A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling

It has become progressively more evident that a single data source is unable to comprehensively capture the variability of a multi-faceted concept, such as product design, driving behaviour or human trust, which has diverse semantic orientations. Therefore, multi-faceted conceptual modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is frequently applied to cope with the high dimensionality and data heterogeneity. The consideration of intra-facets relationships is also indispensable. In this context, a knowledge graph (KG), which can aggregate the relationships of multiple aspects by semantic associations, was exploited to facilitate the multi-faceted conceptual modelling based on heterogeneous and semantic-rich data. Firstly, rules of fault mechanism are extracted from the existing domain knowledge repository, and node attributes are extracted from multi-sourced data. Through abstraction and tokenisation of existing knowledge repository and concept-centric data, rules of fault mechanism were symbolised and integrated with the node attributes, which served as the entities for the concept-centric knowledge graph (CKG). Subsequently, the transformation of process data to a stack of temporal graphs was conducted under the CKG backbone. Lastly, the graph convolutional network (GCN) model was applied to extract temporal and attribute correlation features from the graphs, and a temporal convolution network (TCN) was built for conceptual modelling using these features. The effectiveness of the proposed approach and the close synergy between the KG-supported approach and multi-faceted conceptual modelling is demonstrated and substantiated in a case study using real-world data.