A Semantic similarity measure for predicates in Linked Data

Tracking #: 2104-3317

This paper is currently under review
Authors: 
Rajeev Irny
P Sreenivasa Kumar

Responsible editor: 
Jens Lehmann

Submission type: 
Full Paper
Abstract: 
Semantic similarity measures are used in several applications like link-predication, entity summarization, knowledge-base completion, clustering. In this paper, we propose a new semantic similarity measure called Predicate Semantic Similarity (PSS), specifically for predicates in linked data. Accounting for the apparent similarity between a pair of inverse predicates such as influences and influenced-by is one of the motivations for the work. We exploit implicit semantic information present in linked data to compute two quantities that capture context and (semantic) proximity aspects of a given pair of predicates, respectively. We build on the Normalized Semantic Web Distance (NSWD) and generalise it to predicates to take care of the context aspect. We also propose a novel measure based on neighbourhood-formation computation on a bipartite graph of predicates and classes to capture the proximity aspect. Thus we compute similarity along two semantic-facets namely context and proximity. A weighted sum of these gives us the new measure PSS. Through experiments, we evaluate the performance of PSS against the existing similarity measures including RDF2Vec. We find that including only one of context or proximity is insufficient. We create ground-truths to facilitate a thorough evaluation. The results indicate that PSS improves over all the existing measures for semantic similarity between predicates.
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