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The DKG framework uses a deep-learning algorithm to discover design-related knowledge from massive fragmentary data and constructs a knowledge graph for conceptual product design. The joint entity and relation extraction model is proposed to automatically extract design knowledge from massive unstructured data. The feasibility and high accuracy of the proposed design knowledge extraction model were demonstrated with experimental comparisons and the validation of the DKG in the case study of conceptual product design inspired by massive real data of porcelain.<\/jats:p>","DOI":"10.3233\/jifs-223100","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T11:41:22Z","timestamp":1672400482000},"page":"5333-5355","source":"Crossref","is-referenced-by-count":6,"title":["Design knowledge graph-aided conceptual product design approach based on joint entity and relation extraction"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2253-9141","authenticated-orcid":false,"given":"Yuexin","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi\u2019an, China"},{"name":"School of Industrial 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