Using ontology databases for scalable query answering, inconsistency detection, and data integration

  • Authors:

  • Paea Lependu;Dejing Dou

  • Affiliations:

  • Computer and Information Science Department, University of Oregon, Eugene, USA 97403 and National Center for Biomedical Ontology, Stanford Center for Biomedical Informatics Research, Stanford Univ ...;Computer and Information Science Department, University of Oregon, Eugene, USA 97403

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2011

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Abstract

An ontology database is a basic relational database management system that models an ontology plus its instances. To reason over the transitive closure of instances in the subsumption hierarchy, for example, an ontology database can either unfold views at query time or propagate assertions using triggers at load time. In this paper, we use existing benchmarks to evaluate our method--using triggers--and we demonstrate that by forward computing inferences, we not only improve query time, but the improvement appears to cost only more space (not time). However, we go on to show that the true penalties were simply opaque to the benchmark, i.e., the benchmark inadequately captures load-time costs. We have applied our methods to two case studies in biomedicine, using ontologies and data from genetics and neuroscience to illustrate two important applications: first, ontology databases answer ontology-based queries effectively; second, using triggers, ontology databases detect instance-based inconsistencies--something not possible using views. Finally, we demonstrate how to extend our methods to perform data integration across multiple, distributed ontology databases.