Knowledge Modelling and Active Learning in Manufacturing

TL;DR

This paper explores how combining semantic knowledge models like ontologies and knowledge graphs with active learning techniques can enhance manufacturing processes by effectively capturing, utilizing, and expanding domain knowledge despite limited labeled data.

Contribution

It introduces a framework integrating semantic technologies with active learning to improve knowledge modeling and data annotation in manufacturing.

Findings

Enhanced knowledge representation using ontologies and knowledge graphs.

Active learning reduces data labeling effort in manufacturing applications.

Framework supports multiple manufacturing use cases.

Abstract

The increasing digitalization of the manufacturing domain requires adequate knowledge modeling to capture relevant information. Ontologies and Knowledge Graphs provide means to model and relate a wide range of concepts, problems, and configurations. Both can be used to generate new knowledge through deductive inference and identify missing knowledge. While digitalization increases the amount of data available, much data is not labeled and cannot be directly used to train supervised machine learning models. Active learning can be used to identify the most informative data instances for which to obtain users' feedback, reduce friction, and maximize knowledge acquisition. By combining semantic technologies and active learning, multiple use cases in the manufacturing domain can be addressed taking advantage of the available knowledge and data.