Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring

Abstract

Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem, that is quality monitoring for welding in manufacturing industry. It is an important process accounting for production of millions of cars annually. The work is in line with our research of data-driven solutions that intends to replace the traditional costly quality monitoring. The paper tackles two challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods with literals on real industry data, with consideration of literals. This paper accompanies the full paper in in-use track and provides additional discussion on problem formulation, literal handling strategies, and included information in industrial KG construction.