NDLI: Knowledge-Enhanced Latent Semantic Indexing

Content Provider	Springer Nature Link
Author	Guo, David Berry, Michael W. Thompson, Bryan B. Bailin, Sidney
Copyright Year	2003
Abstract	Latent Semantic Indexing (LSI) is a popular information retrieval model for concept-based searching. As with many vector space IR models, LSI requires an existing term-document association structure such as a term-by-document matrix. The term-by-document matrix, constructed during document parsing, can only capture weighted vocabulary occurrence patterns in the documents. However, for many knowledge domains there are pre-existing semantic structures that could be used to organize and categorize information. The goals of this study are (i) to demonstrate how such semantic structures can be automatically incorporated into the LSI vector space model, and (ii) to measure the effect of these structures on query matching performance. The new approach, referred to as Knowledge-Enhanced LSI, is applied to documents in the OHSUMED medical abstracts collection using the semantic structures provided by the UMLS Semantic Network and MeSH. Results based on precision-recall data (11-point average precision values) indicate that a MeSH-enhanced search index is capable of delivering noticeable incremental performance gain (as much as 35%) over the original LSI for modest constraints on precision. This performance gain is achieved by replacing the original query with the MeSH heading extracted from the query text via regular expression matches.
Starting Page	225
Ending Page	250
Page Count	26
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	6
Issue Number	2
e-ISSN	15737659
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2003-01-01
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Data Structures, Cryptology and Information Theory Management of Computing and Information Systems
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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