Logic programming and databases
Logic programming and databases
Incremental Maintenance of Externally Materialized Views
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Description logic programs: combining logic programs with description logic
WWW '03 Proceedings of the 12th international conference on World Wide Web
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Linked data on the web (LDOW2008)
Proceedings of the 17th international conference on World Wide Web
Effective and efficient semantic web data management over DB2
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Query Answering for OWL-DL with rules
Web Semantics: Science, Services and Agents on the World Wide Web
Towards a complete OWL ontology benchmark
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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Semantic web data management on top of relational database has been regarded as a promising solution for scalable ontology storing, querying and reasoning. In order to reduce query response time, inferred results often need to be pre-computed and materialized. However, this approach faces the great challenge when data is updated, since the repositories need to perform reasoning from scratch to guarantee the consistency of the inferred results. This paper proposes a novel solution to enable the incremental data update on the context that ontology reasoning and user-defined rule reasoning are performed over multiple ontologies. We propose an effective data organization method that uniformly organizes both original ontologies and inferred results for ontology and user-defined rule reasoning, with the support of named graph. Inspired by the Rete algorithm, we design an inference network to link the ontology data, inferred results, and intermediate inferred results. As a consequence, when the ontology data update, changes can be effectively propagated in the network based on their named graphs. We implement the proposed approach on our previous ontology store and the experimental results show that our solution can significantly improve the reasoning performance when ontology data update happens.