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陈文杰; 许海云
Source Publication情报理论与实践


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[Purpose/significance] Effectively integrate multi-dimensional data such as citation relations and text attributes in citation networks, and enhance the semantic association between document nodes, thus providing powerful support for data mining and knowledge discovery.[Method/process]propose a knowledge representation method for citation network. Firstly, method uses the neural network model to learn the k-order neighbor structure in the citation network. Then use the doc2vec model to learn text attributes such as titles and abstracts. Finally, a cross-learning mechanism based on vector sharing is presented for multi-data fusion.[Result/conclusion]Through test of CNKI citation data sets for the stem cell field, get a better performance in link prediction, prove the effectiveness and scientificity of the method.

Keyword引文网络 多元数据融合 知识表示 Word2vec Doc2vec
Indexed ByCSSCI
Document Type期刊论文
Corresponding Author陈文杰
First Author Affilication中国科学院文献情报中心
Corresponding Author Affilication中国科学院文献情报中心
Recommended Citation
GB/T 7714
陈文杰,许海云. 一种基于多元数据融合的引文网络知识表示方法[J]. 情报理论与实践,2019,10(3):173.
APA 陈文杰,&许海云.(2019).一种基于多元数据融合的引文网络知识表示方法.情报理论与实践,10(3),173.
MLA 陈文杰,et al."一种基于多元数据融合的引文网络知识表示方法".情报理论与实践 10.3(2019):173.
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