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Knowledge-intensive conceptual retri...

University of Illinois at Chicago.

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Knowledge-intensive conceptual retrieval of biomedical literature.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Knowledge-intensive conceptual retrieval of biomedical literature./
Author:	Zhou, Wei.
Description:	94 p.
Notes:	Adviser: Clement Yu.
Contained By:	Dissertation Abstracts International69-09B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3327469
ISBN:	9780549798743

Knowledge-intensive conceptual retrieval of biomedical literature.
Zhou, Wei.

Knowledge-intensive conceptual retrieval of biomedical literature. - 94 p.

Adviser: Clement Yu.

Thesis (Ph.D.)--University of Illinois at Chicago, 2008.

Biologists search for biomedical literature on a daily basis. A concept-based approach via utilizing biomedical lexical resources was developed to improve the retrieval effectiveness of biomedical literature. A database of abbreviations and their definitions was created from MEDLINE.

ISBN: 9780549798743Subjects--Topical Terms:

626642
Computer Science.

Knowledge-intensive conceptual retrieval of biomedical literature.
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020 $a 9780549798743
035 $a (UMI)AAI3327469
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040 $a UMI $c UMI
100 1 $a Zhou, Wei. $3 1064868
245 1 0 $a Knowledge-intensive conceptual retrieval of biomedical literature.
300 $a 94 p.
500 $a Adviser: Clement Yu.
500 $a Source: Dissertation Abstracts International, Volume: 69-09, Section: B, page: 5555.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2008.
520 $a Biologists search for biomedical literature on a daily basis. A concept-based approach via utilizing biomedical lexical resources was developed to improve the retrieval effectiveness of biomedical literature. A database of abbreviations and their definitions was created from MEDLINE.
520 $a A number of techniques within this approach were developed and evaluated, including: (i) mapping query terms to MeSH terms and UMLS concepts; (ii) adding synonyms, hyponyms and hypernyms of query terms, (iii) adding lexical variants of gene symbols and implicitly related terms; (iv) utilizing two similarity functions, one measuring concept similarities between the query and the documents and another one measuring term similarities between the query and documents; (v) disambiguation of abbreviations; and (vi) utilizing abbreviated query terms to match related terms in documents.
520 $a This concept-based approach was applied to two tasks that address two important information needs of biologists. TASK I: Answering questions of biologists regarding the relationships between biological objects and biological processes; TASK II: Finding biological entities that satisfy certain conditions from texts.
520 $a The findings reveal that incorporating domain-specific knowledge by utilizing biomedical lexical resources in a concept-base approach significantly improved the retrieval effectiveness of our information retrieval system and it is critical to the success of such a biomedical IR system. The major challenge in our future work is how to utilize these biomedical domain knowledge (or resources) more appropriately.
590 $a School code: 0799.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a University of Illinois at Chicago. $3 1020478
773 0 $t Dissertation Abstracts International $g 69-09B.
790 $a 0799
790 1 0 $a Yu, Clement, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3327469