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研究生： 林傑仁
Lin, Chieh-Jen
論文名稱： 建築設計隱含知識呈現之研究：以住宅案例庫中的空間資訊探勘為例
A Study on Representation of Implicit Knowledge of Architectural Design: Spatial Information Mining in a House Case Library as Examples
指導教授： 鄭泰昇
Jeng, Tay-Sheng
邱茂林
Chiu, Mao-Lin
學位類別： 博士
Doctor
系所名稱： 規劃與設計學院 - 建築學系
Department of Architecture
論文出版年： 2011
畢業學年度： 99
語文別： 英文
論文頁數： 172
中文關鍵詞： 案例庫 、隱含知識 、知識本體論 、空間資訊 、資料探勘
外文關鍵詞： case library, implicit knowledge, ontology, spatial information, data mining
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    • 建築設計不僅是解決問題的活動，也是學習的過程。無論是學生或建築師，設計案例都是學習上重要的媒介。案例式設計的基本想法，即是提供類似的案例，作為學習如何解決新問題的基礎。學習新知的關鍵不在案例的新舊，而在於如何重新詮釋案例中已知的解答，建立與新問題之間有意義的連結。過去的研究多以問題的相似性為基礎，著重於案例擷取、選擇與調適等機制，而忽略了如何從案例中學習設計知識。原因除了技術上的問題，主要還是由於案例中隱含的設計知識，難以事先明確地定義與編碼，成為建置案例庫時主要的“知識瓶頸”。
    為克服案例庫的知識瓶頸，本研究應用基於“知識本體”的知識呈現，結合資料探勘技術，提出以視覺化方式呈現案例庫中隱含設計知識的理論架構。知識本體論是人工智慧研究中以正規形式呈現知識的理論與方法，目的在促進知識的分享與利用。知識本體著重在知識實體之間語義關係的建立，但缺少建築設計必要的圖像知識，如空間拓樸與幾何資訊間的轉換與驗證機制。因此本研究藉由實作視覺化的拓樸編碼工具，解決知識本體語義資訊與圖像知識連結上的問題。
    本研究步驟，分為文獻回顧、方法建立、系統實作與驗證三個階段。藉由(1)以知識本體論為基礎的案例庫開發、(2)以視覺化為基礎的圖像知識呈現與驗證的演算法、(3)應用文字探勘方法獲取隱含設計知識，進行主要研究課題的探討。本論文的研究成果包括：
    1. 基於關鍵字詞的設計知識搜尋與檢索機制：應用文字探勘技術，結合知識本體論方法，本研究提出建立設計案例的關鍵字詞與設計議題之間的語意聯想的架構。並藉由視覺化呈現，協助使用者發現案例文字資訊中隱含的設計知識。
    2. 基於知識本體論的案例庫開發理論：應用知識本體論的方法，本研究提出開放式的案例庫詮釋資料架構。目的在解決應用資料庫技術開發案例庫時，詮釋資料之間缺乏語義關連性，無法協助學習與應用設計知識的問題。
    3. 以視覺化為基礎的圖像知識呈現與驗證演算法：本研究提出以應用視覺化技術，圖像式編碼與呈現設計知識的方法。以住宅平面圖的空間資訊為例，本研究探討轉換圖像資訊，與驗證知識本體的演算法，以解決知識本體與圖像知識之間，難以轉換與驗證的問題。住宅平面圖的空間資訊編碼結果，可以作為進一步探勘住宅空間配置的設計樣型的基礎。
    透過本研究的成果，以及系統開發實作與測試的經驗，本研究對於案例庫隱含知識的呈現進行分析與建議，包括：(1)基於關鍵字詞的設計知識搜尋機制；(2)基於知識本體的案例庫的詮釋資料；(3)設計案例中的圖像知識；與(4)圖像知識的視覺化擷取介面，提出改善的觀點與建議。

    Architectural design is not only a problem-solving activity, but also a learning process. For both students and architects, design cases are important media for learning. Case-based design (CBD) therefore promises to provide cases, which are similar to new problems, as the foundation of learning how to solve the problems. However, the key for learning lies not in the newness of the cases, but in the reinterpretation of known solutions to make meaningful connections among solutions and problems. Past CBD researches focused on retrieval, selection and adaption of cases based on similarity among present and past problems, but ignored how to learn knowledge from cases. Except technical reasons, this disadvantage is because that design knowledge is implicit, and difficult to be clearly defines and encoded beforehand, therefore causes the “knowledge bottleneck” of developing a case library.
