Executing SPARQL Queries over the Web of Linked Data
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Linked Data
Query prediction with context models for populating personal linked data caches
Proceedings of the 23rd ACM conference on Hypertext and social media
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The emergence of a Web of Data 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, this paper proposes the use of smaller, pre-populated data caches whose contents are personalized to the needs of an individual user. We present an approach to a priori population of such caches with Linked Data harvested from the Web, seeded by a simple context model for each user, which is progressively enriched by executing a series of enrichment rules over Linked Data from the Web. Such caches can act as personal data stores supporting a range of different applications. A comprehensive user evaluation demonstrates that our approach can accurately predict the relevance of attributes added to the context model and the execution probability of queries based on these attributes, thereby optimizing the cache population process.