OQAFMA Querying agent for the foundational model of anatomy: a prototype for providing flexible and efficient access to large semantic networks

  • Authors:

  • Peter Mork;James F. Brinkley;Cornelius Rosse

  • Affiliations:

  • Department of Computer Science and Engineering, University of Washington, Seattle, WA and Structural Informatics Group, Departments of Biological Structure and Medical Eucation and Biomedical Info ...;Department of Computer Science and Engineering, University of Washington, Seattle, WA and Structural Informatics Group, Departments of Biological Structure and Medical Eucation and Biomedical Info ...;Structural Informatics Group, Departments of Biological Structure and Medical Eucation and Biomedical Informatics, Univetsity of Washington, Seattle, WA

  • Venue:
  • Journal of Biomedical Informatics - Special issue: Unified medical language system
  • Year:
  • 2003

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Abstract

The development of large semantic networks, such as the UMLS, which are intended to support a variety of applications, requires a flexible and efficient query interface for the extraction of information. Using one of the source vocabularies of UMLS as a test bed, we have developed such a prototype query interface. We first identify common classes of queries needed by applications that access these semantic networks. Next, we survey STRUQL, an existing query language that we adopted, which supports all of these classes of queries. We then describe the OQAFMA Querying Agent for the Foundational Model of Anatomy (OQAFMA), which provides an efficient implementation of a subset of STRUQL by pre-computing a variety of indices. We describe how OQAFMA leverages database optimization by converting STRUQL queries to SQL. We evaluate the flexibility and efficiency of our implementation using English queries written by anatomists. This evaluation verifies that OQAFMA provides flexible, efficient access to one such large semantic network, the Foundational Model of Anatomy, and suggests that OQAFMA could be an efficient query interface to other large biomedical knowledge bases, such as the Unified Medical Language System.