Abstract
OWL2 semantics are becoming increasingly popular for the real domain applications like
Gene engineering and health MIS. The present work identifies the research gap that negligible attention has been paid to the performance evaluation of Knowledge Base Systems
(KBS) using OWL2 semantics. To fulfil this identified research gap, an OWL2 benchmark for
the evaluation of KBS is proposed. The proposed benchmark addresses the foundational
blocks of an ontology benchmark i.e. data schema, workload and performance metrics. The
proposed benchmark is tested on memory based, file based, relational database and graph
based KBS for performance and scalability measures. The results show that the proposed
benchmark is able to evaluate the behaviour of different state of the art KBS on OWL2
semantics. On the basis of the results, the end users (i.e. domain expert) would be able to
select a suitable KBS appropriate for his domain.
Gene engineering and health MIS. The present work identifies the research gap that negligible attention has been paid to the performance evaluation of Knowledge Base Systems
(KBS) using OWL2 semantics. To fulfil this identified research gap, an OWL2 benchmark for
the evaluation of KBS is proposed. The proposed benchmark addresses the foundational
blocks of an ontology benchmark i.e. data schema, workload and performance metrics. The
proposed benchmark is tested on memory based, file based, relational database and graph
based KBS for performance and scalability measures. The results show that the proposed
benchmark is able to evaluate the behaviour of different state of the art KBS on OWL2
semantics. On the basis of the results, the end users (i.e. domain expert) would be able to
select a suitable KBS appropriate for his domain.
| Original language | English |
|---|---|
| Number of pages | 13 |
| Journal | PLoS ONE |
| DOIs | |
| Publication status | Published - 20 Jun 2017 |
Bibliographical note
Copyright © 2017 Khan et al. This is an openaccess article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.