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。
Abstract
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. Therefore, we treat this task as a supervised learning problem, testing SVM, Logistic Regression, Random Forest, and Learning to rank models.
关键词
实体相似度 /
监督学习 /
分类模型 /
集成学习
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Key words
entity similarity /
supervised learning /
classification model /
ensemble learning
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参考文献
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脚注
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基金
心血管疾病与肿瘤疾病中西医临床大数据处理分析与应用研究(2015AA020107)
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PDF(5261 KB)
李阳(1992—),硕士研究生,主要研究领域为自然语言处理。E-mail: marine1ly@163.com
高大启(1957—),教授,主要研究领域为模式级别与机器智能。E-mail: gaodaqi@ecasu.edu.cn
