Mining relationship associations from knowledge about failures using ontology and inference

Authors:
Weisen Guo;Steven B. Kraines
Affiliations:
Science Integration Program, Department of Frontier Sciences and Science Integration, Division of Project Coordination, The University of Tokyo, Kashiwa, Japan;Science Integration Program, Department of Frontier Sciences and Science Integration, Division of Project Coordination, The University of Tokyo, Kashiwa, Japan
Venue:
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Year:
2010

Citing 6
Cited 0

An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
The description logic handbook: theory, implementation, and applications

The description logic handbook: theory, implementation, and applications
Mining Semantic Networks for Knowledge Discovery

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Generalized Substructures from a Set of Labeled Graphs

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Extracting failure knowledge with associative search

JSAI'07 Proceedings of the 2007 conference on New frontiers in artificial intelligence
EKOSS: a knowledge-user centered approach to knowledge sharing, discovery, and integration on the semantic web

ISWC'06 Proceedings of the 5th international conference on The Semantic Web

Quantified Score

Hi-index	0.00

Visualization

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.