SEMANTIC NETWORKS FOR ENGINEERING DESIGN: A SURVEY

There have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.

[1]  P. Bertola.  Design as a Knowledge Agent , 2001 .

[2]  John A. Barnden,et al.  Semantic Networks , 1998, Encyclopedia of Social Network Analysis and Mining.

[3]  Sougata Mukherjea,et al.  Information retrieval and knowledge discovery utilizing a biomedical patent semantic Web , 2005, IEEE Transactions on Knowledge and Data Engineering.

[4]  Jonghwa Kim,et al.  Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework , 2015 .

[5]  Margaret A. Boden,et al.  The Creative Mind - Myths and Mechanisms (2. ed.) , 2003 .

[6]  W. B. Lee,et al.  A methodology for building a semantically annotated multi-faceted ontology for product family modelling , 2011, Adv. Eng. Informatics.

[7]  David C. Anderson,et al.  SEMANTICS-BASED DESIGN KNOWLEDGE ANNOTATION AND RETRIEVAL , 2005 .

[8]  Kristin L. Wood,et al.  TechNet: Technology semantic network based on patent data , 2019, Expert Syst. Appl..

[9]  Catherine Havasi,et al.  Representing General Relational Knowledge in ConceptNet 5 , 2012, LREC.

[10]  Xinlei Chen,et al.  Never-Ending Learning , 2012, ECAI.

[11]  Jonathan Cagan,et al.  Crowdsourcing inspiration: Using crowd generated inspirational stimuli to support designer ideation , 2019, Design Studies.

[12]  Kevin Gimpel,et al.  ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.

[13]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[14]  Arthur B. Markman,et al.  Design by Analogy: A Study of the WordTree Method for Problem Re-Representation , 2012 .

[15]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[16]  Ilya Sutskever,et al.  Language Models are Unsupervised Multitask Learners , 2019 .

[17]  Wei Li,et al.  Automated Extraction of Function Knowledge From Text , 2017 .

[18]  Yiming Yang,et al.  XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.

[19]  Nathan Schneider,et al.  Association for Computational Linguistics: Human Language Technologies , 2011 .

[20]  Jens Lehmann,et al.  Quality assessment for Linked Data: A Survey , 2015, Semantic Web.

[21]  Ashok K. Goel,et al.  Cognitive, collaborative, conceptual and creative - Four characteristics of the next generation of knowledge-based CAD systems: A study in biologically inspired design , 2012, Comput. Aided Des..

[22]  Simon Szykman,et al.  A functional basis for engineering design: Reconciling and evolving previous efforts , 2002 .

[23]  Georgi V. Georgiev,et al.  Methodology for creating new scenes through the use of thematic relations for innovative designs , 2017 .

[24]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[25]  W. B. Lee,et al.  Multi-facet product information search and retrieval using semantically annotated product family ontology , 2010, Inf. Process. Manag..

[26]  Catherine Havasi,et al.  ConceptNet 5.5: An Open Multilingual Graph of General Knowledge , 2016, AAAI.

[27]  Ying Liu,et al.  Product Family Design Through Ontology-Based Faceted Component Analysis, Selection, and Optimization , 2013 .

[28]  Takahiro Kawahara,et al.  Assessing Concept Novelty Potential with Lexical and Distributional Word Similarity for Innovative Design , 2019, Proceedings of the Design Society: International Conference on Engineering Design.

[29]  Feng Shi,et al.  A data-driven text mining and semantic network analysis for design information retrieval , 2017 .

[30]  Karthik Ramani,et al.  Ontology-based design information extraction and retrieval , 2007, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[31]  Kristin L. Wood,et al.  Evaluating Crowdsourced Design Concepts With Machine Learning , 2019, Volume 7: 31st International Conference on Design Theory and Methodology.

[32]  Robert Stone,et al.  A Validation Study of an Automated Concept Generator Design Tool , 2006 .

[33]  John S. Gero,et al.  Characterizing innovative processes in design spaces through measuring the information entropy of empirical data from protocol studies , 2017, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[34]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[35]  Feng Shi,et al.  The Combinator – a computer-based tool for creative idea generation based on a simulation approach , 2018, Design Science.

[36]  Lucienne Blessing,et al.  An Anthology of Theories and Models of Design , 2014 .

[37]  Soe-Tsyr Daphne Yuan,et al.  Using association reasoning tool to achieve semantic reframing of service design insight discovery , 2015 .

