ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics

• URL: http://arxiv.org/abs/2305.02966v1
• Date: Thu, 4 May 2023 16:10:22 GMT
• Title: ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
• Authors: Antonis Klironomos, Baifan Zhou, Zhipeng Tan, Zhuoxun Zheng, Gad-Elrab Mohamed, Heiko Paulheim, Evgeny Kharlamov
• Abstract summary: We present ExeKGLib, a Python library that allows users with minimal machine learning (ML) knowledge to build ML pipelines. We demonstrate the usage of ExeKGLib and compare it with conventional ML code to show its benefits.
• Score: 6.739841914490015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many machine learning (ML) libraries are accessible online for ML practitioners. Typical ML pipelines are complex and consist of a series of steps, each of them invoking several ML libraries. In this demo paper, we present ExeKGLib, a Python library that allows users with coding skills and minimal ML knowledge to build ML pipelines. ExeKGLib relies on knowledge graphs to improve the transparency and reusability of the built ML workflows, and to ensure that they are executable. We demonstrate the usage of ExeKGLib and compare it with conventional ML code to show its benefits.

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