|
|
|
| Research on Disruptive Technology Topic Recognition Based on CLIP-LDAGV Multimodal Information Fusion — A Case Study of the New Energy Field |
| Lyu Kun1,2, Zhang Weixu3, Jing Jipeng4 |
1.Business School, Ningbo University, Ningbo 315211
2.Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory of Ningbo University, Ningbo 315211
3.College of Computer, National University of Defense Technology, Changsha 410073
4.School of Business and Management, Jilin University, Changchun 130012 |
|
|
|
|
Abstract Currently, global technological innovation is exhibiting a trend of rapid development and high integration. Accurately identifying disruptive technological themes that drive comprehensive innovation has become a key driving force for scientific and technological development and economic growth. However, traditional methods of subversive technology topic identification rely primarily on single-modal data, which have some limitations. This study constructs a news text and image feature fusion vector based on the CLIP (contrastive language-image pre-training) and LDAGV (linear discriminant analysis & global vectors for word representation) models, and uses k-means clustering iterations combined with three disruptive technology topic indicators for screening, achieving the fusion of multimodal information and accurate identification of topics. Using the new energy field as an example, the feasibility and effectiveness of the model for disruptive technology topic recognition are verified. Compared with other single-modal models, multimodal information fusion models have additional advantages in identifying disruptive technology topics.
|
|
Received: 08 July 2024
|
|
|
|
1 中国科学院颠覆性技术创新研究组. 颠覆性技术创新研究: 能源领域[M]. 北京: 科学出版社, 2023: 1-2.
2 霍朝光, 卢小宾, 杨冠灿, 等. 数据驱动的产业技术情报分析方法体系框架构建[J]. 图书情报知识, 2022, 39(1): 73-83.
3 苏鹏, 苏成, 潘云涛. 颠覆性技术识别方法发展现状及启示[J]. 图书情报工作, 2019, 63(20): 129-138.
4 张佳维, 董瑜. 颠覆性技术识别指标的研究进展[J]. 情报理论与实践, 2020, 43(6): 194-199, 193.
5 Nagy D, Schuessler J, Dubinsky A. Defining and identifying disruptive innovations[J]. Industrial Marketing Management, 2016, 57: 119-126.
6 刘安蓉, 李莉, 曹晓阳, 等. 颠覆性技术概念的战略内涵及政策启示[J]. 中国工程科学, 2018, 20(6): 7-13.
7 Paap J, Katz R. Anticipating disruptive innovation[J]. IEEE Engineering Management Review, 2004, 32(4): 74-85.
8 Dan Y, Chieh H C. A reflective review of disruptive innovation theory[C]// Proceedings of the 2008 Portland International Conference on Management of Engineering & Technology. Piscataway: IEEE, 2008: 402-414.
9 王志勇, 党晓玲, 刘长利, 等. 颠覆性技术的基本特征与国外研究的主要做法[J]. 国防科技, 2015, 36(3): 14-17, 22.
10 李乾瑞, 郭俊芳, 黄颖, 等. 基于专利计量的颠覆性技术识别方法研究[J]. 科学学研究, 2021, 39(7): 1166-1175.
11 Christensen C M. The innovator’s dilemma: when new technologies cause great firms to fail[M]. Boston: Harvard Business School Press, 1997: 1-179.
12 李晓龙, 鲁平, 李存斌. 基于Delphi和DEMATEL法影响国网的颠覆性创新技术影响因素综合排序分析[J]. 科技管理研究, 2017, 37(6): 127-133.
13 孙永福, 王礼恒, 孙棕檀, 等. 引发产业变革的颠覆性技术内涵与遴选研究[J]. 中国工程科学, 2017, 19(5): 9-16.
14 卢小宾, 朱庆华, 查先进, 等. 信息分析导论[M]. 武汉: 武汉大学出版社, 2020.
15 卢小宾, 霍帆帆, 王壮, 等. 数智时代的信息分析方法: 数据驱动、知识驱动及融合驱动[J]. 中国图书馆学报, 2024, 50(1): 29-44.
16 冯立杰, 秦浩, 王金凤, 等. 融合专利数据与社交媒体数据的潜在颠覆性技术识别——基于深度学习模型[J]. 情报学报, 2024, 43(2): 181-197.
17 马永红, 孔令凯, 林超然, 等. 基于异构数据的颠覆性技术识别研究——以智能制造装备领域为例[J]. 现代情报, 2022, 42(7): 92-104.
18 吕鲲, 项旻昊, 靖继鹏. 基于LDA2vec和DTM模型的颠覆性技术主题识别研究——以能源科技领域为例[J]. 图书情报工作, 2023, 67(12): 89-102.
