Abstract

The Linked Open Data cloud has been increasing in popularity, with DBpedia as a first-class citizen in this cloud that has been widely adopted across many applications. Measuring similarity between resources and identifying their relatedness could be used for various applications such as item-based recommender systems. To this end, several similarity measures such as LDSD (Linked Data Semantic Distance) were proposed. However, some fundamental axioms for similarity measures such as “equal self-similarity”, “symmetry” or “minimality” are violated, and property similarities have been ignored. Moreover, none of the previous studies have provided a comparative study on other similarity measures. In this paper, we present a similarity measure, called Resim (Resource Similarity), based on top of a revised LDSD similarity measure. Resim aims to calculate the similarity of any resources in DBpedia …