Populating Personal Linked Data Caches using Context Models

Presented at: 21st International World Wide Web Conference (WWW2012)

by Olaf Hartig, Tom Heath

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.

Keywords: Context, Data Caches, Linked Data, Semantic Web


Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2012/poster/178


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