Context-Dependent OWL Reasoning in Sindice - Experiences and Lessons Learnt. Delbru, R., Tummarello, G., & Polleres, A. In Web Reasoning and Rule Systems – Fifth International Conference, RR2011, volume 6902, of Lecture Notes in Computer Science (LNCS), pages 46–60, Galway, Ireland, August, 2011. Springer. ![Context-Dependent OWL Reasoning in Sindice - Experiences and Lessons Learnt [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex The Sindice Semantic Web index provides search capabilities over today more than 220 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 learnt 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. First, we introduce our notion of context-dependent reasoning for RDF entities published on the Web according to the linked data principle. We then illustrate an efficient methodology to perform context-dependent RDFS and partial OWL inference based on a persistent TBox composed of a network of web ontologies. Finally we report performance evaluation results of our implementation underlying the Sindice web data index.
@inproceedings{delb-etal-2011RR,
Abstract = {The Sindice Semantic Web index provides search capabilities over today more than 220 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 learnt 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. First, we introduce our notion of context-dependent reasoning for RDF entities published on the Web according to the linked data principle. We then illustrate an efficient methodology to perform context-dependent RDFS and partial OWL inference based on a
persistent TBox composed of a network of web ontologies. Finally we report performance evaluation results of our implementation underlying the Sindice web data index.},
Address = {Galway, Ireland},
Author = {Renaud Delbru and Giovanni Tummarello and Axel Polleres},
Booktitle = {Web Reasoning and Rule Systems -- Fifth International Conference, RR2011},
Day = {29--30},
Month = AUG,
Pages = {46--60},
Publisher = {Springer},
Series = LNCS,
Title = {Context-Dependent {OWL} Reasoning in Sindice - Experiences and Lessons Learnt},
Type = CONF,
Url = {http://renaud.delbru.fr/doc/pub/rr2011-sindice.pdf},
Volume = 6902,
Year = 2011,
Bdsk-Url-1 = {http://renaud.delbru.fr/doc/pub/rr2011-sindice.pdf}}
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