Knowledge discovery in ontologies

Article type: Research Article

Authors: Furletti, Barbara^{; *} | Turini, Franco

Affiliations: Department of Computer Science, University of Pisa, Pisa, Italy

Correspondence: [*] Corresponding author: Barbara Furletti, Department of Computer Science, University of Pisa. Largo B. Pontecorvo, 3-56100 Pisa, Italy. Tel.: +39 050/2213101; E-mail: [email protected]

Abstract: 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 link analysis of the T-box of the ontology integrated with a data mining step on the A-box. The implicit knowledge extracted is in the form of "Influence Rules" i.e. rules structured as: if property p1 of concept c1 has value v1, then property p2 of concept c2 has value v2 with probability π. The technique is completely general and applicable to whatever domain. The Influence Rules can be used to integrate existing knowledge or to support any other data mining process. A case study about an ontology that describes intrusion detection is used to illustrate how the method works.

Keywords: Ontology, knowledge extraction, data mining, influence rules, frequent items

DOI: 10.3233/IDA-2012-0536

Journal: Intelligent Data Analysis, vol. 16, no. 3, pp. 513-534, 2012