{"title":"从类型到网络:建筑智能设计中的知识表示方法综述","authors":"Yihui Li, Wen Gao, Borong Lin","doi":"10.1007/s44223-022-00006-9","DOIUrl":null,"url":null,"abstract":"<div><p>With the rise of the next generation of artificial intelligence driven by knowledge and data, the research on knowledge representation in architecture is also receiving widespread attention from the academia. This paper sorts out the evolution of architectural knowledge representation methods in the history of architecture, and summarizes three progressive representation frameworks of their development with type, pattern and network. By searching these three keywords in the Web of Science Core Collection among 4867 publications from 1990 to 2021, the number of publications in the past 5 years raised more than 50%, which show significant research interest in architecture industry in recent years. Among them, the first two are static declarative knowledge representation methods, while the network-based knowledge representation method also includes procedural knowledge representation methods and provides a way for knowledge association. This means the network representation has more advantage in terms of the logical completeness of knowledge representation, and accounts for 67% of the current research on knowledge representation in architecture. In the context of the rapid development of artificial intelligence, this method can realize the construction of architectural knowledge system and greatly improve the work efficiency of the building industry. On the other hand, in the face of carbon-neutral sustainable development scenarios, using knowledge representation, building performance knowledge and design knowledge could be expressed in a unified manner, and a personalized and efficient workflow for performance-oriented scheme design and optimization would be achieved.</p></div>","PeriodicalId":72270,"journal":{"name":"Architectural intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44223-022-00006-9.pdf","citationCount":"0","resultStr":"{\"title\":\"From type to network: a review of knowledge representation methods in architecture intelligence design\",\"authors\":\"Yihui Li, Wen Gao, Borong Lin\",\"doi\":\"10.1007/s44223-022-00006-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the rise of the next generation of artificial intelligence driven by knowledge and data, the research on knowledge representation in architecture is also receiving widespread attention from the academia. This paper sorts out the evolution of architectural knowledge representation methods in the history of architecture, and summarizes three progressive representation frameworks of their development with type, pattern and network. By searching these three keywords in the Web of Science Core Collection among 4867 publications from 1990 to 2021, the number of publications in the past 5 years raised more than 50%, which show significant research interest in architecture industry in recent years. Among them, the first two are static declarative knowledge representation methods, while the network-based knowledge representation method also includes procedural knowledge representation methods and provides a way for knowledge association. This means the network representation has more advantage in terms of the logical completeness of knowledge representation, and accounts for 67% of the current research on knowledge representation in architecture. In the context of the rapid development of artificial intelligence, this method can realize the construction of architectural knowledge system and greatly improve the work efficiency of the building industry. On the other hand, in the face of carbon-neutral sustainable development scenarios, using knowledge representation, building performance knowledge and design knowledge could be expressed in a unified manner, and a personalized and efficient workflow for performance-oriented scheme design and optimization would be achieved.</p></div>\",\"PeriodicalId\":72270,\"journal\":{\"name\":\"Architectural intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s44223-022-00006-9.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Architectural intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s44223-022-00006-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Architectural intelligence","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s44223-022-00006-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
随着以知识和数据为驱动力的新一代人工智能的兴起,建筑学中的知识表示研究也受到了学术界的广泛关注。本文梳理了建筑史上建筑知识表征方法的演变,并总结了其发展过程中类型、模式和网络三种渐进的表征框架。通过在 Web of Science Core Collection 的 4867 篇论文中检索这三个关键词,从 1990 年到 2021 年,近 5 年的论文数量增加了 50%以上,这表明近年来建筑行业的研究兴趣十分浓厚。其中,前两种是静态的陈述性知识表示方法,而基于网络的知识表示方法还包括程序性知识表示方法,并提供了知识关联的途径。这说明网络表示法在知识表示的逻辑完整性方面更具优势,占目前建筑学知识表示研究的 67%。在人工智能飞速发展的背景下,这种方法可以实现建筑知识体系的构建,大大提高建筑行业的工作效率。另一方面,面对碳中和的可持续发展情景,利用知识表示法,可以统一表达建筑性能知识和设计知识,实现以性能为导向的方案设计和优化的个性化高效工作流程。
From type to network: a review of knowledge representation methods in architecture intelligence design
With the rise of the next generation of artificial intelligence driven by knowledge and data, the research on knowledge representation in architecture is also receiving widespread attention from the academia. This paper sorts out the evolution of architectural knowledge representation methods in the history of architecture, and summarizes three progressive representation frameworks of their development with type, pattern and network. By searching these three keywords in the Web of Science Core Collection among 4867 publications from 1990 to 2021, the number of publications in the past 5 years raised more than 50%, which show significant research interest in architecture industry in recent years. Among them, the first two are static declarative knowledge representation methods, while the network-based knowledge representation method also includes procedural knowledge representation methods and provides a way for knowledge association. This means the network representation has more advantage in terms of the logical completeness of knowledge representation, and accounts for 67% of the current research on knowledge representation in architecture. In the context of the rapid development of artificial intelligence, this method can realize the construction of architectural knowledge system and greatly improve the work efficiency of the building industry. On the other hand, in the face of carbon-neutral sustainable development scenarios, using knowledge representation, building performance knowledge and design knowledge could be expressed in a unified manner, and a personalized and efficient workflow for performance-oriented scheme design and optimization would be achieved.