Ontology-Enriched Query Answering on Relational Databases

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Abstract

We develop a flexible, open-source framework for query answering on relational databases by adopting methods and techniques from the Semantic Web community and the data exchange community, and we apply this framework to a medical use case. We first deploy module-extraction techniques to derive a concise and relevant sub-ontology from an external reference ontology. We then use the chase procedure from the data exchange community to materialize a universal solution that can be subsequently used to answer queries on an enterprise medical database. Along the way, we identify a new class of well-behaved acyclic EL-ontologies extended with role hierarchies, suitably restricted functional roles, and domain/range restrictions, which cover our use case. We show that such ontologies are C-stratified, which implies that the chase procedure terminates in polynomial time. We provide a detailed overview of our real-life application in the medical domain and demonstrate the benefits of this approach, such as discovering additional answers and formulating new queries.

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APA

Ahmetaj, S., Efthymiou, V., Fagin, R., Kolaitis, P. G., Lei, C., Özcan, F., & Popa, L. (2021). Ontology-Enriched Query Answering on Relational Databases. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 17B, pp. 15247–15254). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v35i17.17789

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