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研究生:李安翔
研究生(外文):Li, An-Shiang
論文名稱:以案件摘要本體論為基之商標侵權判決知識表示與分析
論文名稱(外文):Knowledge Representation and Analysis of Trademark Infringement Cases based on Case Brief Ontology Schema
指導教授:張瑞芬張瑞芬引用關係張力元張力元引用關係
指導教授(外文):Trappey, Amy J. C.Trappey, Charles V.
口試委員:李紀寬張艾喆
口試委員(外文):Li, Ge-KuanChang, Ai-Che
口試日期:2020-06-10
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:77
中文關鍵詞:商標侵權知識本體文字探勘推薦系統案件摘要
外文關鍵詞:trademark infringementframe-based ontology representationtext miningrecommendation systemtopic modelingcase brief
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近年來,商標侵權問題已經引起了公眾的關注。商標在商業中有著重要的作用,不但代表公司的品牌形象,也展示其產品的商業價值。然而,關於商標侵權的爭議層出不窮,不道德的商人仿造並使用類似的著名商標,吸引消費者的關注以獲取不當利潤。因此,越來越多商標所有權者提起訴訟來捍衛自己的權益。面對繁雜的訴訟準備過程,蒐集訴訟相關的前案是最耗費時間也難以省略的步驟之一。為了提高搜索和閱讀商標判決的時間和效率,本研究提出以知識本體為基之商標侵權判例電腦輔助推薦系統,將判決文件知識結構化,推薦並提供使用者作為前案蒐集之參考依據。本研究首先以法律上案件摘要(Case Brief)的觀點建構商標侵權知識本體,將本體知識分為五個類別,包含基本資料、案件背景、爭點、法條、裁決,並以美國地方法院及聯邦法院之商標侵權判例為分析文本,針對非結構化的文字資料定義相應的規則,利用自然語言處理提取判例的關鍵特徵,構建商標侵權判決領域的知識本體資料庫。最後透過主題模型(Topic Model)分析判例文本,分群並定義判例的內容主題,對應法律爭點和分類主題之集群,比對判例之間的相似性,提供最符合法律閱讀角度的前案推薦結果。
A registered trademark (TM) distinctively represents a company and the products or services the company sells. TM is a form of intellectual property (IP) legally protected by the IP laws in the country where it is officially registered. Thus, TM related legal cases have grown rapidly due to the exponential growth of globally registered TMs. In this paper, an intelligent decision support system automatically discovers similar TM related legal judgments for any given seed case, is developed to support legal research. This study constructs the semantic network (i.e., an ontology schema) representing TM legal scope and terminologies. In the research, 3580 US trademark infringement cases litigated in the US district and federal court are collected as our experimental corpus. The system extracts critical features of each legal document, in compliance with the TM legal semantic ontology schema. The system can identify matching precedents according to the critical features (representing these legal briefs), e.g., case facts, TM-related issues under disputes, judgment holdings, and applicable laws. Text mining and topic modeling techniques (LDA, DTM) are deployed to discover relevant precedents. The objective is to assist users in searching the most relevant cases in terms of different corresponding rules and laws in those litigation documents. Through the analysis of the similarity between the issues and the topics, the system can provide relevant precedents based on the reasonable legal opinion.
摘要 I
Abstract II
Table of Contents III
List of Figures V
List of Tables VI
1. Introduction 1
1.1 Research Background 1
1.2 Research Purpose 4
1.3 Research Framework and Procedure 5
2. Literature Review 7
2.1 Trademark 7
2.1.1 US Trademark Protection 8
2.1.2 US Trademark Litigation 9
2.1.3 US Statutory Trademark Law 11
2.2 E-Discovery 12
2.3 Ontology 14
2.3.1 Frame-based Ontology Knowledge Representation 15
2.3.2 The Application of Ontology 16
2.4 Information Retrieval and Recommender Systems 17
2.4.1 Information Retrieval 18
2.4.2 Recommender System 18
2.5 Text Mining 18
2.5.1 Topic Modeling 24
2.5.2 Word Embeddings 28
3. Methodology 30
3.1 Knowledge Ontology Construction 31
3.1.1 Trademark Litigation Judgment Ontology 31
3.1.2 Frame-Based Ontology Construction 36
3.1.3 “Case Brief” Feature Extraction 38
3.2 Issue Identification 40
3.2.1 System Programming Implementation 42
3.3 Similarity Calculation 42
4. Case Study 45
4.1 Seed judgments 45
4.2 Identify Precedents in Matching Issues and Their Key Terms 46
4.3 Topic modeling of seed judgments and precedent judgments 48
4.4 Matching precedents 55
4.5 Trademark case examples 56
4.6 Verification 63
5. Conclusion 67
References 69
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