Article Title

A Semantic Network for Modeling Biological Knowledge in Multiple Databases

Authors

Jeffrey Stone, University of Vermont
Xindong Wu, University of Vermont
Marc Greenblatt, University of Vermont

Abstract

We have developed a semantic network of biological terminology to aid in the retrieval and integration of biological information from a variety of disparate information sources. Our semantic network strives to provide a categorization of biological concepts and relationships among these concepts. The semantic network will impart a knowledge structure through which computers can "reason " and draw conclusions about biological data objects and will provide a federated view of the many disparate databases of interest to biologists. In the development of our system, we have included the concepts from several established controlled vocabularies, chief among them being the National Library of Medicine's Unified Medical language System (UMLS). While the UMLS Metathesaurus provides an excellent controlled vocabulary, we have found their semantic network lacking in sufficient detail to be useful as a tool for categorization of biological concepts in databases. We would like to provide a categorization of concepts that provides finer detail than their semantic network without the considerable size and complexity of their Metathesaurus. Our complete semantic network consists of 183 semantic types and 69 relationships.

Recommended Citation

Stone, Jeffrey; Wu, Xindong; and Greenblatt, Marc (2004) "A Semantic Network for Modeling Biological Knowledge in Multiple Databases," Communications of the IIMA: Vol. 4: Iss. 4, Article 5.
DOI: https://doi.org/10.58729/1941-6687.1346
Available at: https://scholarworks.lib.csusb.edu/ciima/vol4/iss4/5