[38]  Feng Shi,et al.  A computational tool for creative idea generation based on analogical reasoning and ontology , 2018, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[39]  Conrad S. Tucker,et al.  Modeling the Semantic Structure of Textually Derived Learning Content and its Impact on Recipients' Response States , 2016 .

[40]  Yinghong Peng,et al.  Research on new creative conceptual design system using adapted case-based reasoning technique , 2017, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[41]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[42]  Johan Malmqvist,et al.  Supporting Knowledge Re-Use with Effective Searches of Related Engineering Documents - A Comparison of Search Engine and Natural Language Processing-Based Algorithms , 2019, Proceedings of the Design Society: International Conference on Engineering Design.

[43]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[44]  Yike Guo,et al.  An artificial intelligence based data-driven approach for design ideation , 2019, J. Vis. Commun. Image Represent..

[45]  Amaresh Chakrabarti,et al.  A conceptual tool for environmentally benign design: development and evaluation of a “proof of concept” , 2020, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[46]  Heiko Paulheim,et al.  Knowledge graph refinement: A survey of approaches and evaluation methods , 2016, Semantic Web.

[47]  Maria C. Yang,et al.  Mining and Representing the Concept Space of Existing Ideas for Directed Ideation , 2019, Journal of Mechanical Design.

[48]  M. Boden.  The creative mind : myths & mechanisms , 1991 .

[49]  Georgi V. Georgiev,et al.  Enhancing user creativity: Semantic measures for idea generation , 2018, Knowl. Based Syst..

[50]  Kristin L. Wood,et al.  Computer-Aided Design Ideation Using InnoGPS , 2019, Volume 2A: 45th Design Automation Conference.

[51]  Estevam R. Hruschka,et al.  Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.

[52]  Ken M. Wallace,et al.  A Methodology for Creating Ontologies for Engineering Design , 2007, J. Comput. Inf. Sci. Eng..

[53]  Julie S. Linsey,et al.  Exploring Automated Text Classification to Improve Keyword Corpus Search Results for Bioinspired Design , 2014 .

[54]  Sebastian Ruder,et al.  Universal Language Model Fine-tuning for Text Classification , 2018, ACL.

[55]  M. Kay.  Language Models , 2006 .

[56]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[57]  Kristin L. Wood,et al.  Idea generation with Technology Semantic Network , 2021, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[58]  Amaresh Chakrabarti,et al.  Evaluating the impact of Idea-Inspire 4.0 on analogical transfer of concepts , 2018, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[59]  Jianxi Luo,et al.  Engineering Knowledge Graph for Keyword Discovery in Patent Search , 2019, Proceedings of the Design Society: International Conference on Engineering Design.

[60]  A. Bonaccorsi,et al.  A Text Mining Based Map of Engineering Design: Topics and their Trajectories Over Time , 2019, Proceedings of the Design Society: International Conference on Engineering Design.

[61]  Kristin L. Wood,et al.  Guiding data-driven design ideation by knowledge distance , 2021, Knowl. Based Syst..

[62]  Yan Li,et al.  Data-Driven Concept Network for Inspiring Designers' Idea Generation , 2020, J. Comput. Inf. Sci. Eng..

[63]  Ashok K. Goel,et al.  DANE: Fostering Creativity in and through Biologically Inspired Design , 2011 .

[64]  Alain Bernard,et al.  The evolution, challenges, and future of knowledge representation in product design systems , 2013, Comput. Aided Des..

[65]  Kristin L. Wood,et al.  Technology Knowledge Graph for Design Exploration: Application to Designing the Future of Flying Cars , 2019, Volume 1: 39th Computers and Information in Engineering Conference.

[66]  Vinayak R. Krishnamurthy,et al.  Investigating a Mixed-Initiative Workflow for Digital Mind-Mapping , 2020 .

[67]  Hugo Liu,et al.  ConceptNet — A Practical Commonsense Reasoning Tool-Kit , 2004 .

[68]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[69]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[70]  Lee Spector,et al.  An approach to human–machine collaboration in innovation , 2017, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[71]  Victor Raskin,et al.  Developing Engineering Ontology for Information Retrieval , 2008, J. Comput. Inf. Sci. Eng..

[72]  Dirk Schaefer,et al.  A DATA-DRIVEN APPROACH FOR CREATIVE CONCEPT GENERATION AND EVALUATION , 2020, Proceedings of the Design Society: DESIGN Conference.

[73]  Maria C. Yang,et al.  A methodology for engineering ontology acquisition and validation , 2008, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[74]  Wei Zhang,et al.  Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.

[75]  Alec Radford,et al.  Improving Language Understanding by Generative Pre-Training , 2018 .