A Scalable Approach for Distributed Reasoning over Large-scale OWL Datasets

Heba Mohamed, Heba Mohamed, Said Fathalla, Said Fathalla, Jens Lehmann, Jens Lehmann, Hajira Jabeen

2021

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 data and node scalability.

Paper Citation

in Harvard Style

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) - Volume 2: KEOD; ISBN 978-989-758-533-3, SciTePress, pages 51-60. DOI: 10.5220/0010656800003064

in Bibtex Style

@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) - Volume 2: KEOD},
year={2021},
pages={51-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010656800003064},
isbn={978-989-758-533-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD
TI - A Scalable Approach for Distributed Reasoning over Large-scale OWL Datasets
SN - 978-989-758-533-3
AU - Mohamed H.
AU - Fathalla S.
AU - Lehmann J.
AU - Jabeen H.
PY - 2021
SP - 51
EP - 60
DO - 10.5220/0010656800003064
PB - SciTePress