Context-dependent OWL reasoning in sindice - experiences and lessons learnt

Authors:
Renaud Delbru;Giovanni Tummarello;Axel Polleres
Affiliations:
Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland and Siemens AG Österreich, Vienna, Austria
Venue:
RR'11 Proceedings of the 5th international conference on Web reasoning and rule systems
Year:
2011

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Abstract

The Sindice Semantic Web index provides search capabilities over 260 million documents. Reasoning over web data enables to make explicit what would otherwise be implicit knowledge: it adds value to the information and enables Sindice to ultimately be more competitive in terms of precision and recall. However, due to the scale and heterogeneity of web data, a reasoning engine for the Sindice system must (1) scale out through parallelisation over a cluster of machines; and (2) cope with unexpected data usage. In this paper, we report our experiences and lessons learned in building a large scale reasoning engine for Sindice. The reasoning approach has been deployed, used and improved since 2008 within Sindice and has enabled Sindice to reason over billions of triples.