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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 Fraunhofer IAIS, Dresden, Germany ; 4 Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Cologne, Germany

Keyword(s): In-memory Computing, Distributed Processing, Hadoop Streaming, Ontology Parsing, SANSA Framework, Large-scale Datasets.

Abstract: Ontologies are widely used in many diverse disciplines, including but not limited to biology, geology, medicine, geography and scholarly communications. In order to understand the axiomatic structure of the ontologies in OWL/XML syntax, an OWL/XML parser is needed. Several research efforts offer such parsers; however, these parsers usually show severe limitations as the dataset size increases beyond a single machine’s capabilities. To meet increasing data requirements, we present a novel approach, i.e., DistOWL, for parsing large-scale OWL/XML datasets in a cost-effective and scalable manner. DistOWL is implemented using an in-memory and distributed framework, i.e., Apache Spark. While the application of the parser is rather generic, two use cases are presented for the usage of DistOWL. The Lehigh University Benchmark (LUBM) has been used for the evaluation of DistOWL. The preliminary results show that DistOWL provides a linear scale-up compared to prior centralized approaches.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Mohamed, H.; Fathalla, S.; Lehmann, J. and Jabeen, H. (2020). A Distributed Approach for Parsing Large-scale OWL Datasets. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KEOD, ISBN 978-989-758-474-9; ISSN 2184-3228, pages 227-234. DOI: 10.5220/0010138602270234

@conference{keod20,
author={Heba Mohamed. and Said Fathalla. and Jens Lehmann. and Hajira Jabeen.},
title={A Distributed Approach for Parsing Large-scale OWL Datasets},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KEOD,},
year={2020},
pages={227-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010138602270234},
isbn={978-989-758-474-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KEOD,
TI - A Distributed Approach for Parsing Large-scale OWL Datasets
SN - 978-989-758-474-9
IS - 2184-3228
AU - Mohamed, H.
AU - Fathalla, S.
AU - Lehmann, J.
AU - Jabeen, H.
PY - 2020
SP - 227
EP - 234
DO - 10.5220/0010138602270234

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