發展具有解析使用者認知能力的專家系統於提升資訊查詢效能

Developing Perception-aware Expert System to Improve Information Search Performance

10.6382/JIM.200901.0002

戚玉樑(Yu-Liang Chi)；陳彥均(Yen-Chun Chen)

知識本體 ； 語意 ； 感性概念 ； 專家系統 ； 資訊查詢 ； Ontology ； Semantic ； Sensory words ； Expert system ； Information search

資訊管理學報

16卷1期（2009 / 01 / 01）

1 - 24

繁體中文

為協助使用者能以「認知」進行資訊查詢，本研究提出以發展知識本體(Ontology)為核心的專家系統，藉由納入使用者的感覺或情緒為知識庫內涵，以期提升系統處理認知解析的能力，由於人類認知是複雜的心智過程，如何轉換為系統可處理的形式將是重要的挑戰。本研究嚐試利用感性工學方法(Kansei Engineering)於知識本體的建置，經由收集主題的特徵及感性語彙，再分析二者重要的關聯性成為知識概念的建置元素，最後逐步形成使用者認知的知識本體。本研究以大眾對犬類的認知為例，說明如何執行知識本體的建置，我們也開發具有知識推論能力的應用系統，整合成為犬種查詢的專家系統。本研究特別適用於查詢物為「不知名」或「不確定」的情境，藉由使用者選擇其認知的感受，達到「所感即所得」的目標。由實證經驗顯示，使用者不須使用犬種分類或專有名稱為查詢詞彙，而是以對犬類特徵的語意概念為條件來查詢，即可獲得候選的犬種，再經由目視候選圖片方式，確認最終查詢標的。

In order to facilitate perception as information retrieval criteria, this study utilizes human feelings and cognition as the design elements of a knowledge base building. Owing to human feelings are complex mental processes, how to translate these physiological states into executable information is a challenge. This study learns from Kansei Engineering (KE) to propose a development approach for obtaining knowledge base design elements. The primary tasks include gathering human feelings in a word form, grouping similar words into a high-level semantic structure, and identifying important semantics as design elements for ontology building. A walkthrough example of identifying dog breed names is given to demonstrate the advantages of this design. Implementation results indicate that the new design allows users to search their needs by simply giving perceptions of what they saw or felt without much domain expertise. Consequently, this study overcomes limitations of keyword-based search by applying a perception-aware of ontology development.

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