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.
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References
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Schneider, P.P.: The Description Logic Handbook: Theory, implementation and applications. CUP (2003)
Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A Comparison of String Distance Metrics for Name-Matching Tasks. In: Proc. of the ACM Workshop on Data Cleaning, Record Linkage and Object Identification 2003 (2003)
Guo, W., Kraines, S.: Explicit Scientific Knowledge Comparison Based on Semantic Description Matching. In: Proc. of American Society for Information Science and Technology 2008 Annual Meeting (2008)
Guo, W., Kraines, S.B.: Mining Common Semantic Patterns from Descriptions of Failure Knowledge. In: Proc. of the 6th International Workshop on Mining and Learning with Graphs (2008)
Guo, W., Kraines, S.B.: Discovering Relationship Associations in Life Sciences Using Ontology and Inference. In: Proc. of the 1st International Conference on Knowledge Discovery and Information Retrieval, pp. 10–17 (2009)
Guo, W., Kraines, S.B.: Extracting Relationship Associations from Semantic Graphs in Life Sciences. In: Fred, A., et al. (eds.) Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2009, Revised Selected Papers. CCIS. Springer, Heidelberg (2010)
Hatamura, Y., IIno, K., Tsuchiya, K., Hamaguchi, T.: Structure of Failure Knowledge Database and Case Expression. CIRP Annals- Manufacturing Technology 52(1), 97–100 (2003)
Inokuchi, A., Washio, T., Motoda, H.: An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data. In: Proc. of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 13–23 (2000)
Inokuchi, A., Washio, T., Nishimura, Y.: A Fast Algorithm for Mining Frequent Connected Subgraphs. IBM Research Report, RT0448 (Feburary 2002)
Inokuchi, A.: Mining Generalized Substructures from a Set of Labeled Graphs. In: Proc. of the 4th IEEE International Conference on Data Mining, pp. 415–418 (2004)
JST Failure Knowledge Database, http://shippai.jst.go.jp/en/
Kraines, S., Guo, W.: Using Human Authored Description Logics ABoxes as Concept Models for Natural Language Generation. In: Proc. of American Society for Information Science and Technology 2009 Annual Meeting (2009)
Kraines, S., Guo, W., Kemper, B., Nakamura, Y.: EKOSS: A Knowledge-User Centered Approach to Knowledge Sharing, Discovery, and Integration on the Semantic Web. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 833–846. Springer, Heidelberg (2006)
Liao, T.W., Zhang, Z.M., Mount, C.R.: A Case-Based Reasoning System for Identifying Failure Mechanisms. Engineering Applications of Artificial Intelligence 13, 199–213 (2000)
Mooney, J.R., Melvile, P., Tang, L.R., Shavlik, J., Castro Dutra, I., Page, D., Costa, V.S.: Relational Data Mining with Inductive Logic Programming for Link Discovery. In: Proc. of the National Science Foundation Workshop on Next Generation Data Mining (2002)
Nakao, M., Tsuchiya, K., Harita, Y., Iino, K., Kinukawa, H., Kawagoe, S., Koike, Y., Takano, A.: Extracting Failure Knowledge with Associative Search. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 269–276. Springer, Heidelberg (2008)
OWL Web Ontology Language Guide, http://www.w3.org/TR/owl-guide/
Rajaraman, K., Tan, A.H.: Mining Semantic Networks for Knowledge Discovery. In: Proc. of the 3rd IEEE International Conference on Data Mining (2003)
Tamura, M.: Learn from Failure! Failure Knowledge in Chemical Substances and Plants and Its Use. Chemistry 58(8), 24–29 (2003) (Japanese)
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Guo, W., Kraines, S.B. (2010). Mining Relationship Associations from Knowledge about Failures Using Ontology and Inference. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_48
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DOI: https://doi.org/10.1007/978-3-642-14400-4_48
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