Abstract
Ontology-based access to large data-sets has recently gained a lot of attention. To access data efficiently, one approach is to rewrite the ontology into Datalog, and then use powerful Datalog engines to compute implicit entailments. Existing rewriting techniques support Description Logics (DLs) from ELH to Horn-SHIQ. We go one step further and present one such data-independent rewriting technique for Horn-SRIQu, the extension of Horn-SHIQ that supports role chain axioms, an expressive feature prominently used in many real-world ontologies. We evaluated our rewriting technique on a large known corpus of ontologies. Our experiments show that the resulting rewritings are of moderate size, and that our approach is more efficient than state-of-the-art DL reasoners when reasoning with data-intensive ontologies.
Original language | English |
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Title of host publication | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
Publisher | AAAI Press |
Pages | 2736-2743 |
ISBN (Electronic) | 9781577358091 |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 - Honolulu, United States Duration: 27 Jan 2019 → 1 Feb 2019 |
Conference
Conference | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
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Country/Territory | United States |
City | Honolulu |
Period | 27/01/19 → 1/02/19 |
Funding
We thank Irina Dragoste for assisting us with the execution of the experiments, for which we used the servers from the Centre for Information Services and High Performance Computing (ZIH) at the Technische Universität Dresden. This work is partly funded by the DFG within the Center for Advancing Electronics Dresden (cfaed), the Collaborative Research Center CRC 912 (HAEC), and Emmy Noether grant KR 4381/1-1 (DIAMOND).
Funders | Funder number |
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HAEC | KR 4381/1-1 |
Deutsche Forschungsgemeinschaft | |
Technische Universität Dresden |