|
|
|
| Computational Reasoning and Application Prospects of Social-Knowledge Big Graph |
| Liu Zhenghao1,2,3,4, Deng Yufeng1,2,3, Li Hanzhi1,2,3, Zheng Ziyang1,2,3,5, Ding Zhen1,2,3, Ma Feicheng1,2,3 |
1.School of Information Management, Wuhan University, Wuhan 430072
2.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
3.Institute of Big Data, Wuhan University, Wuhan 430072
4.Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong 999077
5.Student Engineering Training and Innovation Practice Center, Wuhan University, Wuhan 430072 |
|
|
|
|
Abstract Social-knowledge big graphs inherit and enrich the concepts of social networks and knowledge graphs, covering a variety of human-centered entities and dynamically changing multiple semantic associations. It is used in application scenarios, such as knowledge discovery, reasoning, and social recommendations in many fields. These materials have broad application prospects and value. This article focuses on exploring the value of social-knowledge graphs in computational reasoning and application scenarios, and looks forward to future development trends. Compared with traditional knowledge representation learning and knowledge reasoning, social-knowledge graphs are used in computing and reasoning tasks for complex decision-making scenarios because of their multifaceted nature, ease of calculation, dynamic timeliness, and interactive contagion. It has significant advantages including higher algorithm integration, innovation, and interpretability. In specific application scenarios, social-knowledge graphs have shown good application prospects in fields such as smart finance, social e-commerce, intelligence services, digital humanities, and crisis communication. In future, we will consider relying on cutting-edge technology support and combining it with specific field issues to further enrich the connotations and expand the application boundaries of social-knowledge big graphs.
|
|
Received: 10 January 2025
|
|
|
|
1 贾君枝. 知识组织中多视角知识表示方法的内在逻辑、类型及实现方式[J]. 中国图书馆学报, 2025, 51(1): 49-60.
2 吴江, 于洋, 丁宏昊, 等. 时变网络研究: 概念、应用与展望[J]. 情报学报, 2025, 44(1): 103-122.
3 Xu R Y, Chen H L, Zhao J L. SocioLink: leveraging relational information in knowledge graphs for startup recommendations[J]. Journal of Management Information Systems, 2023, 40(2): 655-682.
4 D?rpinghaus J, Klante S, Christian M, et al. From social networks to knowledge graphs: a plea for interdisciplinary approaches[J]. Social Sciences & Humanities Open, 2022, 6(1): 100337.
5 Zhang D S, Zhou L N, Tao J, et al. KETCH: a knowledge-enhanced transformer-based approach to suicidal ideation detection from social media content[J]. Information Systems Research, 2024, 36(1): 572-599.
6 张彬, 祖后敏, 吴姣. 融合社交关系和知识图谱的双图注意力推荐模型[J]. 现代情报, 2025, 45(4): 12-22.
7 洪亮, 马费成. 面向大数据管理决策的知识关联分析与知识大图构建[J]. 管理世界, 2022, 38(1): 207-219.
8 陈国青, 张维, 任之光, 等. 面向大数据管理决策研究的全景式PAGE框架[J]. 管理科学学报, 2023, 26(5): 4-22.
9 Ju W, Fang Z, Gu Y Y, et al. A comprehensive survey on deep graph representation learning[J]. Neural Networks, 2024, 173: 106207.
10 Tu C C, Zeng X K, Wang H, et al. A unified framework for community detection and network representation learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(6): 1051-1065.
11 Tan Q Y, Liu N H, Hu X. Deep representation learning for social network analysis[J]. Frontiers in Big Data, 2019, 2: 2.
12 Bordes A, Usunier N, Garcia-Durán A, et al. Translating embeddings for modeling multi-relational data[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2013: 2787-2795.
13 Wang Z, Zhang J W, Feng J L, et al. Knowledge graph embedding by translating on hyperplanes[C]// Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2014: 1112-1119.
