1 research outputs found
Similarity-based Browsing over Linked Open Data
An increasing amount of data is published on the Web according to the Linked
Open Data (LOD) principles. End users would like to browse these data in a
flexible manner. In this paper we focus on similarity-based browsing and we
introduce a novel method for computing the similarity between two entities of a
given RDF/S graph. The distinctive characteristics of the proposed metric is
that it is generic (it can be used to compare nodes of any kind), it takes into
account the neighborhoods of the nodes, and it is configurable (with respect to
the accuracy vs computational complexity tradeoff). We demonstrate the behavior
of the metric using examples from an application over LOD. Finally, we
generalize and elaborate on implementation approaches harmonized with the
distributed nature of LOD which can be used for computing the most similar
entities using neighborhood-based similarity metrics