID)
    10022006712
  • NII NACSIS-CAT ID (NCID)
    AA11579226
  • Text Lang
    JPN
  • Article Type
    ART
  • ISSN
    13460714
  • NDL Article ID
    8686550
  • NDL Source Classification
    ZM13(科学技術--科学技術一般--データ処理・計算機)
  • NDL Call No.
    Z74-C589
  • Data Source
    CJP  NDL  J-STAGE 
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