14 Lin Y K, Liu Z Y, Luan H B, et al. Modeling relation paths for representation learning of knowledge bases[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2015: 705-714.
15 徐涌鑫, 赵俊峰, 王亚沙, 等. 时序知识图谱表示学习[J]. 计算机科学, 2022, 49(9): 162-171.
16 Fang Q, Zhang X W, Hu J, et al. Contrastive multi-modal knowledge graph representation learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(9): 8983-8996.
17 An B, Chen B, Han X P, et al. Accurate text-enhanced knowledge graph representation learning[C]// Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2018: 745-755.
18 Xie R B, Liu Z Y, Jia J, et al. Representation learning of knowledge graphs with entity descriptions[C]// Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2016: 2659-2665.
19 Huang C, Xu H C, Xu Y, et al. Knowledge-aware coupled graph neural network for social recommendation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(5): 4115-4122.
20 Shen Y H, Jiang X H, Li Z J, et al. UniSKGRep: a unified representation learning framework of social network and knowledge graph[J]. Neural Networks, 2023, 158: 142-153.
21 Boccaletti S, Bianconi G, Criado R, et al. The structure and dynamics of multilayer networks[J]. Physics Reports, 2014, 544(1): 1-122.
22 Zhou L K, Yang Y, Ren X, et al. Dynamic network embedding by modeling triadic closure process[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 571-578.
23 Kumar S, Zhang X K, Leskovec J. Learning dynamic embeddings from temporal interactions[OL]. (2018-12-06). https://arxiv.org/pdf/1812.02289.
24 Mohan A, Pramod K V. Temporal network embedding using graph attention network[J]. Complex & Intelligent Systems, 2022, 8(1): 13-27.
25 余艳, 张文, 熊飞宇, 等. 融合知识图谱与神经网络赋能数智化管理决策[J]. 管理科学学报, 2023, 26(5): 231-247.
26 Lecocq R, Martineau é, Caropreso M F. An ontology-based social network analysis prototype[C]// Proceedings of the 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support. Piscataway: IEEE, 2013: 149-154.
27 侯中妮, 靳小龙, 陈剑赟, 等. 知识图谱可解释推理研究综述[J]. 软件学报, 2022, 33(12): 4644-4667.
28 Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2D knowledge graph embeddings[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 1811-1818.
29 Lao N, Cohen W W. Relational retrieval using a combination of path-constrained random walks[J]. Machine Learning, 2010, 81(1): 53-67.
30 沈英汉, 江旭晖, 王元卓, 等. 时态知识图谱的推理研究综述[J]. 计算机学报, 2023, 46(6): 1272-1301.
31 Liu Z H, Zhang Z J, Zeng X. Risk identification and management through knowledge association: a financial event evolution knowledge graph approach[J]. Expert Systems with Applications, 2024, 252: 123999.
32 李玥琪, 王晰巍, 王楠阿雪, 等. 突发事件下社交媒体网络舆情风险识别及预警模型研究[J]. 情报学报, 2022, 41(10): 1085-1099.
33 Tang X B, Fu W G, Liu Y. Knowledge big graph fusing ontology with property graph: a case study of financial ownership network[J]. Knowledge Organization, 2021, 48(1): 55-71.
34 Chen K, Li X, Luo P, et al. News-induced dynamic networks for market signaling: understanding the impact of news on firm equity value[J]. Information Systems Research, 2021, 32(2): 356-377.
35 Jiang N, Gao L, Duan F X, et al. SAN: attention-based social aggregation neural networks for recommendation system[J]. International Journal of Intelligent Systems, 2022, 37(6): 3373-3393.
36 Lu Y H, Tong X Y, Xiong X, et al. Knowledge graph enhanced citation recommendation model for patent examiners[J]. Scientometrics, 2024, 129(4): 2181-2203.
