Essie: A Concept-based Search Engine for Structured Biomedical Text

J Am Med Inform Assoc. 2007;14:253–263. DOI 10.1197/jamia.M2233. A rapidly increasing amount of biomedical information in electronic form is readily available to researchers, health care providers, and consumers. However, readily available does not mean conveniently accessible. The large volume of literature makes finding specific information ever more difficult. Development of effective search strategies is time consuming, requires experienced and educated searchers, well versed in biomedical terminology, and is beyond the capability of most consumers. Essie, a search engine developed and used at the National Library of Medicine, incorporates a number of strategies aimed at alleviating the need for sophisticated user queries. These strategies include a fine-grained tokenization algorithm that preserves punctuation, concept searching utilizing synonymy, and phrase searching based on the user’s query. This article is written by an employee of the US Government and is in the public domain. This article may be republished and distributed without penalty. The views expressed in this paper do not necessarily represent those of any U.S. government agency, but rather reflect the opinions of the authors. Affiliations of the authors: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, and Thoughtful Solutions, Inc., McLean, VA. The authors thank Dr. Alexa McCray, Dr. Deborah Zarin, and the research community at the Lister Hill Center for their support and encouragement. Correspondence and reprints: Nicholas C. Ide, Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894; e-mail: ide@nlm .nih.gov . Received for review: 7/31/2006; accepted for publication: 1/26/ 2007. This article describes related background work, the Essie search system, and the evaluation of that system. The Essie search system is described in detail, including its indexing strategy, query interpretation and expansion, and ranking of search results.