Mapping method for complex mechatronic equipment relationship data to contextual semantic-constrained knowledge graphs

Abstract

Complex mechatronic equipment’s complete life cycle information model includes multidimensional data on structure, qualities, behavior, and limitations. The data are diverse, derived from multiple sources, and include both streaming and non-streaming cyber-physical data. The product data system’s structured relational model, which is represented by the XBOM (X Bill of Material), is primarily where this data is stored. However, with the growing amount of equipment information, this relatively isolated and redundant architecture poses challenges. Within relational models, the growing intricacy of inner-outer key joins impedes effective data mining and retrieval for dynamic digital twin business applications. In response, this study proposes a knowledge graph transformation method that takes into account the semantic restrictions of the business application context to translate relational data to entities and semantic expressions. The method builds a knowledge context constraint meta-model by classifying data according to context semantics into one-hop entities, multi-hop entities, and abstract classes. Additionally, an ontology-based data extraction transformation method and an ontology-based mapping algorithm based on relational schema transformation are introduced in this article. This strategy significantly minimizes knowledge redundancy and enhances retrieval performance, as demonstrated by experimental verification through the development of a relational data mapping tool software.