Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (9): 2510-2516.DOI: 10.11772/j.issn.1001-9081.2020111768

Special Issue: 人工智能

• Artificial intelligence • Previous Articles     Next Articles

Entity association query system based on enterprise knowledge graph construction

YU Dunhui1,2, WAN Peng1, WANG She3   

  1. 1. School of Computer and Information Engineering, Hubei University, Wuhan Hubei 430062, China;
    2. Hubei Provincial Engineering and Technology Research Center for Education Informationization(Hubei University), Wuhan Hubei 430062, China;
    3. School of Computer Science and Electronic Information Engineering, Wuhan City Polytechnic, Wuhan Hubei 430061, China
  • Received:2020-11-13 Revised:2021-01-14 Online:2021-09-10 Published:2021-05-08
  • Supported by:
    This work is partially supported by the National Key Research and Development Program of China (2018YFB1003801), the National Natural Science Foundation of China (61977021), the Technology Innovation Special Program of Hubei Province (Major Project) (2018ACA13).


余敦辉1,2, 万鹏1, 王社3   

  1. 1. 湖北大学 计算机与信息工程学院, 武汉 430062;
    2. 湖北省教育信息化工程技术研究中心(湖北大学), 武汉 430062;
    3. 武汉城市职业学院 计算机与电子信息工程学院, 武汉 430061
  • 通讯作者: 万鹏
  • 作者简介:余敦辉(1974-),男,湖北武汉人,教授,博士,CCF会员,主要研究方向:知识图谱、群体智能;万鹏(1996-),男,湖北荆州人,硕士研究生,主要研究方向:知识图谱;王社(1980-),男,湖北武汉人,讲师,硕士,主要研究方向:计算机网络。
  • 基金资助:

Abstract: Concerning the problem of low semantic relevance between nodes and low query efficiency in the current knowledge graph query, an entity-related query method was proposed,and then a knowledge gragh based enterprise query system was designed and implemented base on it. In this method, a four-layer filtering model was adopted. And firstly, the common paths of the target node were found through path search, so that the query nodes with a low degree of relevance were filtered out, and the filtering set was obtained. Then, the relevance degrees of the filtering set's attributes and relationships were calculated in the middle two layers, after that, the graph set filtering was performed based on the dynamic threshold. Finally, the entity relevance and relationship relevance scores was integrated and sorted to obtain the final query result. Experimental results on real enterprise data show that compared with traditional graph query algorithms such as Ness and NeMa, the proposed method reduces the query time by an average of 28.5%, and at the same time increases the filtering performance by an average of 29.6%, verifying that the algorithm can efficiently complete the task of query and display entities associated with the target.

Key words: knowledge graph, graph database, association relationship, query system, entity query

摘要: 针对目前知识图谱查询中节点之间语义关联性不高、查询效率低等问题,提出了一种实体关联的查询方法,然后以此为基础设计并实现了基于知识图谱的企业查询系统。所提查询方法采用四层过滤模型,首先通过路径搜索找到目标节点的公共路径,从而过滤掉关联程度较低的查询节点,得到过滤集合;然后在中间两层分别对过滤集合的属性和关系计算关联度,再基于动态阈值完成图集过滤;最后综合实体关联度和关系关联度得分并排序得到最终的查询结果。基于真实企业数据进行的实验结果表明,与Ness、NeMa等传统图查询方法相对比,所提方法在查询时间上平均降低了28.5%,同时在过滤性能上平均提高了29.6%,可见该方法能高效完成查询和展示与目标相关联实体的任务。

关键词: 知识图谱, 图数据库, 关联关系, 查询系统, 实体查询

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