Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle

  • Authors:

  • Zhe Wu;George Eadon;Souripriya Das;Eugene Inseok Chong;Vladimir Kolovski;Melliyal Annamalai;Jagannathan Srinivasan

  • Affiliations:

  • Oracle, 1 Oracle Drive, Nashua, NH 03062, USA. alan.wu@oracle.com;Oracle, 1 Oracle Drive, Nashua, NH 03062, USA. george.eadon@oracle.com;Oracle, 1 Oracle Drive, Nashua, NH 03062, USA. souripriya.das@oracle.com;Oracle, 1 Oracle Drive, Nashua, NH 03062, USA. eugene.chong@oracle.com;Oracle, 1 Oracle Drive, Nashua, NH 03062, USA/ Univ. of Maryland, Computer Science Dept., MD, USA. kolovoski@cs.umd.edu;Oracle, 1 Oracle Drive, Nashua, NH 03062, USA. melliyal.annamalai@oracle.com;Oracle, 1 Oracle Drive, Nashua, NH 03062, USA. jagannathan.srinivasan@oracle.com

  • Venue:
  • ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
  • Year:
  • 2008

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Abstract

This Inference Engines are an integral part of Semantic Data Stores. In this paper, we describe our experience of implementing a scalable inference engine for Oracle Semantic Data Store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, ii) computing ancillary information (namely, semantic distance and proof) for inferred triples, and iii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of Oracle Database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using both native and synthesized semantic datasets demonstrates the effectiveness of our approach.