Design knowledge is critical to creating ideas in the conceptual design stage of product development for innovation. Fragmentary design data, massive multidisciplinary knowledge call for the development of a novel knowledge acquisition approach for conceptual product design. This study proposes a Design Knowledge Graph-aided (DKG-aided) conceptual product design approach for knowledge acquisition and design process improvement. 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.
KotaS. and LeeC.L., General framework for configuration de-sign: part 1-methodology, Journal of Engineering Design4 (1993), 277–289. https://doi.org/10.1080/09544829308914787
2.
BloomfieldR., MazhariE., HawkinsJ. and SonY.J., Interopera-bility of manufacturing applications using the core manufactur-ing simulation data (CMSD) standard information model, Computers and Industrial Engineering62 (2012), 1065–1079. https://doi.org/10.1016/j.cie.2011.12.034.
3.
WuZ.Y., LiaoJ.H., SongW.Y., MaoH.L., HuangZ.F., LiX.X. and MaoH.Y., Semantic hyper-graph-based knowledge representation architecture for complex product development, Computers in Industry100 (2018), 43–56. https://doi.org/10.1016/j.compind.2018.04.008.
4.
HeY., HaoC.P., WangY.L., LiY.F., WangY., HuangL.Y. and TianX.C., An ontology-based method of knowledge model-ling for remanufacturing process planning, Journal of Cleaner Production258 (2020). https://doi.org/10.1016/j.jclepro.2020.120952.
5.
HuicongH., YingL., WenF.L. and XinG., A knowledge-based approach toward representation and archiving of aesthetic in-formation for product conceptual design, Journal of Compu-ting and Information Science in Engineering22 (2022). https://doi.org/10.1115/1.4053674.
6.
JiaJ., ZhangY. and SaadM., An approach to capturing and reus-ing tacit design knowledge using relational learning for knowledge graphs, Advanced Engineering Informatics51 (2022), 101505. https://doi.org/10.1016/j.aei.2021.101505.
7.
LiX., LyuM., WangZ., ChenC.-H. and ZhengP., Exploiting knowledge graphs in industrial products and services: A sur-vey of key aspects, challenges, and future perspectives, Computers in Industry129 (2021), 103449. https://doi.org/10.1016/j.compind.2021.103449.
8.
SunP. and GuL., Fuzzy knowledge graph system for artificial intelligence-based smart education, Journal of Intelligent & Fuzzy Systems40 (2021), 2929–2940..
9.
DiaoL., YangW., ZhuP., CaoG., SongS. and KongY., The research of clinical temporal knowledge graph based on deep learning, Journal of Intelligent & Fuzzy Systems41 (2021), 4265–4274. https://doi.org/10.3233/JIFS-189687.
10.
HaoJ., ZhaoL., Milisavljevic-SyedJ. and MingZ., Integrating and navigating engineering design decision-related knowledge using decision knowledge graph, Advanced Engineering In-formatics50 (2021), 1474–0346. https://doi.org/10.1016/j.aei.2021.101366.
11.
ZhangY., LiuX., JiaJ. and LuoX., Knowledge representation framework combining case-based reasoning with knowledge graphs for product design, Comput-Aided Des Appl17 (2019), 763–782. https://doi.org/10.14733/cadaps.2020.763-782.
12.
HuetA., PinquiéR., VéronP., MalletA. and SegondsF., CAC-DA: A knowledge graph for a context-aware cognitive designassistant, Computers in Industry125 (2021). https://doi.org/10.1016/j.compind.2020.103377.
13.
GuoL., YanF., LiT., et al., An automatic method for con-structing machining process knowledge base from knowledge graph, Robotics and Computer-Integrated Manufacturing73 (2022). https://doi.org/10.1016/j.rcim.2021.102222.
14.
GuoL., YanF., LuY., et al., An automatic machining process decision-making system based on knowledge graph, Interna-tional Journal of Computer Integrated Manufacturing12 (2021), 1348–1369. https://doi.org/10.1080/0951192X.2021.1972461.
15.
WangZ.X., ChenC.H., ZhengP., LiX.Y. and KhooL.P., A novel data-driven graph-based requirement elicitation frame-work in the smart product-service system context, Advanced Engineering Informatics42 (2019). https://doi.org/10.1016/j.aei.2019.100983
16.
LiX.Y., ChenC.H., ZhengP., WangZ.X., JiangZ.H. and JiangZ.X., A knowledge graph-aided concept-knowledge approach for evolutionary smart product-service system development, Journal of Mechanical Design142 (2020). https://doi.org/10.1115/1.4046807.
17.
ZhaoX., LiuY., XuY., YangY., LuoX. and MiaoC., Heteroge-neous star graph attention network for product attributes pre-diction, Advanced Engineering Informatics51 (2022), 101447. https://doi.org/10.1016/j.aei.2021.101447.
