PlumX Metrics
Embed PlumX Metrics

Knowledge discovery in ontologies

Intelligent Data Analysis, ISSN: 1088-467X, Vol: 16, Issue: 3, Page: 513-534
2012
  • 4
    Citations
  • 0
    Usage
  • 7
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

Conference Paper Description

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. © 2012 - IOS Press and the authors. All rights reserved.

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know