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Mining Relationship Associations from Knowledge about Failures Using Ontology and Inference

  • Weisen Guo
  • Steven B. Kraines
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171)

Abstract

Mining general knowledge about relationships between concepts described in the analyses of failure cases could help people to avoid repeating previous failures. Furthermore, by representing knowledge using ontologies that support inference, we can identify relationships between concepts more effectively than text-mining techniques. A relationship association is a form of knowledge generalization that is based on binary relationships between entities in semantic graphs. Specifically, relationship associations involve two binary relationships that share a connecting entity and that co-occur frequently in a set of semantic graphs. Such connected relationships can be considered as generalized knowledge mined from a set of knowledge resources, such as failure case descriptions, that are formally represented by the semantic graphs. This paper presents the application of a technique to mine relationship associations from formalized semantic descriptions of failure cases. Results of mining relationship associations in a knowledge base containing 291 semantic graphs representing failure cases are presented.

Keywords

Relationship Associations Semantic Relationships Ontology Logical Inference Failure Knowledge Graph Mining Frequent Pattern Mining Knowledge Discovery 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Weisen Guo
    • 1
  • Steven B. Kraines
    • 1
  1. 1.Science Integration Program (Human) Department of Frontier Sciences and Science Integration Division of Project CoordinationThe University of TokyoKashiwaJapan

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