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Alternative TitleMethod of Domain Ontology Concept Automatic Extraction Based on Deep Learning
王思丽1,2,3; 祝忠明1,2; 刘巍1,2; 杨恒1,2
Source Publication情报理论与实践
Contribution Rank1


Other Abstract

[Purpose/significance] Realize the automatic learning extraction of domain concepts and solve the primary basic tasks of domain ontology automation construction.[Method/process] The unsupervised learning method and the end-to-end recognition mode are the theoretical and technical foundations. Firstly, through the comparative analysis of the mainstream word embedding model, the paper designs an automatic generation method of domain text feature word embedding model based on Word2Vec and Skip-Gram. Secondly, the paper constructs a domain concept automatic extraction model named BLSTM-CRF based on self-attention mechanism, using annotated text in IOB format as input. Finally, the paper takes the field of resources and environment as an example to carry out experimental research and evaluation analysis. [Result/conclusion] The model can realize the automatic extraction of the domain concepts, and it also has certain robustness to the automatic identification of new domain concepts or terms. [Limitations] The accuracy of the model has not yet reached the top value and needs to be further optimized.

Keyword深度学习 领域本体 概念自动获取 词嵌入 自注意力
MOST Discipline Catalogue管理学 ; 管理学::图书情报与档案管理
Indexed ByCSSCI ; 中文核心期刊要目总览
Funding Project基于深度学习的领域本体自动构建方法研究###1 ; 基于深度学习的领域本体自动构建方法研究
Document Type期刊论文
First Author Affilication中国科学院文献情报中心
Recommended Citation
GB/T 7714
王思丽,祝忠明,刘巍,等. 基于深度学习的领域本体概念自动获取方法研究[J]. 情报理论与实践,2019(10):1-13.
APA 王思丽,祝忠明,刘巍,&杨恒.(2019).基于深度学习的领域本体概念自动获取方法研究.情报理论与实践(10),1-13.
MLA 王思丽,et al."基于深度学习的领域本体概念自动获取方法研究".情报理论与实践 .10(2019):1-13.
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