Using Semantic Web Technologies for Knowledge-Driven Querying of Biomedical Data

  • Authors:

  • Martin O'Connor;Ravi Shankar;Samson Tu;Csongor Nyulas;Dave Parrish;Mark Musen;Amar Das

  • Affiliations:

  • Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305,;Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305,;Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305,;Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305,;The Immune Tolerance Network, Pittsburgh, PA, USA;Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305,;Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305,

  • Venue:
  • AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
  • Year:
  • 2007

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Abstract

Software applications that work with biomedical data have significant knowledge-management requirements. Formal knowledge models and knowledge-based methods can be very useful in meeting these requirements. However, most biomedical data are stored in relational databases, a practice that will continue for the foreseeable future. Using these data in knowledge-driven applications requires approaches that can form a bridge between relational models and knowledge models. Accomplishing this task efficiently is a research challenge. To address this problem, we have developed an end-to-end knowledge-based system based on Semantic Web technologies. It permits formal design-time specification of the data requirements of a system and uses those requirements to drive knowledge-driven queries on operational relational data in a deployed system. We have implemented a dynamic OWL-to-relational mapping method and used SWRL, the Semantic Web Rule Language, as a high-level query language that uses these mappings. We have used these methods to support the development of a participant tracking application for clinical trials and in the development of a test bed for evaluating biosurveillance methods.