ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics

arXiv (Cornell University)

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.

Authors 7

1. Antonis Klironomos

   University of Mannheim · Robert Bosch (Germany)

   Affiliation as printed

   Bosch Center for Artificial Intelligence , Germany

   University of Mannheim , Germany

2. Baifan Zhou

   University of Oslo

   Affiliation as printed

   University of Oslo , Norway

3. Zhipeng Tan Aachen

   RWTH Aachen University · Robert Bosch (Germany)

   Affiliation as printed

   Bosch Center for Artificial Intelligence , Germany

   RWTH Aachen , Germany

4. Zhuoxun Zheng

   OsloMet – Oslo Metropolitan University · Robert Bosch (Germany)

   Affiliation as printed

   Bosch Center for Artificial Intelligence , Germany

   Oslo Metropolitan University , Norway

5. Gad-Elrab Mohamed

   Robert Bosch (Germany)

   Affiliation as printed

   Bosch Center for Artificial Intelligence , Germany

6. Heiko Paulheim

   University of Mannheim

   Affiliation as printed

   University of Mannheim , Germany

7. Evgeny Kharlamov

   University of Oslo · Robert Bosch (Germany)

   Affiliation as printed

   Bosch Center for Artificial Intelligence , Germany

   University of Oslo , Norway