知识图谱中实体相似度计算研究

Research on Entities Similarity Calculation in Knowledge Graph

实体相似度的计算有诸多应用,例如,电商平台的相似商品推荐,医疗疗效分析中的相似病人组等。在知识图谱的实体相似度计算中,给出了每个实体的属性值,并对部分实体进行相似度的标注,要求能得到其他实体之间的相似度。该文把该问题归结为监督学习问题,提出一种通用的实体相似度计算方法,通过清洗噪声数据,对数值、列表以及文本等不同数据类型进行预处理,使用SVM,Logistic回归等分类模型、Random Forest等集成学习模型以及排序学习模型进行建模,得到了较好的结果。

Entities similarity is useful in many areas, such as recommendation system in E commerce platforms, and patients grouping in healthcare, etc. In our task of calculating the entity similarity in a given knowledge graph, the attributes of every entity is provided, and a sample of entity pairs are provided with their similarity score. There fore, we treat this task as a supervised learning problem, testing SVM, Logistic Regression, Random Forest, and Learning to rank models.