Yuexin Huang1,2, Suihuai Yu1, Jianjie Chu1,*, Zhaojing Su1,3, Yangfan Cong1, Hanyu Wang1, Hao Fan4
CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 167-200, 2024, DOI:10.32604/cmes.2023.028268
Abstract The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in
conceptual product design. This study proposes a novel method for acquiring design knowledge by combining
deep learning with knowledge graph. Specifically, the design knowledge acquisition method utilises the knowledge
extraction model to extract design-related entities and relations from fragmentary data, and further constructs
the knowledge graph to support design knowledge acquisition for conceptual product design. Moreover, the
knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the
entity extraction module, and uses multi-granularity information to overcome segmentation errors and polysemy
ambiguity… More >
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