基于文献信息网络语义特征的相似性搜索

doi:10.11772/j.issn.1001-9081.2017112623

计算机应用 ›› 2018, Vol. 38 ›› Issue (5): 1327-1333.DOI: 10.11772/j.issn.1001-9081.2017112623

基于文献信息网络语义特征的相似性搜索

邱庆羽¹, 李婧², 全兵², 童超², 张利君³, 张海仙¹

1. 四川大学计算机学院, 成都 610065;
2. 中移(苏州)软件技术有限公司, 江苏苏州 215000;
3. 成都瑞贝英特信息技术有限公司, 成都 610041

收稿日期:2017-11-06 修回日期:2017-12-07 出版日期:2018-05-10 发布日期:2018-05-24
通讯作者: 张海仙
作者简介:邱庆羽(1994-),男,黑龙江牡丹江人,硕士研究生,CCF会员,主要研究方向:机器学习、数据挖掘;李婧(1988-),女,江苏无锡人,工程师,硕士,主要研究方向:大数据、人工智能;全兵(1988-),男,江西抚州人,工程师,硕士,主要研究方向:大数据、人工智能;童超(1988-),男,辽宁大连人,硕士,主要研究方向:大数据、机器学习;张利君(1978-),女,四川简阳人,工程师,硕士,主要研究方向:机器学习、数据挖掘;张海仙(1980-),女,河南南阳人,副教授,博士,主要研究方向:深度神经网络。
基金资助:
教育部-中国移动科研基金资助项目（MCM20160307）；四川省科技创新苗子工程项目和成都市科技局国际合作项目（2016-GH02-00048-HZ，2015-GH02-00041-HZ）。

Similarity search based on semantic features of bibliographic information network

QIU Qingyu¹, LI Jing², QUAN Bing², TONG Chao², ZHANG Lijun³, ZHANG Haixian¹

1. School of Computer Science, Sichuan University, Chengdu Sichuan 610065, China;
2. China Mobile(Suzhou) Software Technology Company Limited, Suzhou Jiangsu 215000, China;
3. Chengdu Ruibeiyingte Information Technology Company Limited, Chengdu Sichuan 610041, China

Received:2017-11-06 Revised:2017-12-07 Online:2018-05-10 Published:2018-05-24
Contact: 张海仙
Supported by:
This work is partially supported by the Ministry of Education-China Mobile Research Fund (MCM20160307), the Sichuan Province Science and Technology Innovation Talent Project and the International Cooperation Project of Chengdu Municipal Science and Technology Bureau (2016-GH02-00048-HZ, 2015-GH02-00041-HZ).

摘要/Abstract

摘要： 文献信息网络是典型的异构信息网络，基于其进行相似性搜索是图挖掘领域的一个研究热点。然而，现有的方法主要采用元路径或元结构的方式，并未考虑节点自身的语义特征，从而导致搜索结果出现偏差。对此，基于文献信息网络提出了一种基于向量的语义特征提取方法，并设计实现了基于向量的节点相似性计算方法VSim；此外，结合元路径设计了基于语义特征的相似性搜索算法VPSim；为提高算法的执行效率，针对文献网络数据的特点，设计了剪枝策略。通过在真实数据上的实验，验证了VSim对搜索语义特征相似实体的适用性，以及VPSim算法的有效性、高执行效率和高可扩展性。

关键词: 文献信息网络, 相似性搜索, 图挖掘, 元路径, 语义特征

Abstract: Bibliography information network is a typical heterogeneous information network and the similarity search based on it is a hot topic of graph mining. However, current methods mainly adopt meta path or meta structure to search similar objects, do not consider semantic features of node itself which leads to a deviation in the search results. To fill this gap, a vector-based semantic feature extraction method was proposed, and a vector-based node similarity calculation method called VSim was designed and implemented. In addition, a similarity search algorithm VPSim (Similarity computation Based on Vector and meta Path) based on semantic features was designed by combining the meta-paths. In order to improve the execution efficiency of the algorithm, a pruning strategy based on the characteristics of bibliographic network data was designed. Experiments on real-world data sets demonstrate that VSim is applicative for searching entities with similar semantic features and VPSim is effective, efficient and extensible.

Key words: bibliography information network, similarity search, graph mining, meta path, semantic features

中图分类号:

TP311

邱庆羽, 李婧, 全兵, 童超, 张利君, 张海仙. 基于文献信息网络语义特征的相似性搜索[J]. 计算机应用, 2018, 38(5): 1327-1333.

QIU Qingyu, LI Jing, QUAN Bing, TONG Chao, ZHANG Lijun, ZHANG Haixian. Similarity search based on semantic features of bibliographic information network[J]. Journal of Computer Applications, 2018, 38(5): 1327-1333.

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基于文献信息网络语义特征的相似性搜索

Similarity search based on semantic features of bibliographic information network

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