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W3C recommendation, publishing open and machine-readable content on the Web has recently received a lot more attention, including from corporate and governmental bodies; there now exists a rich vein of heterogeneous RDF data published on the Web (the so-called “Web of Data”) accessible to all.
2008 •
In this paper we discuss the challenges of performing reasoning on large scale RDF datasets from the Web. We discuss issues and practical solutions relating to reasoning over web data using a rule-based approach to forward-chaining; in particular, we identify the problem of ontology hijacking: new ontologies published on the Web re-defining the semantics of existing concepts resident in other ontologies. Our solution introduces consideration of authoritative sources.
ABSTRACT Ontologies, in the OWL language, form the basis of the Semantic Web: they define concepts and relationships, and are thus the fundamental model for encoding information. As the Semantic Web has been more widely adopted, extremely large ontologies have begun to emerge, particularly in the life sciences. Reasoning about such ontologies requires scalability beyond that of current Description Logic-based reasoning tools.
2009 •
Abstract In this article the authors discuss the challenges of performing reasoning on large scale RDF datasets from the Web. Using ter-Horst's pD* fragment of OWL as a base, the authors compose a rule-based framework for application to web data: they argue their decisions using observations of undesirable examples taken directly from the Web.
European Semantic Web Symposium / Conference
Scalable and Parallel Reasoning in the Semantic Web2010 •
The current state of the art regarding scalable reasoning consists of programs that run on a single machine. When the amount of data is too large, or the logic is too complex, the computational resources of a single machine are not enough. We propose a distributed approach that overcomes these limitations and we sketch a research methodology. A distributed approach is challenging because of the skew in data distribution and the difficulty in partitioning Semantic Web data. We present initial results which are promising and suggest that the approach may be successful.
2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems
CEDAR: Efficient Reasoning for the Semantic Web2014 •
We present an extended version of the CEDAR taxonomic reasoner for large-scale ontologies. This new version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. The CEDAR system is based upon the OSF formalism. It is implemented on an entirely new architecture which includes several optimization techniques. We define a bidirectional map-ping between OSF graph structures and the Resource Description Framework (RDF) allowing a translation from OSF queries into SPARQL for retrieving instances from an ABox consisting of an RDF triplestore. We carried out comparative performance evaluation experiments using CEDAR as well as well-known Semantic Web reasoners (such as FaCT++, Pellet, HermiT, TrO WL, and RacerPro) on very large public ontologies. For the same queries on the same ontologies, the results achieved by CEDAR were compared to those obtained by all the other reasoners. The results of experiments show that CEDAR consistently performs on a par ...
2010 •
2015 IEEE 31st International Conference on Data Engineering
Reasoning on web data: Algorithms and performance2015 •
Active Media Technology
The Quest for Parallel Reasoning on the Semantic Web2009 •
Traditional reasoning tools for the Semantic Web cannot cope with Web scale data. One major direction to improve performance is parallelization. This article surveys existing studies, basic ideas and mechanisms for parallel reasoning, and introduces three major parallel applications on the Semantic Web: LarKC, MaRVIN, and Reasoning-Hadoop. Furthermore, this paper lays the ground for parallelizing unified search and reasoning at Web scale.
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