37 Xu Y H, Zhou D N, Ma J. Scholar-friend recommendation in online academic communities: an approach based on heterogeneous network[J]. Decision Support Systems, 2019, 119: 1-13.
38 Li G T, Siddharth L, Luo J X. Embedding knowledge graph of patent metadata to measure knowledge proximity[J]. Journal of the Association for Information Science and Technology, 2023, 74(4): 476-490.
39 潘俊. 面向数字人文的人物分布式语义表示研究——基于CBDB数据库和古籍文献[J]. 图书馆杂志, 2020, 39(8): 94-102.
40 严承希, 王军. 数字人文视角: 基于符号分析法的宋代政治网络可视化研究[J]. 中国图书馆学报, 2018, 44(5): 87-103.
41 王晓光, 侯西龙, 程航航, 等. 敦煌壁画叙词表构建与关联数据发布[J]. 中国图书馆学报, 2020, 46(4): 69-84.
42 Mirbabaie M, Stieglitz S, Brünker F. Dynamics of convergence behaviour in social media crisis communication—a complexity perspective[J]. Information Technology & People, 2022, 35(1): 232-258.
43 路晓蒙, 王一冰, 吴卫星. 传统投资顾问和智能投资顾问: 替代还是互补?[J]. 管理世界, 2023, 39(10): 74-98.
44 O’Connor L G, Dillingham L L. Personal experience as social capital in online investor forums[J]. Library & Information Science Research, 2014, 36(1): 27-35.
45 Liu W W, Yang J Y, Chen J R, et al. How social-network attention and sentiment of investors affect commodity futures market returns: new evidence from China[J]. SAGE Open, 2023, 2023(1). DOI: 10.1177/21582440231152131.
46 毛瑞彬, 朱菁, 李爱文, 等. 基于自然语言处理的产业链知识图谱构建[J]. 情报学报, 2022, 41(3): 287-299.
47 吕华揆, 洪亮, 马费成. 金融股权知识图谱构建与应用[J]. 数据分析与知识发现, 2020, 4(5): 27-37.
48 洪亮, 欧阳晓凤. 金融股权知识大图的知识关联发现与风险分析[J]. 管理科学学报, 2022, 25(4): 44-66.
49 Xu Y S, Li J Q, Qi H Y. The spatial correlation effect of real-estate financial risk in China: a social network analysis[J]. Sustainability, 2022, 14(12): 7085.
50 唐旭丽, 马费成, 傅维刚, 等. 知识关联视角下的金融知识表示及风险识别[J]. 情报学报, 2019, 38(3): 286-298.
51 Schr?der M, Krüger J, Foroutan N, et al. Towards cyber mapping the German financial system with knowledge graphs[C]// Proceedings of 21st European Semantic Web Conference. Cham: Springer, 2024: 270-288.
52 Li Z Y, Zhao S D, Ding X, et al. EEG: knowledge base for event evolutionary principles and patterns[C]// Social Media Processing. Cham: Springer, 2017: 40-52.
53 王玲. 中国社交电商行业发展现状分析[J]. 互联网经济, 2019(9/10): 80-89.
54 Huang Z, Benyoucef M. From e-commerce to social commerce: a close look at design features[J]. Electronic Commerce Research and Applications, 2013, 12(4): 246-259.
55 Farzana S, Zhou Q Z, Ristoski P. Knowledge graph-enhanced neural query rewriting[C]// Companion Proceedings of the ACM Web Conference 2023. New York: ACM Press, 2023: 911-919.
56 Chen J, Ma L Y, Li X H, et al. Relation labeling in product knowledge graphs with large language models for e-commerce[J]. International Journal of Machine Learning and Cybernetics, 2024, 15(12): 5725-5743.
57 Das S, Saboo R, Vadrevu C S K, et al. Applications of LLMs in E-commerce search and product knowledge graph: the DoorDash case study[C]// Proceedings of the 17th ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2024: 1163-1164.
