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Authors: Barbara Furletti and Franco Turini

Affiliation: University of Pisa, Italy

Keyword(s): Ontology Mining, Knowledge Discovery, If-Then Rules.

Abstract: 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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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; ISSN 2184-2833, SciTePress, pages 323-333. DOI: 10.5220/0003438403230333

@conference{icsoft11,
author={Barbara Furletti. and Franco Turini.},
title={MINING INFLUENCE RULES OUT OF ONTOLOGIES},
booktitle={Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT},
year={2011},
pages={323-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003438403230333},
isbn={978-989-8425-77-5},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT
TI - MINING INFLUENCE RULES OUT OF ONTOLOGIES
SN - 978-989-8425-77-5
IS - 2184-2833
AU - Furletti, B.
AU - Turini, F.
PY - 2011
SP - 323
EP - 333
DO - 10.5220/0003438403230333
PB - SciTePress