Computing on-the-fly DBpedia property ranking

In many Semantic Web applications, having RDF predicates sorted by significance is of primarily importance to improve usability and performance. In this paper we focus on predicates available on DBpedia, the most important Semantic Web source of data counting 470 million english triples. Although there is plenty of work in literature dealing with ranking entities or RDF query results, none of them seem to specifically address the problem of computing predicate rank. We address the problem by associating to each DBPedia property (also known as predicates or attributes of RDF triples) a number of original features specifically designed to provide sort-by-importance quantitative measures, automatically computable from an online SPARQL endpoint or a RDF dataset. By computing those features on a number of entity properties, we created a learning set and tested the performance of a number of well-known learning-to-rank algorithms. Our first experimental results show that the approach is effective and fast.