{"title":"A survey of large language model-augmented knowledge graphs for advanced complex product design","authors":"Xinxin Liang, Zuoxu Wang, Jihong Liu","doi":"10.1016/j.jmsy.2025.04.016","DOIUrl":null,"url":null,"abstract":"<div><div>In the Human-AI collaboration rapid development era, the design and development of knowledge-intensive complex products should enable the design process with the help of advanced AI technology, and enhance the reasoning and application of design domain knowledge. Extracting and reusing domain knowledge would greatly facilitate the success of complex product design. Knowledge graphs (KGs), a powerful knowledge representation and storage technology, have been widely deployed in advanced complex product design because of their advantages in mining and applying large-scale, complex, and specialized domain knowledge. But merely KG and its related reasoning approaches still cannot fully support the ill-defined product design tasks. In the future complex product design, Human-AI collaboration will become a mainstream prevention trend. Large language models (LLMs) have outstanding performance in natural language understanding and generation, showing promising potential to collaborate with KGs in complex product design and development. Till 2024/03/04, only a few studies have systematically reviewed the current status of LLM and KG applications in the engineering field, not to mention a further detailed review in the complex product design field, leaving many issues not covered or fully examined. To fill this gap, 100 articles published in the last 4 years (i.e., 2021–2024) were screened and surveyed. This study provides a statistical analysis of the screened research articles, mainstream techniques of LLM & KG, and LLM & KG applications were analyze. To understand how KG and LLM could support complex product design, a framework of LLMs-augmented KG in advanced complex product design was proposed, which contains data layer, KG & LLM collaboration layer, enhanced design capability layer, and design task layer. Furthermore, we also discussed the challenges and future research directions of the LLM-KG-collaborated complex product design paradigm. As an exploratory review paper, it provides insightful ideas for implementing more specialized domain KGs in product design field.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 883-901"},"PeriodicalIF":12.2000,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0278612525001050","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
In the Human-AI collaboration rapid development era, the design and development of knowledge-intensive complex products should enable the design process with the help of advanced AI technology, and enhance the reasoning and application of design domain knowledge. Extracting and reusing domain knowledge would greatly facilitate the success of complex product design. Knowledge graphs (KGs), a powerful knowledge representation and storage technology, have been widely deployed in advanced complex product design because of their advantages in mining and applying large-scale, complex, and specialized domain knowledge. But merely KG and its related reasoning approaches still cannot fully support the ill-defined product design tasks. In the future complex product design, Human-AI collaboration will become a mainstream prevention trend. Large language models (LLMs) have outstanding performance in natural language understanding and generation, showing promising potential to collaborate with KGs in complex product design and development. Till 2024/03/04, only a few studies have systematically reviewed the current status of LLM and KG applications in the engineering field, not to mention a further detailed review in the complex product design field, leaving many issues not covered or fully examined. To fill this gap, 100 articles published in the last 4 years (i.e., 2021–2024) were screened and surveyed. This study provides a statistical analysis of the screened research articles, mainstream techniques of LLM & KG, and LLM & KG applications were analyze. To understand how KG and LLM could support complex product design, a framework of LLMs-augmented KG in advanced complex product design was proposed, which contains data layer, KG & LLM collaboration layer, enhanced design capability layer, and design task layer. Furthermore, we also discussed the challenges and future research directions of the LLM-KG-collaborated complex product design paradigm. As an exploratory review paper, it provides insightful ideas for implementing more specialized domain KGs in product design field.
期刊介绍:
The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs.
With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.