Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (4): 84-93    DOI: 10.11925/infotech.2096-3467.2017.04.10
Orginal Article Current Issue | Archive | Adv Search |
Building Semantic Enrichment Framework for Scientific Literature Retrieval System
Xie Jing, Wang Jingdong, Wu Zhenxin(), Zhang Zhixiong, Wang Ying, Ye Zhifei
National Science Library, Chinese Academy of Sciences, Beijing 100190, China
Download: PDF (6590 KB)   HTML ( 4
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper aims to improve the scientific literature retrieval system with the help of semantic recognition and knowledge relationship computing. [Methods] First, we identified and extracted semantic objects from the scientific literature. Then, we calculated and established semantic relations among the objects using data-mining tools. Finally, we built semantic multidimensional index for these objects and relations, and then designed a new data organization model. [Results] The new system effectively identified the semantic information and improved the user experience. [Limitations] We need to expand the dataset used in this study and evaluate the new system in other areas. [Conclusions] The proposed system could retrieve more knowledge and indicate some future directions.

Key wordsSemantic Enrichment      Semantic Knowledge Organization      Semantic Relation Presentation      Multidimensional Index     
Received: 03 March 2017      Published: 24 May 2017
ZTFLH:  TP391  

Cite this article:

Xie Jing,Wang Jingdong,Wu Zhenxin,Zhang Zhixiong,Wang Ying,Ye Zhifei. Building Semantic Enrichment Framework for Scientific Literature Retrieval System. Data Analysis and Knowledge Discovery, 2017, 1(4): 84-93.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.04.10     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I4/84

