NPRank: Nexus based Predicate Ranking of Linked Data

Abstract

In the typical use case of browsing Linked Data in DBpedia, the user would find an average of 180 facts attached to each entity. These facts are ordered alphabetically based on predicates, but a logical ordering of these facts is a better option. In this article, we present a Nexus based predicate ranking of Linked Data facts named NPRank. The key idea of NPRank is, the importance of a predicate is directly proportional to its familiarity among its group called Nexus. NPRank is a language and endpoint independent model allowing seamless integration and querying of data from multiple endpoints. Nexus score generated to rank predicates also assists in fragmentation of large data and bring in more hidden data from the SPARQL endpoints. Our experiments, conducted with the ranking of the Linked Data facts, corresponding to most visited pages of Wikipedia; from 275 active SPARQL endpoints, achieves better performance than the state-of-the-art methods. © 2019 IEEE.