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Author:

Kang, Yangyang (Kang, Yangyang.) | Li, Jianqiang (Li, Jianqiang.) (Scholars:李建强) | Yang, Jijiang (Yang, Jijiang.) | Wang, Qing (Wang, Qing.) | Sun, Zhihua (Sun, Zhihua.)

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EI Scopus

Abstract:

Medical search technologies are crucial to enable the user to rapidly and effectively discover useful information from massive medical and clinical data. Because of the complexity of medical terminology, traditional information search methods have not fully expressed the intention of the query request and explored the potential semantic knowledge in the document. In this paper, we propose a multi-analysis approach by considering the medical ontology as a semantic resource, which can excavate latent semantic information of a user's query request. In addition, we also recognize topics of medical documents to express text contents for providing support for calculating the similarity between query keywords and documents. Our experiments on PubMed medical article collections show that the semantic-based multi-analysis approach is feasible and efficient compared with other traditional approaches in medical retrieval. © 2017 IEEE.

Keyword:

Terminology Ontology Semantics Character recognition Semantic Web

Author Community:

  • [ 1 ] [Kang, Yangyang]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Li, Jianqiang]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Yang, Jijiang]Research Institute of Information Technology, Tsinghua University, Beijing, China
  • [ 4 ] [Wang, Qing]Research Institute of Information Technology, Tsinghua University, Beijing, China
  • [ 5 ] [Sun, Zhihua]Beijing Chaoyang District Maternal and Child Health Care Hospital, Beijing, China

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Year: 2017

Volume: 2017-January

Page: 1121-1126

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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