Evaluating semantic relatedness of Web resources is still an open challenge. This paper focuses on knowledge-based methods, which represent an alternative to corpus-based approaches, and rely in general on the availability of knowledge graphs. In particular, we have selected 10 methods from the existing literature, that have been organized according to it adjacent resources, triple patterns, and triple weights-based methods. They have been implemented and evaluated by using DBpedia as reference RDF knowledge graph. Since DBpedia is continuously evolving, the experimental results provided by these methods in the literature are not comparable. For this reason, in this work, such methods have been experimented by running them all at once on the same DBpedia release and against 14 well-known golden datasets. On the basis of the correlation values with human judgment obtained according to the experimental results, weighting the RDF triples in combination with evaluating all the directed paths linking the compared resources is the best strategy in order to compute semantic relatedness in DBpedia.

本文评估Web资源的语义相关性，集中讨论了基于知识的方法作为结构型方法的替代，主要依赖于知识图谱的可用性，通过对10个已有方法的选择以及它们的组织方式（邻接资源、三元组模式和三元组权重方法）进行实现和评估，通过使用DBpedia作为RDF知识图谱的参考，为了得到可比较的实验结果，这些方法被同时应用于相同的DBpedia版本和14个著名的黄金数据集，并根据实验结果与人类判断之间的相关性值，得出权衡RDF三元组和评估比较资源间的所有直接路径的组合策略是计算DBpedia语义相关性的最佳策略。

DBpedia中的语义相关性：比较性和实验性评估