19 李牧南, 赖华鹏, 王良, 等. 基于主题强度突变检测的颠覆性技术识别[J]. 情报杂志, 2023, 42(12): 111-118.
20 Dieng A B, Ruiz F J R, Blei D M. Topic modeling in embedding spaces[J]. Transactions of the Association for Computational Linguistics, 2020, 8: 439-453.
21 Dotsika F, Watkins A. Identifying potentially disruptive trends by means of keyword network analysis[J]. Technological Forecasting and Social Change, 2017, 119: 114-127.
22 陈育新, 李健, 韩毅. 核心—边缘理论视角下的颠覆性技术识别研究[J]. 情报理论与实践, 2022, 45(8): 121-129.
23 Min C, Bu Y, Sun J J. Predicting scientific breakthroughs based on knowledge structure variations[J]. Technological Forecasting and Social Change, 2021, 164: 120502.
24 Xu H Y, Luo R, Winnink J, et al. A methodology for identifying breakthrough topics using structural entropy[J]. Information Processing & Management, 2022, 59(2): 102862.
25 何郁冰, 林欣慧. 基于复杂网络演化博弈的颠覆性技术扩散研究[J]. 软科学, 2024, 38(6): 28-36.
26 龚志, 邵曦. 基于多模态的音乐推荐系统[J]. 南京信息工程大学学报(自然科学版), 2019, 11(1): 68-76.
27 Shorten C, Khoshgoftaar T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 2019, 6(1): Article No.60.
28 穰雨辰, 马静. 基于图像字幕的多模态对齐情感分析模型[J]. 数据分析与知识发现, 2025, 9(1): 100-109.
29 陈巧红, 孙佳锦, 漏杨波, 等. 基于多任务学习与层叠Transformer的多模态情感分析模型[J]. 浙江大学学报(工学版), 2023, 57(12): 2421-2429.
30 强子珊, 顾益军. 基于多模态异质图的社交媒体谣言检测模型[J]. 数据分析与知识发现, 2023, 7(11): 68-78.
31 王壮, 隋杰. 基于多级融合的多模态谣言检测模型[J]. 计算机工程与设计, 2022, 43(6): 1756-1761.
32 韩普, 叶东宇, 陈文祺, 等. 面向多模态医疗健康数据的知识组织模式研究[J]. 现代情报, 2023, 43(10): 27-34, 151.
33 Pennington J, Socher R, Manning C. GloVe: global vectors for word representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1532-1543.
34 Yang L B, Cai X Y, Pan S R, et al. Multi-document summarization based on sentence cluster using non-negative matrix factorization[J]. Journal of Intelligent & Fuzzy Systems, 33(3): 1867-1879.
35 王秀红, 高敏. 基于BERT-LDA的关键技术识别方法及其实证研究——以农业机器人为例[J]. 图书情报工作, 2021, 65(22): 114-125.
36 王海军, 于佳文. 基于专利发展路径的颠覆性技术识别: 以智能语音领域为例[J]. 科技管理研究, 2022, 42(6): 170-181.
37 黄鲁成, 蒋林杉, 吴菲菲. 萌芽期颠覆性技术识别研究[J]. 科技进步与对策, 2019, 36(1): 10-17.
38 周萌, 朱相丽. 新兴技术概念辨析及其识别方法研究进展[J]. 情报理论与实践, 2019, 42(10): 162-169.
39 Jia W F, Wang S, Xie Y P, et al. Disruptive technology identification of intelligent logistics robots in AIoT industry: based on attributes and functions analysis[J]. Systems Research and Behavioral Science, 2022, 39(3): 557-568.
40 谭晓, 西桂权, 苏娜, 等. 科学—技术—项目联动视角下颠覆性技术识别研究[J]. 情报杂志, 2023, 42(2): 82-91.
41 Verhoeven D, Bakker J, Veugelers R. Measuring technological novelty with patent-based indicators[J]. Research Policy, 2016, 45(3): 707-723.
42 Shane S. Technological opportunities and new firm creation[J]. Management Science, 2001, 47(2): 205-220.
43 Khattar D, Goud J S, Gupta M, et al. MVAE: multimodal variational autoencoder for fake news detection[C]// Proceedings of the World Wide Web Conference. New York: ACM Press, 2019: 2915-2921.
44 Liu Z, Shen Y, Lakshminarasimhan V B, et al. Efficient low-rank multimodal fusion with modality-specific factors[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018: 2247-2256.
45 倪亮, 吴鹏, 周雪晴. 基于深度学习的多模态新闻数据主题发现研究[J]. 数据分析与知识发现, 2024, 8(3): 85-97. |
|
|
|
