计算机与现代化

• 数据库与数据挖掘 • 上一篇    下一篇

知识图谱中的语义推理算法

  

  1. (西安邮电大学计算机学院,陕西西安710121)
  • 收稿日期:2017-03-23 出版日期:2017-12-25 发布日期:2017-12-26
  • 作者简介:郭琳(1993-),女,陕西西安人,西安邮电大学计算机学院硕士研究生,研究方向:语义Web; 翟社平(1971-),男,陕西眉县人,副教授,博士,研究方向:语义Web,智能Agent,Web服务及云计算; 高山(1993-),男,陕西眉县人,硕士研究生,研究方向:大数据存储。
  • 基金资助:
    陕西省教育厅科学研究计划项目(12JK0733); 陕西省社会科学基金资助项目(2016N008); 西安市社会科学规划基金资助项目(17X63); 西安邮电大学研究生创新基金资助项目(114-602080105)

Semantic Reasoning for Knowledge Graph

  1. (School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China)
  • Received:2017-03-23 Online:2017-12-25 Published:2017-12-26

摘要: 为了提高查询推理系统对高度动态和迅速扩张的知识图谱的适应性,向用户返回更加高效、实时、准确的结果,在对目前流行的知识图谱推理算法对比研究的基础上,提出一种基于语义张量的推理算法。通过筛选简化2个公开的网络规模的知识图谱,利用该算法进行训练测试。实验结果表明,该算法可提升效率,节省内存,提高推理精确度,能够适应高度动态化和不断演变的知识图谱数据信息,提高搜索引擎的智能化程度。

关键词: 知识图谱, 语义张量, 数据降维, 矩阵分解, 推理

Abstract: For the purpose of improving the adaptability of the query reasoning system to the highly dynamic and rapidly expanding knowledge graph returning more efficient, real-time and accurate results to the users, a reasoning algorithm based on semantic tensor is proposed through comparative study of the prevailing knowledge graph reasoning algorithms. Two open network knowledge graphs were selected and simplified to be tested and trained by this algorithm. The experimental results show that this algorithm can adapt to the highly dynamic and consistently evolving data and information of the knowledge graph, improve the accuracy of the reasoning, intelligence of the search engine, and save the memory.

Key words: knowledge graph, semantic tensor, dimension reduction, matrix factorization, reasoning

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