SemRep
标记
文章PMID 来源
标记
文本
位置
术语类型 MeSH词表
术语代码
MeSH词表
标准术语
语义关系缩写 文本中
原始词汇
置信度 术语开始位置 术语结束位置
SE 00000000 tx 1 entity C1280519 Effectiveness qlco Effectiveness 1000 1 13
SE 00000000 tx 1 entity C0150143 Behavior mannagement topp behavioural managenment 964 18 39
SE 00000000 tx 1 entity C0149931 Migraine Disorders dsyn migraine 1000 44 51
SE 00000000 tx 1 entity C0001675 Adult aggp adult 888 56 60
SE 00000000 tx 1 entity C0030705 Patients podg patients 888 62 69
SE 00000000 tx 1 entity C0015607 family medicine
(field)
bmod family practice 901 81 95
SE 00000000 tx 1 entity C0442592 Clinic hcro,mnob clinics 901 97 103
SE 00000000 tx 1 entity C1514720 Randomized ftcn randomized 851 108 117
SE 00000000 tx 1 entity C0702113 Controlled ftcn controlled 851 119 128
SE 00000000 tx 1 entity C0008976 Clinical Trials resa trial 851 130 134
语义关系识别结果:
SE|00000000||tx|1|relation|3|1|C0149931|Migraine Disorders|dsyn|dsyn|||migraine|||1000|44|51|
PREP|PROCESS_OF||53|54|3|1|C0030705|Patients|podg,humn|humn||patients
888|62|69
索引字段 字段描述 字段功能
S 三元组主语 检索查询
P 三元组谓语 检索查询
O 三元组宾语 检索查询
S+P 主语与谓词拼接组合 分面揭示
P+O 谓词与宾语拼接组合 分面揭示
[1] U.S.National Library of Medicine. Semantic Knowledge Representation [EB/OL].[2016-01-13].
[2] Wikipedia. Knowledge Graph [EB/OL].[2016-02-10].
[3] Google Inside Search [EB/OL]. [2016-02-10].
[4] Wolframalpha. Computational Knowledge Engine [EB/OL].[2015-03-10].
[5] Kngine. The Most Intelligent Engine [EB/OL]. [2015-03-10].
[6] SindiceTech. Enterprise Knowledge Graphs [EB/OL]. [2015- 03-10].
[7] W3C Semantic Web. RDF [EB/OL].[2015-06-05].
[8] SindiceTech. FreeBase Distribution [EB/OL]. [2015-03-10].
[9] Apache Solr [EB/OL]. [2015-06-05].
[10] PubMed [EB/OL]. [2015-10-11].
[11] U.S.National Library of Medicine. SemRep [EB/OL].[2015-10-22].
[12] Del Corro L, Gemulla R.ClausIE: Clause-Open Information Extraction[C]//Proceedings of the the 22nd International Conference on World Wide Web. 2013:355-366.
[13] Merrill M D.Knowledge Objects[R]. USA: CBT Solutions, 1998: 1-11.
[14] U.S.National Library of Medicine. Unified Medical Language System (UMLS) [EB/OL].[2016-01-13]. .
[15] 王颖, 张智雄, 李传席, 等. 科技知识组织体系开放引擎系统的设计与实现[J]. 现代图书情报技术,2015 (10): 95-101.
[15] (Wang Ying, Zhang Zhixiong, Li Chuanxi, et al.The Design and Implementation of Open Engine System for Scientific & Technological Knowledge Organization Systems[J]. New Technology of Library and Information Service, 2015 (10): 95-101.)
[16] UMLS. Semantic Relationships [EB/OL].[2015-10-17].
[17] Chakraborty A, Munshi S, Mukhopadhyay D.Searching and Establishment of S-P-O Relationships for Linked RDF Graphs: An Adaptive Approach[C]//Proceedings of International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (CUBE). 2013.
[18] Matthews P H.Syntactic Relations:A Critical Survey[M]. University of CambridgePress, 2007: 3-10.
[19] U.S.National Library of Medicine. Medical Subject Headings (MeSH) [EB/OL].[2015-06-05].
[20] U.S.National Library of Medicine. MeSH Category Tree View [EB/OL].[2015-06-05].
[21] MetaMap - A Tool For Recognizing UMLS Concepts in Text [EB/OL]. [2015-06-20].
[22] The Stanford Natural Language Processing Group. Stanford Part of Speech Tagger [EB/OL].[2015-08-24].
[23] SPECIALIST dTagger [EB/OL]. [2015-06-20].
[24] 孙坦, 刘峥. 面向外文科技文献信息的知识组织体系建设思路[J]. 图书与情报, 2013 (1): 2-7.
doi: 10.3969/j.issn.1003-6938.2013.01.001
[24] (Sun Tan, Liu Zheng.Methodology Framework of Knowledge Organization System for Scientific & Technological Literature[J]. Library & Information, 2013(1): 2-7.)
doi: 10.3969/j.issn.1003-6938.2013.01.001
[25] Rindflesch T C, Fiszman M.The Interaction of Domain Knowledge and Linguistic Structure in Natural Language Pprocessing: Interpreting Hypernymic Propositions in Biomedical Text[J]. Journal of Biomedical Informatics, 2003, 36(6): 462-477.
doi: 10.1016/j.jbi.2003.11.003 pmid: 14759819
[1] Wang Hong, Shu Zhan, Gao Yinquan, Tian Wenhong. Analyzing Implicit Discourse Relation with Single Classifier and Multi-Task Network[J]. 数据分析与知识发现, 2021, 5(11): 80-88.
[2] Wu Yanwen, Cai Qiuting, Liu Zhi, Deng Yunze. Digital Resource Recommendation Based on Multi-Source Data and Scene Similarity Calculation[J]. 数据分析与知识发现, 2021, 5(11): 114-123.
[3] Li Zhenyu, Li Shuqing. Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups[J]. 数据分析与知识发现, 2021, 5(11): 124-134.
[4] Dong Miao, Su Zhongqi, Zhou Xiaobei, Lan Xue, Cui Zhigang, Cui Lei. Improving PubMedBERT for CID-Entity-Relation Classification Using Text-CNN[J]. 数据分析与知识发现, 2021, 5(11): 145-152.
[5] Yu Chuanming, Zhang Zhengang, Kong Lingge. Comparing Knowledge Graph Representation Models for Link Prediction[J]. 数据分析与知识发现, 2021, 5(11): 29-44.
[6] Ding Hao, Ai Wenhua, Hu Guangwei, Li Shuqing, Suo Wei. A Personalized Recommendation Model with Time Series Fluctuation of User Interest[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[7] Hua Bin, Wu Nuo, He Xin. Integrating Expert Reviews for Government Information Projects with Knowledge Fusion[J]. 数据分析与知识发现, 2021, 5(10): 124-136.
[8] Wang Yuan, Shi Kaize, Niu Zhendong. Position-Aware Stepwise Tagging Method for Triples Extraction of Entity-Relationship[J]. 数据分析与知识发现, 2021, 5(10): 71-80.
[9] Yang Chen, Chen Xiaohong, Wang Chuhan, Liu Tingting. Recommendation Strategy Based on Users’ Preferences for Fine-Grained Attributes[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[10] Dai Zhihong, Hao Xiaoling. Extracting Hypernym-Hyponym Relationship for Financial Market Applications[J]. 数据分析与知识发现, 2021, 5(10): 60-70.
[11] Wang Xuefeng, Ren Huichao, Liu Yuqin. Research on the Visualization Method of Drawing Technology Theme Map with Clusters [J]. 数据分析与知识发现, 0, (): 1-.
[12] Wang Yifan,Li Bo,Shi Hua,Miao Wei,Jiang Bin. Annotation Method for Extracting Entity Relationship from Ancient Chinese Works[J]. 数据分析与知识发现, 2021, 5(9): 63-74.
[13] Che Hongxin,Wang Tong,Wang Wei. Comparing Prediction Models for Prostate Cancer[J]. 数据分析与知识发现, 2021, 5(9): 107-114.
[14] Zhou Yang,Li Xuejun,Wang Donglei,Chen Fang,Peng Lijuan. Visualizing Knowledge Graph for Explosive Formula Design[J]. 数据分析与知识发现, 2021, 5(9): 42-53.
[15] Ma Jiangwei, Lv Xueqiang, You Xindong, Xiao Gang, Han Junmei. Extracting Relationship Among Military Domains with BERT and Relation Position Features[J]. 数据分析与知识发现, 2021, 5(8): 1-12.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn