ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics

Zhuoxun Zheng, Baifan Zhou, Dongzhuoran Zhou, Ahmet Soylu, Evgeny Kharlamov

Abstract: Data analytics including machine learning (ML) is essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad-hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable knowledge graph (KG), which represent KGs that rely on semantic technologies to formally encode ML knowledge and solutions. These KGs can be translated to executable scripts in a reusable and modularised fashion. The demo attendees will use our system to modify, integrate and create executable KGs via a graphic user interface, which offer a user-friendly way to understand, configure, reuse, and create data analytics pipelines.