    For overcoming the bottleneck, this study applies ontology-based representation, combining data-mining techniques, to establish a theoretical framework for visualizing implicit knowledge of a case library. Ontology is a theory and approach of formally representing knowledge in artificial intelligent research, and aims to facilitate sharing and reusing knowledge. Ontology focuses on establishing semantic relations among knowledge entities, but lacks of necessary graphic knowledge of architectural design, such as translating and validating mechanism among topological and geometric information. This study therefore implements a visually encoding tool of spatial topology, which can help to link semantic topology with graphic knowledge.
    This study is divided into three sections: literature review; approach and methodology; implementation and verification of the system. This study explores relevant issues through the surveys of: (1) development of an ontology-based case library, (2) visualization-based algorithm for representing graphic knowledge and validating its ontology, and (3) acquiring implicit knowledge by applying text-mining technique. Research results of this study include:
    1. A keyword-based search and index mechanism of design knowledge: By applying text mining technique, combining ontology-based method, this study proposes a framework for the establishment of semantic associations between keywords of case’s textual information and relevant design issues. Via the visualization of semantic associations, the visual interface can help users to discover implicit design knowledge within textual information of design cases.

    2. A theory for developing an ontology-based case library: By applying ontology techniques, this study proposes a case library named “Open Case Study” (OCS), which has an open authoring tool of metadata named “open ontology.” OCS aims to solve the problem of learning and applying design knowledge in a case library, which it is developed by relational database technology therefore usually loses semantic associations among metadata.
    3. Graphic-based knowledge representation and validating algorithm: Via visualizing techniques, this study proposes a graphical approach to encoding and representing knowledge. Using the spatial information of residential floor plans as examples, this study investigates the algorithm for translating graphic information and validating their ontology. It aims to solve the problems of translation and validation between ontological and graphical knowledge. The encoded spatial information of house plans can server as the foundations for further mining the design patterns from house’s spatial layouts.
    Through the results of this study and the experiences derived from developing and implementing systems, this study proposes the following suggestions for the analysis and discussions of representing the implicit knowledge of a case library: (1) the keyword-based search mechanism of design knowledge; (2) ontology-based metadata of a case library; (3) graphic information of design case; and (4) visual interface of graphic knowledge retrieval.

