Query-independent learning to rank for RDF entity search

Abstract

The amount of structured data is growing rapidly. Given a structured query that asks for some entities, the number of matching candidate results is often very high. The problem of ranking these results has gained attention. Because results in this setting equally and perfectly match the query, existing ranking approaches often use features that are independent of the query. A popular one is based on the notion of centrality that is derived via PageRank. In this paper, we adopt learning to rank approach to this structured query setting, provide a systematic categorization of query-independent features that can be used for that, and finally, discuss how to leverage information in access logs to automatically derive the training data needed for learning. In experiments using real-world datasets and human evaluation based on crowd sourcing, we show the superior performance of our approach over two relevant baselines. © 2012 Springer-Verlag.