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

Chen, Zheyuan, Wan, Yuwei, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Valera Medina, Agustin ORCID: https://orcid.org/0000-0003-1580-7133 2024. A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling. Information Fusion 101 , 101985. 10.1016/j.inffus.2023.101985

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Publisher:	Elsevier
ISSN:	1566-2535
Date of First Compliant Deposit:	5 September 2023
Date of Acceptance:	23 August 2023
Last Modified:	08 Oct 2023 11:20
URI:	https://orca.cardiff.ac.uk/id/eprint/162232

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