@Article{cmes.2023.028268,
AUTHOR = {Yuexin Huang, Suihuai Yu, Jianjie Chu, Zhaojing Su, Yangfan Cong, Hanyu Wang, Hao Fan},
TITLE = {Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {138},
YEAR = {2024},
NUMBER = {1},
PAGES = {167--200},
URL = {http://www.techscience.com/CMES/v138n1/54265},
ISSN = {1526-1506},
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 in the relation extraction module. Experimental comparison verified the effectiveness and accuracy
of the proposed knowledge extraction model. The case study demonstrated the feasibility of the knowledge
graph construction with real fragmentary porcelain data and showed the capability to provide designers with
interconnected and visualised design knowledge.},
DOI = {10.32604/cmes.2023.028268}
}