18.
AhnK. and ParkJ., Idle vehicle rebalancing in semiconductor fabrication using factorized graph neural network reinforce-ment learning, IEEE 58th Conference on Decision and Control (2019), 132–138. https://doi.org/10.1109/CDC40024.2019.9030245.
19.
KuhnM., FunkF., ZhangG.L. and FrankeJ., Blockchain-based application for the traceability of complex assembly structures, Journal of Manufacturing Systems59 (2021), 617–630. https://doi.org/10.1016/j.jmsy.2021.04.013.
20.
BaoQ.W., ZhaoG., YuY. and ZhengP., A node2vec-based graph embedding approach for unified assembly process in-formation modeling and workstep execution time prediction, Computers & Industrial Engineering163 (2022). https://doi.org/10.1016/j.cie.2021.107864.
21.
ZhouB., HuaB., GuX., LuY., PengT., ZhengY., ShenX. and BaoJ., An end-to-end tabular information-oriented causality event evolutionary knowledge graph for manufacturing docu-ments, Advanced Engineering Informatics50 (2021), 101441. https://doi.org/10.1016/j.aei.2021.101441.
22.
TranT.K., TaC.D.C. and PhanT.T., Simplified effective method for identifying semantic relations from a knowledge graph, Journal of Intelligent & Fuzzy Systems (2022). https://doi.org/10.3233/JIFS-219288.
23.
LiuZ., LiY., ZhaoL. and GuoP., Construction of intelligent query system for metro electromechanical equipment faults based on the knowledge graph, Journal of Intelligent & Fuzzy Systems41 (2021), 4351–4368. https://doi.org/10.3233/JIFS-189695.
24.
PersadU., LangdonP.M. and ClarksonP.J., Inclusive design evaluation and the capability-demand relationship, Designing Accessible Technology (2006). https://doi.org/10.1007/1-84628-365-5_18.
25.
WangY.H., YuS.H., ChengF.M., LiuZ., ChenD.K., ChuJ.J., MaN. and ChenY.H., A design knowledge management model for civil aircraft cabin based on markov mogic networks, International Journal of Computer Integrated Manufacturing33 (2020), 732–750. https://doi.org/10.1080/0951192x.2019.1699253.
26.
ChenX.J., JiaS.B. and XiangY., A review: knowledge reasoning over knowledge graph, Expert Systems with Applications141 (2020). https://doi.org/10.1016/j.eswa.2019.112948.
27.
LiH.T., WangY., ZhangS.H., SongY.Q. and QuH.M., KG4Vis: a knowledge graph-Based approach for visualization recom-mendation, IEEE Transactions on Visualization and Computer Graphics28 (2022), 195–205. https://doi.org/10.1109/Tvcg.2021.3114863.
28.
WangH.W., ZhaoM., XieX., LiW.J. and GuoM.Y., Knowledge graph convolutional networks for recommender systems, Pro-ceedings of the World Wide Web Conference (2019), 3307–3313. https://doi.org/10.1145/3308558.3313417.
29.
HearstM.A., DumaisS.T., OsunaE., PlattJ. and ScholkopfB., Support vector machines, IEEE Intelligent Systems and their Applications13 (1998), 18–28. https://doi.org/https://doi.org/10.1109/5254.708428.
30.
RabinerL. and JuangB., An introduction to hidden Markov mod-els, IEEE ASSP Magazine3 (1986), 4–16. https://doi.org/https://doi.org/10.1109/MASSP.1986.1165342.
31.
WallachH.M., Conditional random fields: an introduction, Technical Reports (2004), 22.
32.
DouJ.H., QinJ.Y., JinZ.X. and LiZ., Knowledge graph based on domain ontology and natural language processing technol-ogy for chinese intangible cultural heritage, Journal of Visual Languages and Computing48 (2018), 19–28. https://doi.org/10.1016/j.jvlc.2018.06.005.
33.
LiL.F., WangP., YanJ., WangY., LiS.M., JiangJ.P., SunZ., TangB.Z., ChangT.H., WangS.H. and LiuY.T., Real-world data medical knowledge graph: construction and applications, Artificial Intelligence in Medicine103 (2020). https://doi.org/10.1016/j.artmed.2020.101817.
34.
DevlinJ., ChangM.W., LeeK. and ToutanovaK., BERT: pre-training of deep bidirectional transformers for language under-standing, arXiv (2018). https://doi.org/10.48550/arXiv.1810.04805.
35.
YangZ.L., DaiZ.H., YangY.M., CarbonellJ., Salakhutdi-novR. and LeQ.V., XLNet: generalized autoregressive pretraining for language understanding, Proceedings of the Advances in Neural Information Processing Systems (2019). https://doi.org/10.48550/arXiv.1906.08237.
