|
|
|
| Interdisciplinary Event Knowledge Fusion Research for Library Digital Collections |
| Wang Zhongyi, Wang Zeren, Li Zhipeng, Zhang Jiexin |
| School of Information Management, Central China Normal University, Wuhan 430079 |
|
|
|
|
Abstract In the current big science era, the research problems encountered are increasingly complex. Thus, interdisciplinary research has gradually developed as a tool for solving these complex problems, which has led to a growing demand for interdisciplinary knowledge services. Therefore, librarians must gather the knowledge required to solve complex problems from different disciplines and provide comprehensive, interdisciplinary knowledge services. The fusion of interdisciplinary event knowledge has become crucial in addressing this challenge. Starting with the fusion of interdisciplinary event knowledge, this study first proposes a model for extracting interdisciplinary event knowledge based on argumentative semantic associations to extract relevant event knowledge from digital library collections across various disciplines. Subsequently, methods are introduced to fuse and generate interdisciplinary linear and nonlinear event knowledge. Finally, this study conducts empirical research in the field of climate change to demonstrate the feasibility and effectiveness of this approach.
|
|
Received: 06 May 2024
|
|
|
|
1 吕冬晴, 阮选敏, 李江, 等. 跨学科知识融合对D指数的影响[J]. 情报学报, 2022, 41(3): 263-274.
2 王兴元, 姬志恒. 基于知识分类的跨学科交叉创新组织机制研究[J]. 理论学刊, 2012(11): 52-56.
3 王忠义, 夏立新, 李玉海. 基于知识内容的数字图书馆跨学科多粒度知识表示模型构建[J]. 中国图书馆学报, 2019, 45(6): 50-64.
4 孙传明, 陈熙, 王萍. 基于在线评论的非遗纪录片受众需求事理图谱构建研究[J]. 情报科学, 2023, 41(9): 107-114.
5 万齐智, 万常选, 胡蓉, 等. 基于句法语义依存分析的中文金融事件抽取[J]. 计算机学报, 2021, 44(3): 508-530.
6 Li Q, Ji H, Hong Y, et al. Constructing information networks using one single model[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1846-1851.
7 郭鑫, 高彩翔, 陈千, 等. 面向新冠新闻的三阶段篇章级事件抽取方法[J]. 计算机工程与应用, 2023, 59(3): 150-157.
8 Zheng S, Cao W, Xu W, et al. Doc2EDAG: an end-to-end document-level framework for Chinese financial event extraction[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2019: 337-346.
9 Xu R X, Liu T Y, Li L, et al. Document-level event extraction via heterogeneous graph-based interaction model with a tracker[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2021: 3533-3546.
10 Gou J, Jiang Y L, Wu Y Y, et al. A new knowledge fusion method based on semantic rules[C]// Proceedings of the 8th International Conference on Signal Processing. Piscataway: IEEE, 2006: 1865-1868.
11 Xie N F, Wang W S, Yang X R, et al. Rule-based agricultural knowledge fusion in web information integration[J]. Sensor Letters, 2012, 10(1/2): 635-638.
12 Santos E, Wilkinson J T, Santos E E. Fusing multiple Bayesian knowledge sources[J]. International Journal of Approximate Reasoning, 2011, 52(7): 935-947.
13 Andrade D, Horeis T, Sick B. Knowledge fusion using Dempster-Shafer theory and the imprecise Dirichlet model[C]// Proceedings of the 2008 IEEE Conference on Soft Computing in Industrial Applications. Piscataway: IEEE, 2008: 142-148.
14 鲁慧民, 冯博琴, 赵英良, 等. 一种基于扩展主题图的分布式知识融合[J]. 吉林大学学报(理学版), 2009, 47(3): 543-547.
15 刘志辉, 魏娟霞, 张均胜. 基于知识图谱的科技创新指标自适应计算方法研究[J]. 情报学报, 2019, 38(8): 826-837.
16 张海涛, 栾宇, 周红磊, 等. 总体国家安全观下重大突发事件的智能决策情报体系研究[J]. 情报学报, 2022, 41(11): 1174-1187.
17 Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
18 Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2017: 6000-6010.
19 Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2019: 4171-4186.
20 张智雄, 刘欢, 丁良萍, 等. 不同深度学习模型的科技论文摘要语步识别效果对比研究[J]. 数据分析与知识发现, 2019, 3(12): 1-9.
21 周丽娜, 常笑, 胡枫. 利用邻接结构熵确定超网络关键节点[J]. 计算机工程与应用, 2022, 58(8): 76-82.
22 张诗莹, 李阳. 融合事理知识图谱与网络舆情分析的突发事件情报支持路径及实证研究——以危化品事故为例[J]. 信息资源管理学报, 2023, 13(4): 60-71.
23 于文轩, 田丹宇. 美国和墨西哥应对气候变化立法及其借鉴意义[J]. 江苏大学学报(社会科学版), 2016, 18(2): 1-6, 21.
24 Roland-Holst D. Climate action, energy efficiency, and job creation in California[R/OL]. Berkeley: University of California, (2008-09-01) [2025-03-13]. https://laborcenter.berkeley.edu/pdf/2008/CAEfficiency_Jobs_DRH080901.pdf.
25 曾以禹, 吴柏海, 周彩贤, 等. 碳交易市场设计支持森林生态补偿研究[J]. 农业经济问题, 2014, 35(6): 67-76.
26 彭红军, 徐笑, 俞小平. 林业碳汇产品价值实现路径综述[J]. 南京林业大学学报(自然科学版), 2022, 46(6): 177-186.
27 曾维忠, 刘胜, 杨帆, 等. 扶贫视域下的森林碳汇研究综述[J]. 农业经济问题, 2017, 38(2): 102-109, 3.
28 王联合, 陈凯. 本体安全与社会认同: 美国次国家行为体参与中美气候治理合作的动向探析[J]. 同济大学学报(社会科学版), 2023, 34(6): 36-46. |
|
|
|
