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作者:

Zhao, Xiaoli (Zhao, Xiaoli.) | Lin, Shaofu (Lin, Shaofu.) | Huang, Zhisheng (Huang, Zhisheng.)

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摘要:

The relationship of biomedical entity is the cornerstone of acquiring biomedical knowledge. It is of great significance to the construction of related databases in the biomedical field and the management of medical literature. How to quickly and accurately extract the required relationships of biomedical entity from massive unstructured literature is an important research. In order to improve accuracy, we use support vector machine (SVM) which is a machine learning algorithm based on feature vectors to extract relationships of entities. We extract the five main relationships in medical literature, including ISA, PART_OF, CAUSES, TREATS and DIAGNOSES. First of all, related topics are used to search medical literature from PubMed database, such as disease-drug, cause-disease. These documents are used as experimental data and then processed to form a corpus. In selection of features, the method of information gain is used to select the influential entities' own features and entities' context features. On this basis, semantic predicates are added as a feature to improve accuracy. The experimental results show that the accuracy of extraction is increased by 5%-10%. In the end, Resource Description Framework (RDF) is used to store extracted relationships from the corresponding documents, and it provides support for the subsequent retrieval of related documents.

关键词:

Relation extraction Semantic technology RDF Multi-classification SVM

作者机构:

  • [ 1 ] [Zhao, Xiaoli]Beijing Univ Technol, Coll Software, Beijing, Peoples R China
  • [ 2 ] [Lin, Shaofu]Beijing Univ Technol, Coll Software, Beijing, Peoples R China
  • [ 3 ] [Huang, Zhisheng]Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands

通讯作者信息:

  • [Zhao, Xiaoli]Beijing Univ Technol, Coll Software, Beijing, Peoples R China

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来源 :

HEALTH INFORMATION SCIENCE (HIS 2018)

ISSN: 0302-9743

年份: 2018

卷: 11148

页码: 17-24

语种: 英文

被引次数:

WoS核心集被引频次: 1

SCOPUS被引频次: 1

ESI高被引论文在榜: 0 展开所有

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