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Efficient identification of implicit facts in incomplete OWL2-EL knowledge bases
2014
Proceedings of the VLDB Endowment
Integrating incomplete and possibly inconsistent data from various sources is a challenge that arises in several application areas, especially in the management of scientific data. A rising trend for data integration is to model the data as axioms in the Web Ontology Language (OWL) and use inference rules to identify new facts. Although there are several approaches that employ OWL for data integration, there is little work on scalable algorithms able to handle large datasets that do not fit in
doi:10.14778/2733085.2733104
fatcat:vht4ep5uavgvlbd3qstglae5p4