Ontology module extraction via datalog reasoning
<p>Module extraction — the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S — has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible....
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
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AAAI Press
2015
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| _version_ | 1824459295115182080 |
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| author | Romero, AA Kaminski, M Grau, BC Horrocks, I |
| author_facet | Romero, AA Kaminski, M Grau, BC Horrocks, I |
| author_sort | Romero, AA |
| collection | OXFORD |
| description | <p>Module extraction — the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S — has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and may lead to large modules in practice. In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog. Our approach not only generalises existing approximations in an elegant way, but it can also be tailored to preserve only specific kinds of entailments, which allows us to extract significantly smaller modules. An evaluation on widely-used ontologies has shown very encouraging results.</p> |
| first_indexed | 2024-03-06T23:13:35Z |
| format | Conference item |
| id | oxford-uuid:66582e16-9d83-42fc-96a9-3d662c6b62d6 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:39:31Z |
| publishDate | 2015 |
| publisher | AAAI Press |
| record_format | dspace |
| spelling | oxford-uuid:66582e16-9d83-42fc-96a9-3d662c6b62d62025-02-18T14:55:50ZOntology module extraction via datalog reasoningConference itemhttp://purl.org/coar/resource_type/c_5794uuid:66582e16-9d83-42fc-96a9-3d662c6b62d6EnglishSymplectic Elements at OxfordAAAI Press2015Romero, AAKaminski, MGrau, BCHorrocks, I<p>Module extraction — the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S — has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and may lead to large modules in practice. In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog. Our approach not only generalises existing approximations in an elegant way, but it can also be tailored to preserve only specific kinds of entailments, which allows us to extract significantly smaller modules. An evaluation on widely-used ontologies has shown very encouraging results.</p> |
| spellingShingle | Romero, AA Kaminski, M Grau, BC Horrocks, I Ontology module extraction via datalog reasoning |
| title | Ontology module extraction via datalog reasoning |
| title_full | Ontology module extraction via datalog reasoning |
| title_fullStr | Ontology module extraction via datalog reasoning |
| title_full_unstemmed | Ontology module extraction via datalog reasoning |
| title_short | Ontology module extraction via datalog reasoning |
| title_sort | ontology module extraction via datalog reasoning |
| work_keys_str_mv | AT romeroaa ontologymoduleextractionviadatalogreasoning AT kaminskim ontologymoduleextractionviadatalogreasoning AT graubc ontologymoduleextractionviadatalogreasoning AT horrocksi ontologymoduleextractionviadatalogreasoning |