中文摘要：

在近来出现的面向实体的搜索服务中,准确地预测实体间的相关程度是至关重要的。该文提出了一种基于实体的事实知识,即利用＂实体—属性—属性值＂（SPO）记录进行实体相关度计算的方法。该文通过基于属性和属性值的两步概率估计,将实体表示为一个属性值词的概率分布列,并通过比对两个实体共享的属性值词汇得出二者的相关度。实验表明,在用于面向实体搜索的相关实体排序问题上,该文方法达到了80.9%的平均top-5准确率,优于词袋方法和基于查询日志共现的方法。此外,该文通过定量分析,考察了不同领域的用户需求特性对实体相关度计算结果的影响。

英文摘要：

In the emerging entity-oriented search service, an accurate prediction of the relatedness between entities is essential. This paper proposes an approach to compute entity relatedness based on entities＇ fact knowledge, i. e. , subject-property-object （SPO） records. We adopt a two step estimation based on property and object, mapping an entity to a discrete distribution of the object words, and obtained two entities＇ relatedness by comparing the object words they share. On the related entity re-ranking problem in entity-oriented search, experimental results showed that our approach achieves 80.9 % top-5 precision on average, outperforming the bag-of-words and query log co-oc- currence based approaches. We also conducted quantitative analysis to find out how user demand in different domains affects the relatedness computation.