Datalignment: Ontology Schema Alignment through datalog containment

Daniel Deutch, Evgeny Marants, Yuval Moskovitch

Research output: Contribution to journalConference articlepeer-review

Abstract

We focus on the problem of aligning ontology relations, namely finding relation names that correspond to the same or related concepts. Such alignment is a prerequisite to the integration of the multiple available Knowledge Bases many of which include similar concepts, differently termed. We propose a novel approach for this problem, by leveraging association rules -originally mined in order to enrich the ontological content. Here, we treat the rules as Datalog programs and look for bounded-depth sub-programs that are contained in (or equivalent to) each other. Heads of such programs intuitively correspond to related concepts, and we propose them as candidates for alignment. The candidate alignments require further verification by experts; to this end we accompany each aligned pair with explanations based on the provenance of each relation according to its sub-program. We have implemented our novel solution in a system called Datalignment. We propose to demonstrate Datalignment, presenting the aligned pairs that it finds, and the computed explanations, in context of real-life Knowledge Bases.

Original languageEnglish
Pages (from-to)1870-1873
Number of pages4
JournalProceedings of the VLDB Endowment
Volume12
Issue number12
DOIs
StatePublished - 2018
Event45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States
Duration: 26 Aug 201730 Aug 2017

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