On Event-Driven Knowledge Graph Completion in Digital Factories

TL;DR

This paper explores how event logs in smart factories can be used to automate and improve the completion of industrial knowledge graphs through tailored machine learning techniques.

Contribution

It introduces a novel approach leveraging event logs for knowledge graph completion specifically designed for industrial environments.

Findings

Event logs improve knowledge graph accuracy in smart factories.

Machine learning tailored for industrial data enhances automation.

Encouraging results demonstrate practical applicability.

Abstract

Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e.g., in the form of knowledge graphs. Creation and maintenance of such knowledge is expensive and requires automation. In this work we show how machine learning that is specifically tailored towards industrial applications can help in knowledge graph completion. In particular, we show how knowledge completion can benefit from event logs that are common in smart factories. We evaluate this on the knowledge graph from a real world-inspired smart factory with encouraging results.