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• W2808391653 abstract "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-the-art precision, recall and speed, and provides a more flexible framework for variations of the problem." @default.
• W2808391653 created "2018-06-21" @default.
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• W2808391653 date "2018-07-01" @default.
• W2808391653 modified "2023-10-06" @default.
• W2808391653 title "Machine Learning and Constraint Programming for Relational-To-Ontology Schema Mapping" @default.
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• W2808391653 doi "https://doi.org/10.24963/ijcai.2018/178" @default.
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