Semantic Knowledge Discovery and Data-Driven Logical Reasoning from Heterogeneous Data Sources.
Claudia d’Amato, Volha Bryl, Luciano Serafini · 2014
Available domain ontologies are increasing over the time. However there is still a huge amount of data stored and managed with RDBMS. This complementarity could be exploited both for discovering knowledge patterns that are not formalized within the ontology but that are learnable from the data, and for enhancing reasoning on ontologies by relying on the combination of formal domain models and the evidence coming from data. We propose a method for learning association rules from both ontologies and RDBMS in an integrated way. The extracted patterns can be used for enriching the available knowledge in both format and for refining existing ontologies. We also propose a method for automated reasoning on grounded knowledge bases i.e. knowledge bases linked to RDBMS data based on the standard Tableaux algorithm which combines logical reasoning and statistical inference thus making sense of the heterogeneous data sources.