Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (5): 1-7    DOI: 10.11925/infotech.1003-3513.2015.05.01
Current Issue | Archive | Adv Search |
Review on Semantic Retrieval System for Scientific Literature
Wang Ying, Wu Zhenxin, Xie Jing
National Science Library, Chinese Academy of Sciences, Beijing 100190, China
Download: PDF(480 KB)   HTML  
Export: BibTeX | EndNote (RIS)      

[Objective] To investigate and summarize the typical semantic retrieval system for scientific literature. [Coverage] Use literatures related to semantic search retrieved by Web of Knowledge or Google Scholar, references and research reports of semantic retrieval systems. [Methods] This paper classifies current systems into four categories according to the degree of semantic processing, semantic query expansion retrieval system, concepts or entities centered retrieval system, relation-centered retrieval system, and retrieval system for knowledge discovery. [Results] The authors propose a basic framework of semantic retrieval systems for scientific literature, and summarize the features of semantic retrieval systems for scientific literature. [Limitations] Lack of performance evaluation of semantic retrieval system. [Conclusions] It provides a good guide for developing a semantic retrieval system for the scientific literature.

Key wordsSemantic search      Scientific literature      Text mining     
Received: 29 January 2015      Published: 11 June 2015
:  G250.76  

Cite this article:

Wang Ying, Wu Zhenxin, Xie Jing. Review on Semantic Retrieval System for Scientific Literature. New Technology of Library and Information Service, 2015, 31(5): 1-7.

URL:     OR

[1] Lu Z, Kim W, Wilbur W J. Evaluation of Query Expansion Using MeSH in PubMed [J]. Information Retrival, 2009, 12(1): 69-80.
[2] Griffon N, Chebil W, Rollin L, et al. Performance Evaluation of Unified Medical Language System®'s Synonyms Expansion to Query PubMed [J]. BMC Medical Informatics and Decision Making, 2012(12). doi: 10.1186/1472-6947-12-12.
[3] Matos S, Arrais J P, Maia-Rodrigues J, et al. Concept-based Query Expansion for Retrieving Gene Related Publications from MEDLINE [J]. BMC Bioinformatics, 2010(11). doi: 10. 1186/1471-2105-11-212.
[4] Bettembourg C, Diot C, Burgun A, et al. GO2PUB: Querying PubMed with Semantic Expansion of Gene Ontology Terms [J]. Journal of Biomedical Semantics, 2012, 3(1). doi: 10. 1186/2041-1480-3-7.
[5] Doms A, Schroeder M. GoPubMed: Exploring PubMed with the Gene Ontology [J]. Nucleic Acids Research, 2005 (33, Web Sever Issue): W783-W786.
[6] Kupershmidt I, Su Q J, Grewal A, et al. Ontology-based Meta-analysis of Global Collections of High-throughput Public Data [J]. PLoS One, 2010, 5(9). DOI: 10.1371/journal. pone.0013066.
[7] Nobata C, Sasaki Y, Okazaki N, et al. Semantic Search on Digital Document Repositories Based on Text Mining Results [C]. In: Proceedings of International Conferences on Digital Libraries and the Semantic Web 2009 (ICSD2009). 2009: 34-48.
[8] Coppernoll-Blach P. Quertle: The Conceptual Relationships Alternative Search Engine for PubMed [J]. Journal of Medical Library Association, 2011, 99(2): 176-177.
[9] Frijters R, Heupers B, van Beek P, et al. A Literature-based Keyword Enrichment Tool for Microarray Data Analysis [J]. Nucleic Acids Research, 2008 (36, Web Server Issue): W406-W410.
[10] Cheng D, Knox C, Young N, et al. PolySearch: A Web-based Text Mining System for Extracting Relationships Between Human Diseases, Genes, Mutations, Drugs and Metabolites [J]. Nucleic Acids Research, 2008 (36,Web Server Issue): W399-W405.
[11] Fleuren W W, Verhoeven S, Frijters R, et al. CoPub Update: CoPub 5.0 a Text Mining System to Answer Biological Questions [J]. Nucleic Acids Research, 2011 (39, Web Server Issue): W450-W454.
[12] Tsuruoka Y, Miwa M, Hamamoto K, et al. Discovering and Visualizing Indirect Associations Between Biomedical Concepts [J]. Bioinformatics, 2011, 27 (13): i111-i119.
[13] Ananiadou S. Advances of Biomedical Text Mining for Semantic Search [C]. In: Proceedings of the 2nd International Workshop on Web Science and Information Exchange in the Medical Web (MedEx'2011), Glasgow, UK. 2011.
[14] Wiley Online Library. The Smart Article: Introducing New and Enhanced Article Tools for Chemistry Content [EB/OL]. [2014-10-13]. homepage/new.htm.
[15] Lu Z. PubMed and Beyond: A Survey of Web Tools for Searching Biomedical Literature [J]. Database: The Journal of Biological Databases and Curation, 2011. doi: 10.1093/ database/baq036.
[16] Rebholz-Schuhmann D, Kirsch H, Arregui M, et al. EBIMed-text Crunching to Gather Facts for Proteins from Medline [J]. Bioinformatics, 2007, 23(2): e237-e244.
[17] Wei C H, Kao H Y, Lu Z. PubTator: A Web-based Text Mining Tool for Assisting Biocuration [J]. Nucleic Acids Research, 2013(41,Web Server Issue): W518-W522.
[18] Bizer C, Heath T, Berners-Lee T. Linked Data-The Story So Far [J]. International Journal on Semantic Web and Information Systems, 2009, 5(3): 1-22.
[19] Fogarolli A, Keizer J, Anibaldi S, et al. AGRIS-From a Bibliographic Database to a Semantic Data Service on Agricultural Research Information [J]. Agricultural Information Worldwide, 2010, 3(1): 26-30.
[20] Yagoda A. Elsevier Health Sciences: Smart Content Drives Smart Applications Using Knowledge in Healthcare [EB/OL]. [2014-11-19]. %24%24Meetings%24%242012-05-08_AlanYagoda.pdf.
[21] Doszkocs T. Semantic Search and Discovery [EB/OL]. [2014-10-13]. pdf.
[22] Schneider A, Landefeld R, Wermter J, et al. Do Users Appreciate Novel Interface Features for Literature Search? [C]. In: Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics. 2009: 2062-2067.
[23] Wang J, Cetindil I, Ji S, et al. Interactive and Fuzzy Search: A Dynamic Way to Explore MEDLINE [J]. Bioinformatics, 2010, 26(18): 2321-2327.