58 金海波, 冯雨静. 社交知识感知网络推荐算法[J]. 计算机科学与探索, 2025, 19(4): 1105-1114.
59 陈烨, 王乐, 陈天雨, 等. 基于社会网络分析的社会化问答平台用户画像研究[J]. 情报学报, 2021, 40(4): 414-423.
60 王惠, 冯子宸. 数字人文视域下多模态名人档案资源知识服务模式研究[J]. 情报科学, 2024, 42(11): 31-40.
61 Tang J. AMiner: mining deep knowledge from big scholar data[C]// Proceedings of the 25th International Conference Companion on World Wide Web. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2016: 373.
62 Kim Y, Yoo S. Global financial investment connectedness, ICT, and intellectual property strategies: a country-level empirical analysis[J]. Sustainability, 2024, 16(8): 3282.
63 唐恒,何锦润, 孙莹琳, 等. 专利创造激励政策协同网络演化研究[J]. 科学学与科学技术管理, 2019, 40(9): 48-62.
64 张玲玲, 黄务兰. 基于ChatGPT API和提示词工程的专利知识图谱构建[J]. 情报杂志, 2025, 44(3): 180-187.
65 谭春辉, 李玥澎, 梁远亮, 等. 基于空间引力模型的科研机构合作联结强度分析——以国家科技进步奖获奖项目为例[J]. 情报资料工作, 2022, 43(5): 52-59.
66 范昊, 王一帆. 知识关联视角下标准文档的多粒度知识组织方法研究[J]. 信息资源管理学报, 2024, 14(4): 133-145.
67 Zhang C, Feng X J, Li Y H, et al. Scientific collaboration network structure and connectedness among the “Belt and Road” countries[J]. Journal of the Knowledge Economy, 2025, 16(2): 7908-7933.
68 刘虹, 李煜. 时序视域下图情领域重要国家的学术贡献研究[J]. 图书情报工作, 2018, 62(23): 87-96.
69 Du W, Cheng X S, Yang C, et al. Establishing interoperability among knowledge organization systems for research management: a social network approach[J]. Scientometrics, 2017, 112(3): 1489-1506.
70 卢超, 李梦婷, 陈秀娟, 等. 合贡献者网络的结构特征及其合作群体识别应用研究[J]. 情报学报, 2024, 43(7): 773-788.
71 D?rpinghaus J, Jacobs M. Knowledge detection and discovery using semantic graph embeddings on large knowledge graphs generated on text mining results[C]// Proceedings of the 2020 15th Conference on Computer Science and Information Systems. Piscataway: IEEE, 2020: 169-178.
72 邢晓昭, 任亮, 雷孝平, 等. 基于专利主题演化的颠覆性技术识别研究——以类脑智能领域为例[J]. 情报科学, 2023, 41(3): 81-88.
73 周文浩, 吴炳毅, 李海林, 等. 基于动态复杂网络的突破性技术创新识别方法研究[J]. 情报学报, 2025, 44(1): 20-34.
74 Dotsika F, Watkins A. Identifying potentially disruptive trends by means of keyword network analysis[J]. Technological Forecasting and Social Change, 2017, 119: 114-127.
75 苑朋彬, 邢晓昭. 专利知识流动视角下的颠覆性技术方向识别研究——以6G太赫兹通信技术领域为例[J]. 情报杂志, 2023, 42(11): 142-146.
76 Gardiner E, Musto R G. The digital humanities: a primer for students and scholars[M]. Cambridge: Cambridge University Press, 2015.
77 李章超, 何琳. 文化遗产语义组织研究进展[J]. 图书情报工作, 2020, 64(7): 4-12.
78 施晓华, 王昕. 数字人文社会网络分析方法应用与研究[J]. 图书馆杂志, 2020, 39(5): 93-99.
79 王晓光, 侯西龙. 面向活化利用的文化遗产智慧数据建设论纲[J]. 信息资源管理学报, 2023, 13(5): 4-14, 43.
