Paper 202 (Research track)

Accuracy and Efficiency of Performance Metrics in Reasoning EL Ontologies

Author(s): Isa Guclu, Martin Kollingbaum, Jeff Pan

Abstract: This paper analyses existing performance prediction approaches for EL ontologies from the accuracy and the efficiency perspectives and proposes the core structural metrics for EL ontologies that provide efficiency of measuring metrics with a comparable prediction accuracy. The proposed approach is designed for simplicity and feasibility. The generalizability of the proposed metrics is validated through comparing their prediction accuracy with that of existing approaches by taking metric generation (time) cost into account.
The experiment results indicate that the core structural metrics provide a comparable prediction accuracy to existing metrics for (1) time prediction of ABox-intensive EL ontologies, (2) energy prediction of EL ontologies, and (3) time prediction of EL ontologies. In addition, it is shown that the proposed metrics are efficient and feasible by consuming less than the 0.1% of the time consumed by the existing metrics for measuring metrics of an ontology, which enables the adoption of the proposed approach for any processing environment, especially the resource-bounded mobile devices.

Keywords: Semantic Web; Ontology Reasoning; Performance Prediction; Random Forests

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