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                                       Details for article 8 of 10 found articles
  Knowledge discovery in ontologies
Title: Knowledge discovery in ontologies
Author: Furletti, Barbara
Turini, Franco
Appeared in: Intelligent data analysis
Paging: Volume 16 (2012) nr. 3 pages 513-534
Year: 2012-05-09
Contents: 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 p_1 of concept c_1 has value v_1, then property p_2 of concept c_2 has value v_2 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.
Publisher: IOS Press
Source file: Elektronische Wetenschappelijke Tijdschriften

                             Details for article 8 of 10 found articles
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