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A distributed approach for parsing large-scale owl datasets

 
: Mohamed, H.; Fathalla, S.; Lehmann, J.; Jabeen, H.

:

Aveiro, D. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings. Vol.2: KEOD : November 2-4, 2020, web-based event
SciTePress, 2020
ISBN: 978-989-758-474-9
S.227-234
International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) <12, 2020, Online>
International Conference on Knowledge Engineering and Ontology Development (KEOD) <12, 2020, Online>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

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 inmemory 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.

: http://publica.fraunhofer.de/dokumente/N-639514.html