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Semantically Enhanced Medical Information Retrieval System: A Tensor Factorization Based Approach

文献类型：期刊论文

作者Wang, Haolin1,2; Zhang, Qingpeng3,4; Yuan, Jiahu1,2
刊名IEEE ACCESS
出版日期2017
卷号5页码:7584-7593
关键词Information retrieval tensor factorization knowledge based systems
ISSN号2169-3536
DOI10.1109/ACCESS.2017.2698142
通讯作者Zhang, QP (reprint author), City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China. ; Zhang, QP (reprint author), City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China.
英文摘要Medical information retrieval plays an increasingly important role to help physicians and domain experts to better access medical-related knowledge and information, and support decision making. Integrating the medical knowledge bases has the potential to improve the information retrieval performance through incorporating medical domain knowledge for relevance assessment. However, this is not a trivial task due to the challenges to effectively utilize the domain knowledge in the medical knowledge bases. In this paper, we proposed a novel medical information retrieval system with a two-stage query expansion strategy, which is able to effectively model and incorporate the latent semantic associations to improve the performance. This system consists of two parts. First, we applied a heuristic approach to enhance the widely used pseudo relevance feedback method for more effective query expansion, through iteratively expanding the queries to boost the similarity score between queries and documents. Second, to improve the retrieval performance with structured knowledge bases, we presented a latent semantic relevance model based on tensor factorization to identify semantic association patterns under sparse settings. These identified patterns are then used as inference paths to trigger knowledge-based query expansion in medical information retrieval. Experiments with the TREC CDS 2014 data set: 1) showed that the performance of the proposed system is significantly better than the baseline system and the systems reported in TREC CDS 2014 conference, and is comparable with the state-of-the-art systems and 2) demonstrated the capability of tensor-based semantic enrichment methods for medical information retrieval tasks.
资助项目National Natural Science Foundation of China[71402157] ; National Natural Science Foundation of China[71672163] ; Guangdong Provincial Natural Science Foundation[2014A030313753] ; Research Grants Council of Hong Kong[T32-102/14N]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000403140800070
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://172.16.51.4:88/handle/2HOD01W0/289]  
专题高性能计算应用研究中心
通讯作者Zhang, Qingpeng
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China
4.City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Haolin,Zhang, Qingpeng,Yuan, Jiahu. Semantically Enhanced Medical Information Retrieval System: A Tensor Factorization Based Approach[J]. IEEE ACCESS,2017,5:7584-7593.
APA Wang, Haolin,Zhang, Qingpeng,&Yuan, Jiahu.(2017).Semantically Enhanced Medical Information Retrieval System: A Tensor Factorization Based Approach.IEEE ACCESS,5,7584-7593.
MLA Wang, Haolin,et al."Semantically Enhanced Medical Information Retrieval System: A Tensor Factorization Based Approach".IEEE ACCESS 5(2017):7584-7593.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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