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

Urbani, Jacopo; Kotoulas, Spyros; Maassen, Jason; Harmelen, Frank van; Bal, Henri E.

doi:10.1007/978-3-642-13486-9_15

Cited by 116 publications

(120 citation statements)

References 16 publications

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“…Finally, we implement a prototype system to evaluate these optimizations. The experimental results show that the running time of our system is comparable with that of WebPIE [12] which is the state-of-the-art inference engine for OWL pD * fragment.…”

Section: Introductionmentioning

confidence: 86%

“…In [12] and [13], several optimizations are proposed that improve the performance of inference in OWL pD * fragment significantly. We list them as follows.…”

Section: Mapreduce Algorithm For Pd * Reasoningmentioning

confidence: 99%

“…-Sameas algorithm. For OWL pD * fragment, [12] uses the canonical representation to deal with the sameas rules. This method greatly reduces both the computation time and the space required.…”

Section: Mapreduce Algorithm For Pd * Reasoningmentioning

confidence: 99%

“…[12]) have proved that MapReduce is a very efficient framework to handle the computation of the closure containing up to 100 billion triples under pD * semantics. One may wonder if it is helpful to scalable reasoning in fuzzy pD * semantics.…”

Section: Introductionmentioning

confidence: 99%

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Large Scale Fuzzy pD * Reasoning Using MapReduce

Liu

Wang

et al. 2011

The Semantic Web – ISWC 2011

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Abstract. The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD * semantics and has shown promising results. In this paper, we move a step forward to consider scalable reasoning on top of semantic data under fuzzy pD * semantics (i.e., an extension of OWL pD * semantics with fuzzy vagueness). To the best of our knowledge, this is the first work to investigate how MapReduce can help to solve the scalability issue of fuzzy OWL reasoning. While most of the optimizations used by the existing MapReduce framework for pD * semantics are also applicable for fuzzy pD * semantics, unique challenges arise when we handle the fuzzy information. We identify these key challenges, and propose a solution for tackling each of them. Furthermore, we implement a prototype system for the evaluation purpose. The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for scalable reasoning in pD * semantics.

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Section: Introductionmentioning

confidence: 86%

“…In [12] and [13], several optimizations are proposed that improve the performance of inference in OWL pD * fragment significantly. We list them as follows.…”

Section: Mapreduce Algorithm For Pd * Reasoningmentioning

confidence: 99%

Section: Mapreduce Algorithm For Pd * Reasoningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Large Scale Fuzzy pD * Reasoning Using MapReduce

Liu

Wang

et al. 2011

The Semantic Web – ISWC 2011

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“…As pointed out by Horridge et al [8], universal quantification axioms, used in this strategy to emulate rdfs:range definitions, are disallowed in the computationally friendly OWL 2 EL profile. As illustrated by Urbani et al [14], using property restrictions to infer typing requires multiple joins between large sets of candidate entities, greatly complicating reasoning, particularly when dealing with large datasets. The results of Kang et al [9] also indicate that there may be performance penalties associated with this type of reasoning.…”

Section: Strategy Effectsmentioning

confidence: 99%

Ontology Design Pattern Property Specialisation Strategies

Hammar

2014

Lecture Notes in Computer Science

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Abstract. Ontology Design Patterns (ODPs) show potential in enabling simpler, faster, and more correct Ontology Engineering by laymen and experts. For ODP adoption to take off, improved tool support for ODP use in Ontology Engineering is required. This paper presents and evaluates the effects of strategies for object property specialisation in ODPs, and suggests tool improvements based on those strategies.

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Scalable RDF data compression with MapReduce

Urbani

Maassen²,

Drost³

et al. 2012

Concurrency and Computation

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The Semantic Web contains many billions of statements, which are released using the resource description framework (RDF) data model. To better handle these large amounts of data, high performance RDF applications must apply a compression technique. Unfortunately, because of the large input size, even this compression is challenging. In this paper, we propose a set of distributed MapReduce algorithms to efficiently compress and decompress a large amount of RDF data. Our approach uses a dictionary encoding technique that maintains the structure of the data. We highlight the problems of distributed data compression and describe the solutions that we propose. We have implemented a prototype using the Hadoop framework, and evaluate its performance. We show that our approach is able to efficiently compress a large amount of data and scales linearly on both input size and number of nodes. SCALABLE RDF DATA COMPRESSION WITH MAPREDUCE 25 make dictionary encoding a feasible technique on a very large input, a distributed implementation is required. To the best of our knowledge, no distributed approach exists to solve this problem.In this paper, we propose a technique to compress and decompress RDF statements using the MapReduce programming model [6]. Our approach uses a dictionary encoding technique that maintains the original structure of the data. This technique can be used by all RDF applications that need to efficiently process a large amount of data, such as RDF storage engines, network analysis tools, and reasoners.Our compression technique was essential in our recent work on Semantic Web inference engines, as it allowed us to reason directly on the compressed statements with a consequent increase of performance. As a result, we were able to reason over tens of billions of statements [7,8], advancing the current state of the art in the field significantly.The compression technique we present in this paper has the following: (i) performance that scales linearly; (ii) the ability to build a very large dictionary of hundreds of millions of entries; and (iii) the ability to handle load balancing issues with sampling and caching.This paper is structured as follows. In Section 2, we discuss the conventional approach to dictionary encoding and highlight the problems that arise. Sections 3 and 4 describe how we have implemented the data compression and decompression in MapReduce. Section 5 evaluates our approach, and Section 6 describes related work. Finally, we conclude and discuss future work in Section 7. DICTIONARY ENCODINGDictionary encoding is often used because of its simplicity. In our case, dictionary encoding has also the additional advantage that the compressed data can still be manipulated by the application. Traditional techniques such as gzip or bzip2 hide the original data so that reading without decompression is impossible. Algorithm 1 shows a sequential algorithm to compress and decompress RDF statements. The compression algorithm starts by initializing the dictionary table. The table has two columns, one tha...

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

Cited by 116 publications

References 16 publications

Large Scale Fuzzy pD * Reasoning Using MapReduce

Large Scale Fuzzy pD * Reasoning Using MapReduce

Ontology Design Pattern Property Specialisation Strategies

Scalable RDF data compression with MapReduce

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