ExeKGLib: A Platform for Machine Learning Analytics based on Knowledge Graphs
This work addresses the problem for domain experts in science and engineering who lack ML expertise but need ML-based analytics, though it is incremental as it builds on existing knowledge graph and library concepts.
The authors tackled the challenge of enabling domain experts without machine learning expertise to build ML pipelines by developing ExeKGLib, a Python library with a graphical interface that uses knowledge graphs to encode ML knowledge, and demonstrated its usability and usefulness through real use cases.
Nowadays machine learning (ML) practitioners have access to numerous ML libraries available online. Such libraries can be used to create ML pipelines that consist of a series of steps where each step may invoke up to several ML libraries that are used for various data-driven analytical tasks. Development of high-quality ML pipelines is non-trivial; it requires training, ML expertise, and careful development of each step. At the same time, domain experts in science and engineering may not possess such ML expertise and training while they are in pressing need of ML-based analytics. In this paper, we present our ExeKGLib, a Python library enhanced with a graphical interface layer that allows users with minimal ML knowledge to build ML pipelines. This is achieved by relying on knowledge graphs that encode ML knowledge in simple terms accessible to non-ML experts. ExeKGLib also allows improving the transparency and reusability of the built ML workflows and ensures that they are executable. We show the usability and usefulness of ExeKGLib by presenting real use cases.