RDFS Reasoning and Query Answering on Top of DHTs

Kaoudi, Zoi; Miliaraki, Iris; Koubarakis, Manolis

doi:10.1007/978-3-540-88564-1_32

Cited by 57 publications

(72 citation statements)

References 18 publications

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“…4sr does not materialize any entailments in the assertion phase, therefore the import throughput we obtained when importing the LUBM datasets is similar to the figures reported by 4store developers, around 100kT/s for the cluster set-up and 114kT/s for the server set-up 6 .…”

Section: Lubm Scalability Evaluationsupporting

confidence: 71%

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The Design and Implementation of Minimal RDFS Backward Reasoning in 4store

Salvadores

Correndo

Harris³

et al. 2011

The Semanic Web: Research and Applications

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Abstract. This paper describes the design and implementation of Minimal RDFS semantics based on a backward chaining approach and implemented on a clustered RDF triple store. The system presented, called 4sr, uses 4store as base infrastructure. In order to achieve a highly scalable system we implemented the reasoning at the lowest level of the quad store, the bind operation. The bind operation runs concurrently in all the data slices allowing the reasoning to be processed in parallel among the cluster. Throughout this paper we provide detailed descriptions of the architecture, reasoning algorithms, and a scalability evaluation with the LUBM benchmark. 4sr is a stable tool available under a GNU GPL3 license and can be freely used and extended by the community 1 .

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Section: Lubm Scalability Evaluationsupporting

confidence: 71%

“…To date, there has been little progress on distributed backward chained reasoning for triple stores. [6] presented an implementation on top of DHTs using p2p techniques. So far, such solutions have not provided the community with tools, and recent investigations have concluded that due to load balancing issues they cannot scale [7].…”

Section: Related Workmentioning

confidence: 99%

The Design and Implementation of Minimal RDFS Backward Reasoning in 4store

Salvadores

Correndo

Harris³

et al. 2011

The Semanic Web: Research and Applications

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show abstract

“…Several proposed techniques are based on deterministic rendezvous-peers on top of distributed hashtables [1,2,4,9]. However, because of load-balancing problems due to the data distributions, these approaches do not scale [10].…”

Section: Related Workmentioning

confidence: 99%

OWL Reasoning with WebPIE: Calculating the Closure of 100 Billion Triples

Urbani

Kotoulas

Maassen

et al. 2010

Lecture Notes in Computer Science

115

117

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Abstract. In previous work we have shown that the MapReduce framework for distributed computation can be deployed for highly scalable inference over RDF graphs under the RDF Schema semantics. Unfortunately, several key optimizations that enabled the scalable RDFS inference do not generalize to the richer OWL semantics. In this paper we analyze these problems, and we propose solutions to overcome them. Our solutions allow distributed computation of the closure of an RDF graph under the OWL Horst semantics.We demonstrate the WebPIE inference engine, built on top of the Hadoop platform and deployed on a compute cluster of 64 machines. We have evaluated our approach using some real-world datasets (UniProt and LDSR, about 0.9-1.5 billion triples) and a synthetic benchmark (LUBM, up to 100 billion triples). Results show that our implementation is scalable and vastly outperforms current systems when comparing supported language expressivity, maximum data size and inference speed.

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“…An embarrassingly parallel algorithm is used in [27] for computing the RDFS closure. In [9], distributed hash tables were used for the computation of RDFS closure. Soma et al [23] investigate two partitioning approaches for parallel inferencing in OWL Horst.…”

Section: Related Workmentioning

confidence: 99%

Very Large Scale OWL Reasoning through Distributed Computation

Mutharaju

2012

The Semantic Web – ISWC 2012

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Abstract. Due to recent developments in reasoning algorithms of the various OWL profiles, the classification time for an ontology has come down drastically. For all of the popular reasoners, in order to process an ontology, an implicit assumption is that the ontology should fit in primary memory. The memory requirements for a reasoner are already quite high, and considering the ever increasing size of the data to be processed and the goal of making reasoning Web scale, this assumption becomes overly restrictive. In our work, we study several distributed classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.

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RDFS Reasoning and Query Answering on Top of DHTs

Cited by 57 publications

References 18 publications

The Design and Implementation of Minimal RDFS Backward Reasoning in 4store

The Design and Implementation of Minimal RDFS Backward Reasoning in 4store

OWL Reasoning with WebPIE: Calculating the Closure of 100 Billion Triples

Very Large Scale OWL Reasoning through Distributed Computation

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