Semantic relatedness in DBpedia: A comparative and experimental assessment

TL;DR

This paper evaluates knowledge-based methods for semantic relatedness in DBpedia, comparing 10 approaches on a common dataset to identify the most effective strategy for aligning computational measures with human judgment.

Contribution

It provides a comprehensive experimental comparison of 10 knowledge-based semantic relatedness methods on DBpedia, highlighting the best strategies for accurate measurement.

Findings

Weighting RDF triples improves relatedness accuracy.

Evaluating all directed paths yields better correlation with human judgment.

Running all methods on the same DBpedia release ensures comparability.

Abstract

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.