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数据分析与知识发现  2019, Vol. 3 Issue (1): 46-54    DOI: 10.11925/infotech.2096-3467.2018.1365
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基于深度学习的创新主题智能挖掘算法研究*
付常雷1,钱力1,2(),张华平3,赵华茗1,谢靖1,2
1中国科学院文献情报中心 北京 100190
2中国科学院大学图书情报与档案管理系 北京 100190
3北京理工大学计算机学院 北京 100081
Mining Innovative Topics Based on Deep Learning
Changlei Fu1,Li Qian1,2(),Huaping Zhang3,Huaming Zhao1,Jing Xie1,2
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Archives Management, University of Chinese Academy of Sciences, Beijing 100190, China
3School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China;
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摘要 

【目的】从海量的文本数据中挖掘创新主题。【方法】以学术知识图谱数据为基础, 根据知识点的“热度”、“新颖度”、“权威度”三维指标, 筛选出权重较高的作为创新种子, 然后根据知识图谱的路径对创新 种子进行知识关联计算, 计算结果输入一个用大量科技论文数据训练而成的深度学习模型, 从而生成创 新主题; 采用的模型为由双向LSTM层组成的Sequence to Sequence模型。【结果】以人工智能领域内中 文科技论文作为实验数据, 实验结果表明, 模型的挖掘结果经过专家人为判断验证, 创新效果平均值为6.52。【局限】目前知识图谱的知识丰富度和关联性有限、用于训练模型的训练集质量和体量还有待于进一步提升。【结论】本文模型实现了从文本数据中挖掘出创新主题, 但创新主题识别模型的整体水平仍然需要进一步完善优化。

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付常雷
钱力
张华平
赵华茗
谢靖
关键词 创新主题深度学习Seq2Seq智能挖掘    
Abstract

[Objective] This paper aims to identify innovative topics from massive volumes of texts. [Methods] First, we extracted knowledge points with heavier weights from the data of scholarly knowledge graph. Then, these knowledge points were labeled as innovative seeds from the perspectives of “popularity”, “novelty” and “authority”. Third, we computed the knowledge correlation of the innovative seeds. Finally, the results were input to a deep learning model trained by large amounts of sci-tech papers to generate innovative topics. Note: the model is sequence to sequence with Bi-LSTM. [Results] We used Chinese research papers on artificial intelligence as the experimental data and found the average innovation score of the retrieved topics was 6.52, which were evaluated by experts manually. [Limitations] At present, contents of the knowledge graph and the training datasets need to be improved. [Conclusions] The proposed model, which identifies innovative topics from scholarly papers, could be optimized in the future.

Key wordsInnovative Topic    Deep Learning    Seq2Seq    Intelligent Mining
收稿日期: 2018-12-04     
基金资助:*本文系中国科学院青年创新促进会(项目编号: 院1721)和创新构想话题生成机器人研发(项目编号: JW1701)的研究成果之一
引用本文:   
付常雷,钱力,张华平,赵华茗,谢靖. 基于深度学习的创新主题智能挖掘算法研究*[J]. 数据分析与知识发现, 2019, 3(1): 46-54.
Changlei Fu,Li Qian,Huaping Zhang,Huaming Zhao,Jing Xie. Mining Innovative Topics Based on Deep Learning. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.1365.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1365
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