Barbara Furletti, Franco Turini


A method for extracting new implicit knowledge from ontologies by using an inductive/deductive approach is presented. By analyzing the relationships that already exist in an ontology, we are able to return the extracted knowledge as weighted If-Then Rules among concepts. The technique, that combines data mining and link analysis, is completely general and applicable to whatever domain. Since the output is a set of “standard” If-Then Rules, it can be used to integrate existing knowledge or for supporting any other data mining process. An application of the method to an ontology representing companies and their activities is included.


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  16. 1. Company owner/CEO with past successful achievements even in different fields from the one in which the company operates today.
  17. 2. Company owner/CEO with no relevant past experiences.
  18. 3. Company owner/CEO with one or more unsuccessful past experiences.
  19. 1. Growing.
  20. 2. Stable.
  21. 3. Going toward stabilization.
  22. 4. In recession.

Paper Citation

in Harvard Style

Furletti B. and Turini F. (2011). MINING INFLUENCE RULES OUT OF ONTOLOGIES . In Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT, ISBN 978-989-8425-77-5, pages 323-333. DOI: 10.5220/0003438403230333

in Bibtex Style

author={Barbara Furletti and Franco Turini},
booktitle={Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT,
SN - 978-989-8425-77-5
AU - Furletti B.
AU - Turini F.
PY - 2011
SP - 323
EP - 333
DO - 10.5220/0003438403230333