ADaMaP: Automatic Alignment of Data Sources using Mapping Patterns

Diego Calvanese, Avigdor Gal, Naor Haba, Davide Lanti, Marco Montali, Alessandro Mosca, and Roee Shraga

Proc. of the 33rd Int. Conf. on Advanced Information Systems Engineering (CAiSE 2021). Lecture Notes in Computer Science. 2021.

We propose a method for automatically extracting semantics from data sources. The availability of multiple data sources on the one hand and the lack of proper semantic documentation of such data sources on the other hand call for new strategies in integrating data sources by extracting semantics from the data source itself rather than from its documentation. In this work we focus on relational databases, observing they are created from semantically-rich designs such as ER diagrams, which are often not conveyed together with the database itself. While the relational model may be semantically-poor with respect to ontological models, the original semantically-rich design of the application domain leaves recognizable footprints that can be converted into ontology mapping patterns. In this work, we offer an algorithm to automatically detect and map a relational schema to ontology mapping patterns and offer an empirical evaluation using two benchmark datasets.


@inproceedings{CAiSE-2021,
   title = "ADaMaP: Automatic Alignment of Data Sources using Mapping
Patterns",
   year = "2021",
   author = "Diego Calvanese and Avigdor Gal and Naor Haba and Davide
Lanti and Marco Montali and Alessandro Mosca and Roee Shraga",
   booktitle = "Proc. of the 33rd Int. Conf. on Advanced Information Systems
Engineering (CAiSE 2021)",
   publisher = "Springer",
   series = "Lecture Notes in Computer Science",
}
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