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Authors: Heba Mohamed 1 ; 2 ; Said Fathalla 1 ; 2 ; Jens Lehmann 3 ; 2 and Hajira Jabeen 4

Affiliations: 1 Faculty of Science, University of Alexandria, Alexandria, Egypt ; 2 Smart Data Analytics (SDA), University of Bonn, Bonn, Germany ; 3 NetMedia Department, Fraunhofer IAIS, Dresden Lab, Germany ; 4 Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Cologne, Germany

Keyword(s): Big Data, Distributed Computing, In-Memory Computation, Parallel Reasoning, OWL Horst Rules, OWL Axioms.

Abstract: With the tremendous increase in the volume of semantic data on the Web, reasoning over such an amount of data has become a challenging task. On the other hand, the traditional centralized approaches are no longer feasible for large-scale data due to the limitations of software and hardware resources. Therefore, horizontal scalability is desirable. We develop a scalable distributed approach for RDFS and OWL Horst Reasoning over large-scale OWL datasets. The eminent feature of our approach is that it combines an optimized execution strategy, pre-shuffling method, and duplication elimination strategy, thus achieving an efficient distributed reasoning mechanism. We implemented our approach as open-source in Apache Spark using Resilient Distributed Datasets (RDD) as a parallel programming model. As a use case, our approach is used by the SANSA framework for large-scale semantic reasoning over OWL datasets. The evaluation results have shown the strength of the proposed approach for both da ta and node scalability. (More)

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Paper citation in several formats:
Mohamed, H.; Fathalla, S.; Lehmann, J. and Jabeen, H. (2021). A Scalable Approach for Distributed Reasoning over Large-scale OWL Datasets. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KEOD; ISBN 978-989-758-533-3; ISSN 2184-3228, SciTePress, pages 51-60. DOI: 10.5220/0010656800003064

@conference{keod21,
author={Heba Mohamed. and Said Fathalla. and Jens Lehmann. and Hajira Jabeen.},
title={A Scalable Approach for Distributed Reasoning over Large-scale OWL Datasets},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KEOD},
year={2021},
pages={51-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010656800003064},
isbn={978-989-758-533-3},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KEOD
TI - A Scalable Approach for Distributed Reasoning over Large-scale OWL Datasets
SN - 978-989-758-533-3
IS - 2184-3228
AU - Mohamed, H.
AU - Fathalla, S.
AU - Lehmann, J.
AU - Jabeen, H.
PY - 2021
SP - 51
EP - 60
DO - 10.5220/0010656800003064
PB - SciTePress