Patent text based technology tree construction research
특허 텍스트 기반의 기술 체계도 구축 방법론 연구
- 주제(키워드) Technology tree , SAO structure , Association rule mining , Semantic network , Patent analysis
- 주제(DDC) 006.31
- 발행기관 아주대학교
- 지도교수 신현정
- 발행년도 2023
- 학위수여년월 2023. 2
- 학위명 석사
- 학과 및 전공 일반대학원 인공지능학과
- 실제URI http://www.dcollection.net/handler/ajou/000000032553
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
A technology tree(Tech tree) is used to represent attribute information such as the function, purpose, and components of a technology, and is a useful tool to support decision-making on technology development in a particular technology area. But there is a limitation in the construction of Tech tree that it is difficult to reflect rapidly changing technologies properly due to the highly dependent of experts in the technology field. So, this research is aimed to reducing reliance on expert dependence by presenting a methodology that automates the construction of a Tech tree by focusing on the functional information of the technology, and to analyzing and present the attribute information of the technology. The information in the patent document is used to classify similar technology groups, and the text inside the patent document is used to identify the functional relationship of the detailed technology of each technology group. The results of this research will be utilized to provide information that can support R&D planning and decision-making on building a technology portfolio by providing information on technology relevance and technology development status.
more목차
Chapter I Introduction 1
1 Research background and purpose 1
2 Research organization 4
Chapter II Background 5
1 Technology tree 5
Chapter III Research framework 8
1 Overall research process 8
2 Step 1. Classification of technical documents 10
3 Step 2. Semantic keyword extraction and normalization 12
4 Step 3. Technology cluster labeling 15
5 Step 4. Network construction and semantic keyword classification 18
6 Step 5. Network-based technology tree 21
Chapter IV Case study 23
1 Data 23
2 Step 1. Classification of technical documents 25
3 Step 2. Semantic keyword extraction and normalization 27
4 Step 3. Technology cluster labeling 28
5 Step 4. Network construction and semantic keyword classification 31
6 Step 5. Network-based technology tree 33
Chapter V Conclusion 36
1 Discussion 36
2 Research contribution and limitation 38
Reference 40
