Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
404078 | Knowledge-Based Systems | 2008 | 7 Pages |
Abstract
The biomedical literature is increasing rapidly, but most information retrieval systems for biomedicine are not what we really expect. In general, users suffer from exactly specifying what they want to the information retrieval systems, thereby getting back unsatisfied results from these systems. In this paper, we proposed PubMed Smarter that improves the effectiveness of information retrieval in PubMed. We built the word-relationship tree for biomedicine used to find implicit words. The implicit words are the ones correlative to a user query, and facilitate searching the PubMed database. Finally, we also used a fair assessment to evaluate the effectiveness of the system.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yin-Fu Huang, Chun-Hao Hsu,