Query prediction with context models for populating personal linked data caches

Abstract

The emergence of a Web of Linked Data [2] enables new forms of application that require expressive query access, for which mature, Web-scale information retrieval techniques may not be suited. Rather than attempting to deliver expressive query capabilities at Web-scale, we propose the use of smaller, pre-populated data caches whose contents are personalized to the needs of an individual user. Such caches can act as personal data stores supporting a range of different applications. Furthermore, we discuss a user evaluation which demonstrates that our approach can accurately predict queries and their execution probability, thereby optimizing the cache population process. In this paper we formally introduce a strategy for predicting queries that can then be used to inform an a priori population of a personal cache of Linked Data harvested from Web. Based on a comprehensive user evaluation we demonstrate that our approach can accurately predict queries and their execution probability, thereby optimizing the cache population process.