知識的累積過程中，由於知識庫中知識來源不同，專家意見不一，導致知識庫中知識存在相同意義、衝突或數據不一致。並隨時空環境改變，新技術、新法規、新方法、新證據產生等因素，可能產生知識不適用等情況。建立規則式知識庫專家系統往往著重於規則基礎之結構錯誤驗證，對於衝突規則處理大都以指定規則執行先後次序解決。至於規則中存在的衝突或對應數值大小不一或重覆的處理，尚未有妥適處理方法。由於不確定知識本身存在不確定性，所以其衝突規則處理更顯困難。而引用錯誤的知識，會導致錯誤的決策，94%使用者認為引用衝突或重覆規則存在困擾，確認所引用知識的存在確定及信賴程度是必要的。
本研究提出「條件機率知識相似度演算法」，發展「規則知識相似度計算系統」，有效、準確地求得知識相似度矩陣，擷取具有衝突與重疊知識。針對衝突與重疊知識，整合群體決策及不確定推理觀念，提出「不確定規則知識衝突處理推論模式」，本模式以信賴指數表示存在衝突或重疊或對應數據大小不一知識的信賴程度；以確定指數顯示知識是否確定存在程度；而確定信賴指數則用以綜合顯示衝突知識之確定存在及被信賴程度。再依據知識關聯性，提出「不確定規則知識加值處理推論模式」，本模式針對具有關聯規則群集，經合併、整合、創新、尋找、刪除及新增等加值處理推論，使原有的衝突規則或創新的規則，都明確顯示其確定信賴指數，因此，92%使用者認為選用較高確定信賴指數的規則，對於知識應用與輔助決策有幫助，可減少錯誤決策，提升知識應用之附加價值，使知識應用更有效益。

In the process of knowledge accumulation, due to the difference of knowledge source in the knowledge base and opinions of experts, the knowledge in the knowledge base would have inconsistency with the same meaning, conflict and data, which may lead to an improperness of knowledge use as a result of change of time, new technology, new regulation, new methodology and new evidence and other factors. Rule-based knowledge base expert systems have traditionally emphasized the verification of structural errors in the rule base. For conflicting or overlapping rules, designated rules are usually followed to implement prioritized or direct deletions. However, there exist no proper methods by which to resolve conflicts, inconsistencies or redundancies in value. Due to the uncertainty of uncertain knowledge itself, it is difficult to treat conflicting rules, and the citation of erroneous knowledge leads to mistakes in decision making. Among users, 94% report perplexity when conflicting or redundant rules are cited. It is therefore a necessity to confirm the existence and reliability of the cited knowledge.
This study proposes integrates methods of conditional probability, and vector matrices to establish a conditional probability knowledge similarity algorithm and calculation system. This calculation system can quickly and accurately calculate rule-based knowledge similarity matrices and capture the conflicting rules and redundant rules. For the conflicting rules and redundant rules, the current study attempts to provide an uncertainty rule-based knowledge conflict treatment inference model by integrating a group decision and an uncertainty inference. In the model, a “reliable factor” refers to the reliability level of the knowledge containing conflicts, redundancies or inconsistencies in values, while the “certainty factor” indicates the existence of the knowledge itself. A “certainty reliable index” is used to show both the existence of the knowledge itself and its reliability. Based on knowledge relationship, a rule-based uncertainty knowledge value-adding treatment inference model is established to perform value-adding treatment such as merge, integration, innovate, search, delete, and add, so that certainty reliable indexes of the rules can be obviously represented, For conflicting or overlapping knowledge, it is suggested that the knowledge with a higher certainty reliable index be chosen. Among users, 92% reported that the model is helpful to knowledge application and an aid to the decision-making process. It can more effectively prevent mistakes in decision making and enables users to acquire more benefits from the knowledge application.

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