80 王晓光, 梁梦丽, 侯西龙, 等. 文化遗产智能计算的肇始与趋势——欧洲时光机案例分析[J]. 中国图书馆学报, 2022, 48(1): 62-76.
81 洪亮, 宋睿, 朱丽雅, 等. 知识关联视角下的文化遗产知识大图研究: 理论、方法和趋势[J]. 图书情报知识, 2022(2): 133-143.
82 周莉娜, 洪亮, 高子阳. 唐诗知识图谱的构建及其智能知识服务设计[J]. 图书情报工作, 2019, 63(2): 24-33.
83 Ruiz L, Benitez J, Castillo A, et al. Digital human resource strategy: conceptualization, theoretical development, and an empirical examination of its impact on firm performance[J]. Information & Management, 2024, 61(4): 103966.
84 包弼德, 王宏苏, 傅君劢, 等. “中国历代人物传记资料库”(CBDB)的历史、方法与未来[J]. 数字人文研究, 2021, 1(1): 21-33.
85 夏翠娟. 构建数智时代社会记忆的多重证据参照体系: 理论与实践探索[J]. 中国图书馆学报, 2022, 48(5): 86-102.
86 牛力, 刘慧琳, 高晨翔. 数字记忆视角下的学术名人知识库研究[J]. 情报理论与实践, 2022, 45(9): 164-172.
87 陈涛, 刘炜, 单蓉蓉, 等. 知识图谱在数字人文中的应用研究[J]. 中国图书馆学报, 2019, 45(6): 34-49.
88 梁世豪. 社交媒体文化遗产信息组织与应用研究[D]. 长春: 吉林大学, 2024.
89 房小可, 王巧玲. 档案著录、知识关联与社会记忆重构[J]. 档案学通讯, 2021(3): 27-33.
90 Steele J L, Blau N. An analysis of police department crisis communication via social media[J]. Police Quarterly, 2023, 26(4): 520-544.
91 Li N, Zheng X M, Ni D, et al. Leadership in a crisis: a social network perspective on leader brokerage strategy, intra-organizational communication patterns, and business recovery[J]. Journal of Management, 2025, 51(5): 2041-2073.
92 苏欣宇, 张海涛, 张鑫蕊, 等. 重大突发事件下应急管理部门政府回应性的组态归因研究[J]. 情报学报, 2024, 43(11): 1297-1309.
93 何巍. 社交网络舆情多模态知识图谱构建框架研究[J]. 情报杂志, 2024, 43(1): 160-166.
94 刘雅姝, 张海涛, 徐海玲, 等. 多维特征融合的网络舆情突发事件演化话题图谱研究[J]. 情报学报, 2019, 38(8): 798-806.
95 刘政昊, 曾曦, 张志剑. 面向应急管理的金融突发事件事理知识图谱构建与分析研究[J]. 信息资源管理学报, 2022, 12(3): 137-151.
96 李纲, 王施运, 毛进, 等. 面向态势感知的国家安全事件图谱构建研究[J]. 情报学报, 2021, 40(11): 1164-1175.
97 马哲坤, 涂艳. 基于知识图谱的网络舆情突发话题内容监测研究[J]. 情报科学, 2019, 37(2): 33-39.
98 Win Myint P Y, Lo S L, Zhang Y H. Unveiling the dynamics of crisis events: Sentiment and emotion analysis via multi-task learning with attention mechanism and subject-based intent prediction[J]. Information Processing & Management, 2024, 61(4): 103695.
99 Kitchens B, Claggett J L, Abbasi A. Timely, granular, and actionable: designing a social listening platform for public health 3.0[J]. MIS Quarterly, 2024, 48(3): 899-930.
100 苏成, 赵润博, 黄雁宁, 等. 复杂信息环境下的情报快速反应机制研究[J]. 情报学报, 2024, 43(5): 528-537. |
|
|
|