36.
RonnqvistS., SchenkN. and ChiarcosC., Arecurrent neural model with attention for the recognition of chinese implicit discourse relations, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (2017), 256–262. https://doi.org/10.18653/v1/P17-2040.
37.
LiZ.R., DingN., LiuZ.Y., ZhengH.T. and ShenY., Chinese relation extraction with multi-grained information and external linguistic knowledge, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019), 4377–4386. https://doi.org/10.18653/v1/P19-1430.
38.
ZhengS.C., WangF., BaoH.Y., HaoY.X., ZhouP. and XuB., Joint extraction of entities and relations based on a novel tag-ging scheme, arXiv (2017). https://doi.org/10.48550/arXiv.1706.05075.
ZengX.R., ZengD.J., HeS.Z., LiuK. and ZhaoJ., Extracting relational facts by an end-to-end neural model with copy mechanism, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (2018), 506–514. https://doi.org/10.18653/v1/P18-1047.
41.
ZengX.R., HeS.Z., ZengD.J., LiuK., LiuS.P. and ZhaoJ., Learning the extraction order of multiple relational facts in a sentence with reinforcement learning, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Pro-cessing and the 9th International Joint Conference on Natural Language Processing (2019), 367–377. https://doi.org/10.18653/v1/D19-1035.
42.
DuB., LinB., ZhangC.M., DongB.T. and ZhangW.D., Safe deepreinforcement learning-based adaptive control for USV interceptionmission, Ocean Engineering246 (2022). https://doi.org/https://doi.org/10.1016/j.oceaneng.2021.110477>.
43.
DuB., LuY., ChengX., ZhangW. and ZouX., The object-orienteddynamic task assignment for unmanned surface vessels, Engineering Applications of Artificial Intelligence106 (2021), 104476. https://doi.org/https://doi.org/10.1016/j.engappai.2021.104476>.
44.
FuT.J., LiP.H. and MaW.Y., GraphRel: modeling text as rela-tional graphs for joint entity and relation extraction, Proceedings of the 57th Annual Meeting of the Association for Com-putational Linguistics (2019), 1409–1418. https://doi.org/10.18653/v1/P19-1136.
45.
LiX.Y., YinF., SunZ.J., LiX.Y., YuanA., ChaiD., ZhouM.X. and LiJ.W., Entity-relation extraction as multi-turn question answering, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019), 1340–1350. https://doi.org/10.48550/arXiv.1905.05529.
46.
WeiZ.P., SuJ.L., WangY., TianY. and ChangY., A novel cas-cade binary tagging framework for relational triple extraction, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020), 1476–1488. https://doi.org/10.48550/arXiv.1909.03227.
47.
KimK.Y., ManleyD.G. and YangH., Ontology-based assembly design and information sharing for collaborative product de-velopment, Computer-Aided Design38 (2006), 1233–1250. https://doi.org/10.1016/j.cad.2006.08.004.
48.
DoerrM., The CIDOC conceptual reference module: an onto-logical approach to semantic interoperability of metadata, AI Magazine24 (2003), 75–75. https://doi.org/https://doi.org/10.1609/aimag.v24i3.1720.
49.
LuoS.J. and DongY.N., Integration and management method of cultural artifacts knowledge for cultural creative design, Computer Integrated Manufacturing Systems24 (2018), 964–977.
50.
LiangB., van der WalC.N., XieK., et al., Mapping the knowledge domain of soft computing applications for emer-gency evacuation studies: A scientometric analysis and critical review, Safety Science158 (2023), 105955. https://doi.org/10.1016/j.ssci.2022.105955.
51.
ClarkK., MinhT.L., QuocV.L. and ChristopherD.M., ELEC-TRA: pre-training text encoders as discriminators rather than generators, Proceedings of the 10th International Conference on Learning Representations (2020). https://doi.org/10.48550/arXiv.2003.10555.
LiS.J., HeW., ShiY.B., JiangW.B., LiangH.J., JiangY., ZhangY., LyuY.J. and ZhuY., DuIE: a large-scale chinese dataset for information extraction, International Conference on Natu-ral Language Processing and Chinese Computing (2019), 791–800. https://doi.org/10.1007/978-3-030-32236-6_72.
55.
BekoulisG., DeleuJ., DemeesterT. and DevelderC., Joint entity recognition and relation extraction as a multi-head selection problem, Expert Systems with Applications114 (2018), 34–45. https://doi.org/https://doi.org/10.1016/j.eswa.2018.07.032.
MikolovT., SutskeverL., ChenK., CorradoG.S. and DeanJ., Distributed representations of words and phrasesand their compositionality, Advances inneural information processing systems26 (2013). https://doi.org/10.48550/arXiv.1310.4546.
