属性是实体的重要组成部分,因此实体属性的获取是知识图谱构建的关键步骤。由哈尔滨工业大学社会计算与信息检索研究中心推出的开放域中文知识图谱《大词林》是通过从文本中自动挖掘实体及实体间的关系构建而成的,因此为《大词林》中缺少属性的实体添加属性也成为必须研究的问题之一。该文提出了一种解决方案: 基于贝叶斯网络的概率统计模型,通过上位词概念与属性之间的依赖关系和实体与上位词概念的依赖关系来自动地为《大词林》中没有属性的实体添加属性,并与相似度计算方法对比证明了其有效性,可大规模提高《大词林》的属性覆盖率。
Abstract
Attribute is an important part of entity, and the acquisition of entity attribute is a key step of knowledge graph construction. To complete the attributes related to entities in open domain Chinese knowledge graph "BigCilin",this paper proposes to employ the dependency relationships between 1) hypernym conception and attribute, and 2) entity and hypernym conception to add attributes to entities based on the Bayesian network. Compared with similarity measures, this method are proved for its validity in terms of significantly improving the attribute coverage of "BigCilin".
关键词
属性补全 /
知识图谱 /
大词林
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Key words
attribute completion /
knowledge graph /
BigCilin
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参考文献
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脚注
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基金
科技创新2030—“新一代人工智能”科技部重大项目(2018AAA0101901);国家自然科学基金(61772156,61976073);深圳市基础研究项目(JCYJ20180507183527919)
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