Chemical-text hybrid search engines.
As the amount of chemical literature increases, it is critical that researchers be enabled to accurately locate documents related to a particular aspect of a given compound. Existing solutions, based on text and chemical search engines alone, suffer from the inclusion of "false negative" and "false positive" results, and cannot accommodate diverse repertoire of formats currently available for chemical documents. To address these concerns, we developed an approach called Entity-Canonical Keyword Indexing (ECKI), which converts a chemical entity embedded in a data source into its canonical keyword representation prior to being indexed by text search engines. We implemented ECKI using Microsoft Office SharePoint Server Search, and the resultant hybrid search engine not only supported complex mixed chemical and keyword queries but also was applied to both intranet and Internet environments. We envision that the adoption of ECKI will empower researchers to pose more complex search questions that were not readily attainable previously and to obtain answers at much improved speed and accuracy.
Zhou
Yingyao
Y
Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, USA.
Zhou
Bin
B
Jiang
Shumei
S
King
Frederick J
FJ
D057225:Data Mining; D016208:Databases, Factual; D055808:Drug Discovery; D048088:Informatics; D020407:Internet; D057386:Search Engine / Q000379:methods*; D013997:Time Factors; D014584:User-Computer Interface