Machine Learning and Constraint Programming for Relational-to-Ontology Schema Mapping
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.
Cite
Text
de Uña et al. "Machine Learning and Constraint Programming for Relational-to-Ontology Schema Mapping." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/178Markdown
[de Uña et al. "Machine Learning and Constraint Programming for Relational-to-Ontology Schema Mapping." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/deuna2018ijcai-machine/) doi:10.24963/IJCAI.2018/178BibTeX
@inproceedings{deuna2018ijcai-machine,
title = {{Machine Learning and Constraint Programming for Relational-to-Ontology Schema Mapping}},
author = {de Uña, Diego and Rümmele, Nataliia and Gange, Graeme and Schachte, Peter and Stuckey, Peter J.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {1277-1283},
doi = {10.24963/IJCAI.2018/178},
url = {https://mlanthology.org/ijcai/2018/deuna2018ijcai-machine/}
}