Semantic relatedness in DBpedia: A comparative and experimental assessment
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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.
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