Knowledge discovery in ontologies

Furletti B., Turini F.

Influence rules  Ontology  Artificial Intelligence  Theoretical Computer Science  Knowledge Discovery  Computer Vision and Pattern Recognition 

Ontologies allow us to represent knowledge and data in implicit and explicit ways. Implicit knowledge can be derived by means of several deductive logic-based processes. This paper introduces a new way for extracting implicit knowledge from ontologies by means of a sort of link analysis of the T-box of the ontology integrated with a data mining step on the A-box. The implicit extracted knowledge has the form of In uence Rules" i.e. rules structured as: if the property p1 of concept c1 has value v1, then the property p2 of concept c2 has value v2 with probability . The technique is completely general and applicable to whatever domain. The In uence Rules can be used to integrate existing knowledge or for supporting any other data mining process. A case study about an ontology describing intrusion detection is used to illustrate the result of the method.

Source: Intelligent data analysis 16 (2012): 513–534. doi:10.3233/IDA-2012-0536

Publisher: Elsevier Science, Inc.,, New York, NY , Stati Uniti d'America

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