有鑒於網路市場的蓬勃發展，使得線上購物的消費人口大量增加，網路行銷模式亦成為企業主要的行銷工具，然而大量的網路行銷導致商標圖樣更廣泛的被使用，也使商標侵權發生的頻率提高。Facebook、Line等社群媒體的普及，使用通訊軟體聊天成為目前人與人之間最常見的互動方式，造就聊天機器人這種新的服務型態。聊天機器人（Chatbot）是經由對話或文字進行交談的應用程式，藉由自然語言學習與和資訊檢索模擬人類對話，給予適當的回應。當面臨商標相關問題時，大眾常缺少了快速獲取答案的管道，因此本研究以商標常見問答FAQ作為聊天機器人之任務並進行系統設計和開發，以聊天機器人作為介面，讓人們可以透過簡短的問句理解商標註冊和侵權等相關知識並獲得需要的答案。
研究主要分為兩個部分，建構商標FAQ本體論及聊天機器人建構，本體論作為聊天機器人之知識背景、對話邏輯並支撐驅動程式運行。本研究根據台灣商標法建構本體論，分為意圖、子意圖及子意圖關鍵字三個階層。首先透過台灣智財局網站商標常見問題分類將本體論分為14個意圖，並透過商標法將範圍較大之意圖向下延伸至數個子意圖。接著藉由Term Frequency - Inverse Document Frequency找出子意圖的關鍵詞彙作為本體論之第三層，本體論中每一個子意圖皆含有一個可以回應的答案。完成本體論的建構後將其轉換為JSON資料模式，作為聊天機器人的資料庫。聊天機器人之檢索根據本體論分為兩個階段，分別為問題分類和關鍵字分析，在問題分類中，先透過RNN-LSTM訓練，獲得問題之意圖後並進行資料庫的檢索，若意圖中沒有子意圖便直接生成回答；反之則進行第二階段，將問句與子意圖下的關鍵字做相似度分析。本研究使用word2vec生成詞向量，計算詞彙之間的餘弦相似度(cosine similarity)得出平均分數最高的子意圖，根據最後得到之意圖查詢資料庫生成回應之答案。最後，本研究透過Django網頁架構建立網頁應用程式，目的是希望本研究之結果可以作為實際上之應用。

Recently, enterprises in Taiwan began to develop their own brands and trademarks so the importance of trademark increase rapidly. The development of the Internet led to more trademark (TM) infringement cases over the Internet, which makes the brand protection more difficult. When people encounter questions related to TM registration, infringement and other issues, which may be hard to find the correct answers. Therefore, this study builds a chatbot platform to provide users with simple, easy, and quick answers to their questions. This study is mainly devided in two part, first one is to build the ontology of trademark consultation based on Taiwan IP Office (TIPO) TM frequent asked questions (FAQs), which aims to mapping the chatbot process and provide for knowledge database. The second is to construct the chatbot system that embedded with natural language processing capabilities.
The TM consultation ontology is built on hierarchical structure with three levels, namely intent, sub-intent and key terms of sub-intent. Intent and sub-intent are defined according to the classifications of FAQ in TIPO and literature of TM law. For the third level, we use term frequency - inverse document frequency method to extract the key terms. The process of retrieval system includes intent prediction and key term similarity analysis. In intent prediction, we use long short-term memory model from recurrent neural network to train the classifier. For similarity analysis, it generated word vectors by word2vec model, and then calculated the cosine similarity between key terms and question to obtain the sub-intent with the highest average score and response answers by querying the database. Last, this study builds a web application through Django web architecture in order to make this study available for practical use.

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