    I. Introduction 1 1-1. Research Backgrounds 2 1-1-1. Design as a Problem-Solving Activity 2 1-1-2. Case-Based Design & Reasoning 3 1-1-3. Computational Models and Knowledge Representations of Intelligent Systems 4 1-1-4. Intelligent Agents and Knowledge Ontology 5 1-1-5. Knowledge Acquisitions of Information-Overloading 6 1-2. Research Motivation 7 1-3. Research Scopes and Goals 10 1-3-1. The Case Library of a CBD System 10 1-3-2. Knowledge Representations of Design Cases 11 1-3-3. Knowledge Discovery from Design Information 13 1-3-4. Research Goals 14 1-4. Research Method and Procedure 15 1-5. Definitions of Terminology 18 II. Literature Survey 23 2-1. Case-Based Architectural Design 23 2-1-1. CBD System for Architectural Design 23 2-1-2. Architectural Case Library 26 2-1-3. Information of Architectural Design Cases 28 2-1-4. Summary 29 2-2. Knowledge Representation of Architectural Design Cases 32 2-2-1. Human Memory Based Approaches 32 2-2-2. Information Model Based Approaches 34 2-2-3. Semantic Ontology Based Approaches 36 2-2-4. Summary 38 2-3. Architectural Design Knowledge Management 41 2-3-1. Tacit, Implicit, and Explicit Knowledge 43 2-3-2. Procedural and Declarative Knowledge of Intelligent System 46 2-3-3. Design Information Mining for Architectural Design Knowledge Management 48 2-3-4. Summary 50 2-4. Conclusions 52 III. Research Framework and System Analysis 55 3-1. The Requirements of a Case Library 55 3-1-1. The Application of Architectural Education 55 3-1-2. The Application of Architectural Practice 58 3-1-3. Summary 60 3-2. Case Base for Architecture (CBA) 62 3-2-1. Keyword-Based Approach of the CBA 62 3-2-2. Information Structures of the CBA 64 3-2-3. Keyword Based Search Mechanism of the CBA 66 3-2-4. Smart Semantic Query of the CBA 68 3-2-5. Basic Schema of Keywords 70 3-2-6. Summary 72 3-3. Open Case Study (OCS) 73 3-3-1. Ontology-Based Approach of the OCS 74 3-3-2. The Open Ontology of OCS 79 3-3-3. Detection and Visualization of the Ontology-Based Knowledge 85 3-3-4. Summary 88 3-4. Modeling Spatial Information in OCS 90 3-4-1. The Graphic Approach of Spatial Topology Retrieval (STR) 91 3-4-2. Space Layout Algorithm of STR 94 3-4-3. Encoding & Retrieval of Spatial Information by STR 98 3-4-4. Summary 104 3-5. Conclusions 105 IV. System Implementations and Evaluations 107 4-1. Smart Semantic Query of CBA 107 4-2. The Open Ontology of OCS 109 4-2-1. Implementation of Open Ontology 110 4-2-2. Implementation of Concept Explorer Interface 112 4-3. Graphic Interface of Spatial Information 113 4-3-1. Simple Validating Algorithm of SLG 114 4-3-2. Encoding Spatial Information by STR 116 4-3-3. Partial Retrieval of Floor Planes by STR Ranking 117 4-4. Testing and Evaluations of Implementations 118 4-4-1. Keyword-Based Searching and Representation of SSQ 119 4-4-2. Ontology-Based Metadata of OCS 121 4-4-3. Graphic Spatial Information of STR 123 4-5. Conclusions 126 V. Discussions 127 5-1. Keyword-Based Search and Representation Mechanism 127 5-1-1. Narrowing Query Gaps by Reversed-Indexing of Keywords 127 5-1-2. Necessary Rich Corpus for Text Mining 129 5-1-3. Assistance of Keyword Search by Visual Interface 130 5-1-4. Keyword-Based Knowledge Representation 131 5-2. Ontology-Based Metadata of a Case Library 133 5-2-1. Semantic Association as Connections among Design Knowledge 133 5-2-2. Re-interpretation of Design Knowledge in Cases 135 5-2-3. Knowledge Types in Design Cases 136 5-3. Implicit Graphic Knowledge of Design Cases 137 5-4. Summary 139 VI. Conclusions and Future Works 141 6-1. Conclusions 142 6-2. Future Works 144 VII. References 146 VIII. Appendixes 157 8-1. Algorithms of SSQ 157 8-1-1. The tf-idf Weighting 157 8-1-2. Cosine Similarity 157 8-1-3. Memory-Based Detecting Algorithm 158 8-1-4. Association-Base Detecting Algorithm 159 8-1-5. Early House Cases of CBA 160 8-1-6. Keyword Recognition Experiment 163 8-2. Simple Validating Algorithm of SLG 165 8-3. Converting Algorithm of STR 167 8-4. Similarity Ranking Algorithm of TPM 167 IX. The Author’s Resume 169 9-1. Publications 169 9-1-1. Journal Papers 169 9-1-2. Conference Papers 170 9-2. Research Projects 171 9-3. Other Projects 172

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