Semantic relatedness in DBpedia: A comparative and experimental
  assessment
Anna Formica Francesco Taglino
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.