Relation and Entropy Weight-Aware Knowledge Graph Embedding for Cloud Manufacturing

Cloud manufacturing has emerged as a service-oriented paradigm, in which knowledge graphs (KGs) play a crucial role in enabling modularization and on-demand servitization by converting unstructured resources into a structured graph representation and factual knowledge for manufacturing tasks. KG embedding converts entities and relations into a low-dimensional space while expressing rich semantics of high-dimensional KGs. Existing works just focus on translating relations surrounding the nodes of graphs, while ignoring the applicability for modeling and inferring multiple relation patterns in the manufacturing context. Furthermore, the relative importance of complex manufacturing relations among different dimensions in embedded procedures has been ignored, leading to unclear representation learning of complex relations and entities in KGs. To overcome this issue, in this article, a novel relation and entropy weight-aware embedding model is proposed, named TransCE. TransCE performs coordinated transformations in vector space by normalizing the distance to integrate coordination. An entropy-based weighting method is also proposed to represent complex relations and entities surrounding the edges of graph embedding and assign the weighted value of relations to support the score function. Extended experiments are performed on several datasets, and a manufacturing indicates that TransCE shows remarkable improvement relative to baseline models.