Deriving Production Rules for Incremental View Maintenance
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
A Method for Change Computation in Deductive Databases
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
SOR: a practical system for ontology storage, reasoning and search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A Rule-Based Object-Oriented OWL Reasoner
IEEE Transactions on Knowledge and Data Engineering
DLDB2: A Scalable Multi-perspective Semantic Web Repository
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A comparison of reasoning techniques for querying large description logic ABoxes
LPAR'06 Proceedings of the 13th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
Towards a complete OWL ontology benchmark
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
OWLIM – a pragmatic semantic repository for OWL
WISE'05 Proceedings of the 2005 international conference on Web Information Systems Engineering
Minerva: a scalable OWL ontology storage and inference system
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
The extension-based inference algorithm for pD*
Data & Knowledge Engineering
Type inference methods and performance for data in an RDBMS
SWIM '12 Proceedings of the 4th International Workshop on Semantic Web Information Management
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In this paper we describe a method to perform type inference over data stored in an RDBMS, where rules over the data are specified using OWL-DL. Since OWL-DL is an implementation of the Description Logic (DL) called SHOIN(D), we are in effect implementing a method for SHOIN(D) reasoning in relational databases. Reasoning may be broken down into two processes of classification and type inference. Classification may be performed efficiently by a number of existing reasoners, and since classification alters the schema, it need only be performed once for any given relational schema as a preprocessor of the schema before creation of a database schema. However, type inference needs to be performed for each data value added to the database, and hence needs to be more tightly coupled with the database system. We propose a technique to meet this requirement based on the use of triggers, which is the first technique to fully implement SHOIN(D) as part of normal transaction processing.