東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Development of a conceptual graph-ba...

Huang, Huan.

Linked to FindBook

Google Book

Amazon

博客來

Development of a conceptual graph-based information retrieval model for medical question databases.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development of a conceptual graph-based information retrieval model for medical question databases./
Author:	Huang, Huan.
Description:	118 p.
Notes:	Source: Masters Abstracts International, Volume: 43-01, page: 0237.
Contained By:	Masters Abstracts International43-01.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1421143
ISBN:	0496258885

Development of a conceptual graph-based information retrieval model for medical question databases.
Huang, Huan.

Development of a conceptual graph-based information retrieval model for medical question databases. - 118 p.

Source: Masters Abstracts International, Volume: 43-01, page: 0237.

Thesis (M.S.)--University of Missouri - Columbia, 2004.

Evidence-based answers for family practice are concise answers to clinical questions from family physicians. Electronic availability of these answers could be used to improve patient care and lower costs if an automated computer system could be developed to access the information. To find the precise answer efficiently, the first step is to match the really asked questions with the prior well-built questions in a clinical question database. This match can direct the search to relevant and precise answers. In this thesis, we propose a new conceptual graph based information retrieval model. The clinical questions are represented as conceptual graphs, thus the information retrieval task can be expressed as a graph matching problem. To solve the problem that typical graph matching algorithms are of exponential complexity, we introduce a hybrid tree/ternary-tree approach of indexing and storage of conceptual graphs. An efficient algorithm to detect the largest common subgraph which uses the prior knowledge of the conceptual graph database is used to reduce the time taken during on-line retrieval. The semantic similarity measurement between two questions represented as conceptual graphs is also presented. Finally, we evaluate the retrieval performance of this conceptual graph based model against a clinical question database, and compare the performance with that of the classic vector space model.

ISBN: 0496258885Subjects--Topical Terms:

626642
Computer Science.

Development of a conceptual graph-based information retrieval model for medical question databases.
LDR:02305nmm 2200277 4500 001 1849038
005 20051202085225.5
008 130614s2004 eng d
020 $a 0496258885
035 $a (UnM)AAI1421143
035 $a AAI1421143
040 $a UnM $c UnM
100 1 $a Huang, Huan. $3 1674777
245 1 0 $a Development of a conceptual graph-based information retrieval model for medical question databases.
300 $a 118 p.
500 $a Source: Masters Abstracts International, Volume: 43-01, page: 0237.
500 $a Supervisor: Chi-Ren Shyu.
502 $a Thesis (M.S.)--University of Missouri - Columbia, 2004.
520 $a Evidence-based answers for family practice are concise answers to clinical questions from family physicians. Electronic availability of these answers could be used to improve patient care and lower costs if an automated computer system could be developed to access the information. To find the precise answer efficiently, the first step is to match the really asked questions with the prior well-built questions in a clinical question database. This match can direct the search to relevant and precise answers. In this thesis, we propose a new conceptual graph based information retrieval model. The clinical questions are represented as conceptual graphs, thus the information retrieval task can be expressed as a graph matching problem. To solve the problem that typical graph matching algorithms are of exponential complexity, we introduce a hybrid tree/ternary-tree approach of indexing and storage of conceptual graphs. An efficient algorithm to detect the largest common subgraph which uses the prior knowledge of the conceptual graph database is used to reduce the time taken during on-line retrieval. The semantic similarity measurement between two questions represented as conceptual graphs is also presented. Finally, we evaluate the retrieval performance of this conceptual graph based model against a clinical question database, and compare the performance with that of the classic vector space model.
590 $a School code: 0133.
650 4 $a Computer Science. $3 626642
650 4 $a Information Science. $3 1017528
690 $a 0984
690 $a 0723
710 2 0 $a University of Missouri - Columbia. $3 1017522
773 0 $t Masters Abstracts International $g 43-01.
790 1 0 $a Shyu, Chi-Ren, $e advisor
790 $a 0133
791 $a M.S.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1421143