AI Open
AI Open is a peer-reviewed open access journal published by KeAi, the Beijing-based joint venture of China Science Publishing and Media and Elsevier, launched in 2020 and devoted to artificial intelligence theory and applications, with a 2025 Journal Impact Factor of 8.9《AI 开放》(AI Open)是由(KeAi)出版的同行评审开放获取期刊,2020 年创刊,专注人工智能的理论与应用,2025 年影响因子为 8.9;(KeAi)是(China Science Publishing and Media)与(Elsevier)在北京设立的合资出版机构。
Scope
- The journal welcomes research articles, review papers, perspectives, short communications and technical notes on all aspects of artificial intelligence该刊接收研究论文、综述、观点、短文与技术札记,覆盖人工智能的各个方面。
- Topics include deep learning and representation learning, machine learning and data mining, and knowledge representation, reasoning and logic主题包括深度学习与表示学习、机器学习与数据挖掘,以及知识表示、推理与逻辑。
- Further areas are graph theory and graph mining, constraints, satisfiability and search, and knowledge graphs and their applications其他方向还有图论与图挖掘、约束、可满足性与搜索,以及知识图谱及其应用。
- The scope also covers agent-based and multi-agent systems and web and knowledge-based information systems其范围还涵盖基于智能体与多智能体系统,以及网络与基于知识的信息系统。
- Natural language processing, image processing and analysis, and uncertainty are additional core topics自然语言处理、图像处理与分析以及不确定性也是其核心主题。
- Emerging directions such as brain-based learning and implicit cognition and learning also fall within scope基于大脑的学习与内隐认知与学习等新兴方向同样在其收稿范围之内。
- The journal is indexed in the Emerging Sources Citation Index and sits in the first quartile of the Computer Science, Interdisciplinary Applications category该刊被(Emerging Sources Citation Index)收录,并位居计算机科学交叉应用类的第一四分位。
History
- AI Open was launched in 2020, with its first volume appearing that year as an open access title(AI Open)于 2020 年创刊,首卷当年即以开放获取形式出版。
- It is published by KeAi, a joint venture of China Science Publishing and Media and Elsevier based in Beijing, with publishing services provided by Elsevier该刊由(KeAi)出版,后者由(China Science Publishing and Media)与(Elsevier)合资创办、总部设于北京,出版服务由(Elsevier)提供。
- The journal carries the China serial number CN 10-2107/TP and the electronic identifier 2666-6510该刊国内统一刊号(CN 10-2107/TP)与电子刊号 2666-6510。
- Articles published from Volume 1 in 2020 were accepted into the Emerging Sources Citation Index in 2024自 2020 年第一卷起发表的文章,已于 2024 年被(Emerging Sources Citation Index)收录。
- A fully open access journal, it applies an article publishing charge of USD 900 and offers fee agreements through participating institutions作为完全开放获取期刊,其文章处理费为 900 美元,并通过参与机构提供费用协议。
- Its first volume included a review of graph neural networks that became one of the most cited papers in the field该刊首卷刊载的一篇图神经网络综述已成为该领域被引次数最多的论文之一。[1]
- Its Journal Impact Factor rose sharply to 14.8 in 2024 before settling at 8.9 in 2025其影响因子在 2024 年大幅升至 14.8,随后在 2025 年回落至 8.9。
Editors
- Jie Tang of Tsinghua University serves as Editor-in-Chief and works on data mining and social network analysis主编(Jie Tang)来自(Tsinghua University),从事数据挖掘与社会网络分析研究。
- Bjorn W. Schuller of Imperial College London is co-Editor-in-Chief and works on machine learning and speech and affect analysis共同主编(Bjorn W. Schuller)来自(Imperial College London),从事机器学习以及语音与情感分析研究。
- Associate editors include Wendy Hall of the University of Southampton and Michalis Vazirgiannis of Ecole Polytechnique副编辑包括(University of Southampton)的(Wendy Hall)与(Ecole Polytechnique)的(Michalis Vazirgiannis)。
- The associate editor team also draws on scholars from Renmin University of China, Fudan University, USTC and Hefei University of Technology副编辑团队还汇聚了来自(Renmin University of China)、(Fudan University)、(USTC)与(Hefei University of Technology)的学者。
- International associate editors come from institutions such as the University of the Basque Country, the University of Montreal and the University of Illinois Urbana-Champaign国际副编辑来自(University of the Basque Country)、(University of Montreal)与(University of Illinois Urbana-Champaign)等机构。
- It is peer reviewed with the process managed by KeAi, and the editorial board spans Asia, Europe and North America该刊实行同行评审,评审流程由(KeAi)管理,编委会成员遍布亚洲、欧洲与北美。
References
- ^Zhou J, Cui G, Hu S, et al. Graph neural networks: A review of methods and applications. AI Open, 2020, 1: 57-81. DOI: 10.1016/j.aiopen.2021.01.001