A Pragmatic Approach for RDFS Reasoning over Large Scale Instance Data

Özacar, Tuğba; Öztürk, Övünç; Ünalır, Murat Osman

doi:10.1007/978-3-540-76890-6_43

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Tools and techniques exist as well for applying inference rules to semantically annotated data, given the context of a query (Özacar et al, 2007;Stuckenschmidt and Broekstra, 2005). Illoura combines key elements of both: it has been designed for application to complex biological data in native spatial context, but with the power and flexibility of semantic technologies for data management and querying.…”

Section: Resultsmentioning

confidence: 99%

Illoura™: a software tool for analysis, visualization and semantic querying of cellular and other spatial biological data

et al. 2009

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Section: Resultsmentioning

confidence: 99%

Illoura™: a software tool for analysis, visualization and semantic querying of cellular and other spatial biological data

et al. 2009

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“…However, this query rewriting technique incurs many UNION operations in the rewritten query, which ends up with a poor query performance. Ozacar et al (2007) cope with this problem by rewriting the entailment rules. However, rewiring entailment rules prevent us from using existing RDFS reasoners, which result in the degradation of the applicability of the scheme.…”

Section: Related Workmentioning

confidence: 99%

“…As can be easily imagined, the query performance in this case is deteriorated due to the additional task (Stuckenschmidt and Broekstra, 2005). Another approach (Ozacar et al, 2007) uses a dedicated predicates, e.g. hasInferredExtension, to have a compact representation of the same RDF data.…”

Section: Introductionmentioning

confidence: 99%

RDF packages: a scheme for efficient reasoning and querying over large‐scale RDF data

Ohsawa

Amagasa²,

Kitagawa³

2012

International Journal of Web Information Systems

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Purpose -The purpose of this paper is to improve the performance of querying and reasoning and querying over large-scale Resource Description Framework (RDF) data. When processing RDF(S) data, RDFS entailment is performed which often generates a large number of additional triples, which causes a poor performance. To deal with large-scale RDF data, it is important to develop a scheme which enables the processing of large RDF data in an efficient manner. Design/methodology/approach -The authors propose RDF packages, which is a space efficient format for RDF data. In an RDF package, a set of triples of the same class or triples having the same predicate are grouped into a dedicated node named Package. Any RDF data can be represented using RDF packages, and vice versa. Findings -It is found that using RDF packages can significantly reduce the size of RDF data, even after RDFS entailment. The authors experimentally evaluate the performance of the proposed scheme in terms of triple size, reasoning speed, and querying speed.Research limitations/implications -The proposed scheme is useful in processing RDF(S) data, but it needs further development to deal with an ontological language such as OWL. Originality/value -An important feature of the RDF packages is that, when performing RDFS reasoning, there is no need to modify either reasoning rules or reasoning engine; while other related schemes require reasoning rules or reasoning engine to be modified.

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“…However, this query rewriting technique incurs many UNION operations in the rewritten query, which ends up with a poor query performance. Ozacar et al [9] cope with this problem by rewriting the entailment rules. However, rewiring entailment rules prevent us from using existing RDFS reasoners, which result in the degradation of the applicability of the scheme.…”

Section: Related Workmentioning

confidence: 99%

RDF packages

Ohsawa

Amagasa

Kitagawa

2010

Proceedings of the 12th International Conference on Information Integration and Web-Based Applications &Amp; Services

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When querying RDF and RDFS data, for improving the performance, it is common to derive all triples according to RDFS entailment rules before query processing. An undesirable drawback of this approach is that a large number of triples are generated by the RDFS reasoning, and hence considerable amount of storage space is required if we materialize the RDFS closure. In this paper, we propose RDF packages, which is a time and space efficient format for RDF data. In an RDF package, a set of triples of the same class or triples having the same predicate are grouped into a dedicated node named Package. Using Packages, we can represent any metadata that can be expressed by RDF. An important feature of the RDF packages is that, when performing RDFS reasoning, the same rules can be applied without any modification, thereby allowing us to use existing RDFS reasoners. In this paper, we discuss the model of RDF packages and its rules, followed by the transformation between RDF and RDF packages. We also discuss the implementation RDF packages using an existing RDF framework. Finally, we demonstrate the performance of the proposed scheme in triple size, reasoning speed, and querying speed.

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A Pragmatic Approach for RDFS Reasoning over Large Scale Instance Data

Cited by 4 publications

References 8 publications

Illoura™: a software tool for analysis, visualization and semantic querying of cellular and other spatial biological data

Illoura™: a software tool for analysis, visualization and semantic querying of cellular and other spatial biological data

RDF packages: a scheme for efficient reasoning and querying over large‐scale RDF data

RDF packages

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