[1] Yanan Yang,Wenhui Zhao,Jian Zhang,Shen Tan,Beibei Zhang. Visualizing Policy Texts Based on Multi-View Collaboration[J]. 数据分析与知识发现, 2019, 3(6): 30-41.
[2] Mengji Zhang,Wanyu Du,Nan Zheng. Predicting Stock Trends Based on News Events[J]. 数据分析与知识发现, 2019, 3(5): 11-18.
[3] Hongxia Xu,Chunwang Li. Review of Knowledge Extraction of Scientific Literature[J]. 数据分析与知识发现, 2019, 3(3): 14-24.
[4] Ning Zhang,Lemin Yin,Lifeng He. Impacts of “Poster-Follower” Sentiment on Stock Market Performance[J]. 数据分析与知识发现, 2018, 2(6): 1-12.
[5] Jiaqi Wang,Junsheng Zhang,Xiaodong Qiao. Analyzing Representation and Semantic Links of Scientific Research Events[J]. 数据分析与知识发现, 2018, 2(5): 32-39.
[6] Xinyue Fan,Lei Cui. Using Text Mining to Discover Drug Side Effects: Case Study of PubMed[J]. 数据分析与知识发现, 2018, 2(3): 79-86.
[7] Qiangbing Wang,Chengzhi Zhang. Constructing Users Profiles with Content and Gesture Behaviors[J]. 数据分析与知识发现, 2017, 1(2): 80-86.
[8] Xiufang Xie,Xiaolin Zhang. Integrated Analysis and Visualization of Sci-Tech Roadmaps: Case Study of Renewable Energy[J]. 数据分析与知识发现, 2017, 1(1): 16-25.
[9] Yunfei Qi,Yuxiang Zhao,Qinghua Zhu. Linked Data for Mobile Visual Search System of Digital Library[J]. 数据分析与知识发现, 2017, 1(1): 81-90.
[10] Yao Zhaoxu,Ma Jing. Extracting Topic and Opinion from Microblog Posts with New Algorithm[J]. 现代图书情报技术, 2016, 32(7-8): 78-86.
[11] Lan Qiujun,Liu Wenxing,Li Weikang,Hu Xingye. Sentiment Analysis of Financial Forum Textual Message[J]. 现代图书情报技术, 2016, 32(4): 64-71.
[12] He Huixin,Liu Lijuan. A Scientific Research Object Labeling System Based on Active earning[J]. 现代图书情报技术, 2016, 32(3): 67-73.
[13] Qiang Bi, Jian Liu, Yulai Bao. A New Text Clustering Method Based on Semantic Similarity[J]. 数据分析与知识发现, 2016, 32(12): 9-16.
[14] Lin Yuanyuan,Zhan Hongfei,Yu Junhe,Li Changjiang,Zhang Fan. Using Product Reviews to Analyze Sentiment Fluctuation of Consumer[J]. 现代图书情报技术, 2016, 32(11): 44-53.
[15] Zhao Dongxiao,Wang Xiaoyue,Bai Rujiang,Liu Ziqiang. Semantic Text Mining Methodologies for Intelligence Analysis[J]. 现代图书情报技术, 2016, 32(10): 13-24.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938