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Data-Driven Logical Reasoning

By Volha Bryl and Luciano Serafini

Abstract

Abstract. The co-existence of heterogeneous but complementary data sources, such as ontologies and databases describing the same domain, is the reality of the Web today. In this paper we argue that this complementarity could be exploited both for discovering the knowledge not captured in the ontology but learnable from the data, and for enhancing the process of ontological reasoning by relying on the combination of formal domain models and evidence coming from data. We build upon our previous work on knowledge discovery from heterogeneous sources of information via association rules mining, and propose a method for automated reasoning on grounded knowledge bases (i.e. knowledge bases linked to data) based on the standard Tableaux algorithm. The proposed approach combines logical reasoning and statistical inference thus making sense of heterogeneous data sources.

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.360.1298
Provided by: CiteSeerX
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