PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Furletti B., Turini F. Knowledge discovery in ontologies. In: Intelligent Data Analysis, vol. 16 (3) pp. 513 - 534. IOS PRESS, 2012.
 
 
Abstract
(English)
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.
URL: http://iospress.metapress.com/content/765h53w41286p578/?p=f709eeb49d96473da12d07bab8109178&pi=9
DOI: 10.3233/IDA-2012-0536
Subject Ontology, Knowledge Discovery, influence rules,
Data mining
97R50 Data bases, information systems


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