Large-scale reasoning with (semantic) data

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, we discuss scalable methods for nonmonotonic rule-based reasoning over Semantic Web Data, using MapReduce. This work is motivated by the recent unparalleled explosion of available data coming from the Web, sensor readings, databases, ontologies and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application or domain specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. Our results indicate that our method shows good scalability properties and is able to handle a benchmark dataset of 1 billion triples, bringing it on par with state-of-the-art methods for monotonic reasoning on the semantic web.

Original languageEnglish
Title of host publication4th International Conference on Web Intelligence, Mining and Semantics, WIMS 2014
PublisherAssociation for Computing Machinery (ACM)
ISBN (Print)9781450325387
Publication statusPublished - 2014
Event4th International Conference on Web Intelligence, Mining and Semantics - Thessaloniki, Greece
Duration: 2 Jun 20144 Jun 2014
Conference number: 4


Conference4th International Conference on Web Intelligence, Mining and Semantics
Abbreviated titleWIMS 2014


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