Ontology-Enriched Query Answering on Relational Databases

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.

Cite

Text

Ahmetaj et al. "Ontology-Enriched Query Answering on Relational Databases." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I17.17789

Markdown

[Ahmetaj et al. "Ontology-Enriched Query Answering on Relational Databases." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/ahmetaj2021aaai-ontology/) doi:10.1609/AAAI.V35I17.17789

BibTeX

@inproceedings{ahmetaj2021aaai-ontology,
  title     = {{Ontology-Enriched Query Answering on Relational Databases}},
  author    = {Ahmetaj, Shqiponja and Efthymiou, Vasilis and Fagin, Ronald and Kolaitis, Phokion G. and Lei, Chuan and Özcan, Fatma and Popa, Lucian},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15247-15254},
  doi       = {10.1609/AAAI.V35I17.17789},
  url       = {https://mlanthology.org/aaai/2021/ahmetaj2021aaai-ontology/}
}