Improving the ShExML engine through a profiling methodology

Tracking #: 3680-4894

This paper is currently under review
Authors: 
Herminio Garcia-Gonzalez

Responsible editor: 
Guest Editors KGC 2024

Submission type: 
Tool/System Report
Abstract: 
The ShExML language was born as a more user-friendly approach for knowledge graph construction. However, recent studies have highlighted that its companion engine suffers from serious performance issues. Thus, in this paper I undertake the optimisation of the engine by means of a profiling methodology. The improvements are then measured as part of a performance evaluation whose results are statistically analysed. Upon this analysis, the effectiveness of each proposed enhancement is discussed. As a direct result of this work the ShExML engine offers a much more optimised version which can cope better with users' demands.
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Under Review