Conference Proceedings

Machine learning and constraint programming for relational-to-ontology schema mapping

D De Uña, N Rümmele, G Gange, P Schachte, PJ Stuckey

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence | International Joint Conferences on Artificial Intelligence | Published : 2018

Abstract

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. The problem of integrating heterogeneous data sources into an ontology is highly relevant in the database field. Several techniques exist to approach the problem, but side constraints on the data cannot be easily implemented and thus the results may be inconsistent. In this paper we improve previous work by Taheriyan et al. [2016a] using Machine Learning (ML) to take into account inconsistencies in the data (unmatchable attributes) and encode the problem as a variation of the Steiner Tree, for which we use work by De Uña et al. [2016] in Constraint Programming (CP). Combining ML and CP achieves state-of-th..

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