SRank: Shortest paths as distance between nodes of a graph with application to RDF clustering

摘要

Similarity estimation between interconnected objects appears in many real-world applications and many domain-related measures have been proposed. This work proposes a new perspective on specifying the similarity between resources in linked data, and in general for vertices of a directed graph. More specifically, we compute a measure that says 'two objects are similar if they are connected by multiple small-length shortest path'. This general similarity measure, called SRank, is based on simple and intuitive shortest paths. For a given domain, SRank can be combined with other domain-specific similarity measures. The suggested model is evaluated in a clustering procedure on a sample data from DBPedia knowledge-base, where the class label of each resource is estimated and compared with the ground-truth class label. Experimental results show that SRank outperforms other similarity measures in terms of precision and recall rate.