SML-Bench – A Benchmarking Framework for Structured Machine Learning

Tracking #: 1603-2815

This paper is currently under review
Authors: 
Patrick Westphal
Lorenz Bühmann
Simon Bin
Hajira Jabeen
Jens Lehmann

Responsible editor: 
Guest Editors Benchmarking Linked Data 2017

Submission type: 
Tool/System Report
Abstract: 
The availability of structured data has increased significantly over the past decade and several approaches to learn from structured data have been proposed. The proposed logic-based, inductive learning methods are often conceptually similar, which would allow a comparison among them even if they stem from different research communities. However, so far no efforts were made to define an environment for running learning tasks on a variety of tools, covering multiple knowledge representation languages. With SML-Bench, we propose a benchmarking framework to run inductive learning tools from the ILP and semantic web communities on a selection of learning problems. In this paper, we present the foundations of SML-Bench, discuss the systematic selection of benchmarking datasets and learning problems, and showcase an actual benchmark run on the currently supported tools.
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