About the Workshop
Artificial intelligence (AI), particularly the increasing success of large language models (LLMs), is revolutionizing the research paradigm of scientometrics and informetrics, highlighting its incredible capabilities in scalable, effective, robust, and adaptable data analytics. AI-empowered informetric models have achieved significant accomplishments in the context of scientometric studies, e.g., supporting the design of scientometric research with insights, communicating the community by combining computational models and human knowledge, and developing adaptable analytical tools for deep literature analysis.
As one of the fundamental tasks in scientometrics, extracting useful knowledge entities from massive scientific data has been a long interest of the community, while the exponentially increased data volume and modality, the complicated real-world context of diverse knowledge entities, and the adaptability of rapidly developing AI techniques to actual information retrieval scenarios further obstacle a comprehensive solution.
This joint workshop aims to engage the scientometrics community with broad open problems in AII and EEKE, foster interactive applications in the context of scientometrics, and gather researchers and practical users to open a collaborative platform for exchanging ideas, sharing pilot studies, and scoping future directions on this cutting-edge venue. We highlight the following core objectives:
Call for Papers
This workshop is primarily designed for academic researchers in broad information and library sciences, science of science, artificial intelligence, and will also be of interest to librarians, ST&I administrators and policymakers, and practitioners in any related sectors. We invite stimulating research on topics including, but not limited to, methods of knowledge entity extraction and applications of knowledge entity. Specific examples of fields of interest include:
- Bibliometrics/Scientometrics/Informetrics with large language models
- Bibliometrics/Scientometrics/Informetrics with machine learning (including deep learning)
- Bibliometrics/Scientometrics/Informetrics with natural language processing or computational linguistics
- Bibliometrics/Scientometrics/Informetrics with computer vision
- Bibliometrics/Scientometrics/Informetrics with other related AI techniques (e.g., information retrieval)
- Task and methodology from scientific documents
- Model and algorithmize entity extraction from scientific documents
- Dataset and metrics mention extraction from scientific documents
- Software and tool extraction from scientific documents
- Knowledge entity summarization
- Relation extraction of knowledge entity
- Modeling function of knowledge entity citation
- AI for science of science
- AI for science, technology, & innovation
- AI for research policy and strategic management
- Application of knowledge entity extraction
- Applications of AI-empowered informetrics
Programme
To be announced soon.
Submission Information
Submissions must be in English using the CEUR-ART style. Upload as PDF via OpenReview (link TBA). All accepted papers must be presented in person by at least one registered author.
Submit - OpenReview (TBA) Download CEUR-ART TemplateImportant Dates (AoE)
| Milestone | Date |
|---|---|
| Paper Submissions Open on OpenReview | December 8, 2025 |
| Full Paper Submission Deadline | January 15, 2026 |
| Full Paper Acceptance Notification | March 1, 2026 |
| Camera-ready Paper Submission Deadline | March 15, 2026 |
| Workshop Date | July 15, 2026 |
Chairs & Committees
General Chairs
 Yi Zhang | Yi Zhang (yi.zhang@uts.edu.au) is an Associate Professor at the Australian Artificial Intelligence Institute, University of Technology Sydney. He holds dual Ph.D. degrees in Management Science & Engineering and in Software Engineering. His research interests align with intelligent bibliometrics - incorporating artificial intelligence and data science techniques with bibliometric indicators for broad science, technology & innovation studies. He is the recipient of the 2019 Discovery Early Career Researcher Award granted by the Australian Research Council. He serves as the Executive Editor for Technological Forecasting & Social Change, Associate Editor for the IEEE Transactions on Engineering Management and Scientometrics, and the Specialty Chief Editor for Frontiers in Research Metrics and Analytics. (https://www.uts.edu.au/staff/yi.zhang) |
 Chengzhi Zhang | Chengzhi Zhang (zhangcz@njust.edu.cn) is a professor of Department of Information Management, Nanjing University of Science and Technology, China. He received his PhD degree of Information Science from Nanjing University, China. He has published more than 100 publications, including JASIST, IPM, LISR, TFSC, Aslib JIM, JOI, OIR, SCIM, ACL, NAACL, etc. His current research interests include scientific text mining, knowledge entity extraction and evaluation, social media mining. He serves as Editorial Board Member and Managing Guest Editor for 10 international journals (Patterns, IPM, SCIM, OIR, Aslib JIM, TEL, JDIS, DIM, DI, etc.) and PC members of several international conferences in fields of natural language process and scientometrics. (https://chengzhizhang.github.io/) |
 Philipp Mayr | Philipp Mayr (philipp.mayr@gesis.org) is a team leader at the GESIS - Leibniz-Institute for the Social Sciences department Knowledge Technologies for the Social Sciences (WTS). He received his PhD in applied informetrics and information retrieval from the Berlin School of Library and Information Science at Humboldt University Berlin. He has published in top conferences and prestigious journals in the areas informetrics, information retrieval and digital libraries. His research group focuses on methods and techniques for interactive information retrieval and data set search. He was the main organizer of the BIR workshops at ECIR 2014-2021 and the BIRNDL workshops at JCDL 2016 and SIGIR 2017-2019. (https://philippmayr.github.io/) |
Organization Committee
 Wei Lu | Wei Lu (weilu@whu.edu.cn) is a professor of School of Information Management and director of Information Retrieval and Knowledge Mining Center, Wuhan University. He received his PhD degree of Information Science from Wuhan University, China. His current research interests include information retrieval, text mining, QA etc. He has papers published on SIGIR, Information Sciences, JASIT, Journal of Information Science etc. He serves as diverse roles (e.g., Associate Editor, Editorial Board Member, and Managing Guest Editor) for several journals. (http://39.103.203.133/member/4) |
 Ying Ding | Ying Ding (ying.ding@austin.utexas.edu)is Bill & Lewis Suit professor at School of Information, University of Texas at Austin. She has been involved in various NIH, NSF and European-Union funded projects. She has published 240+ papers in journals, conferences, and workshops, and served as the program committee member for 200+ international conferences. She is the co-editor of book series called Semantic Web Synthesis by Morgan & Claypool publisher, the co-editor-in-chief for Data Intelligence published by MIT Press and Chinese Academy of Sciences, and serves as the editorial board member for several top journals in Information Science and Semantic Web. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies. (https://yingding.ischool.utexas.edu/) |
 Arho Suominen | Arho Suominen (Arho.Suominen@vtt.fi) is principal scientist at the VTT Technical Research Centre of Finland and Industrial professor at Tampere University (Finland). Dr. Suominen’s research focuses on qualitative and quantitative assessment of innovation systems with a special focus on quantitative methods. His prior research has been funded by the European Commission via H2020, Academy of Finland, Finnish Funding Agency for Technology, Turku University Foundation and the Fulbright Center Finland. Through the Fulbright program, he worked as Visiting Scholar at the School of Public Policy at the Georgia Institute of Technology. Dr. Suominen has a Doctor of Science (Tech.) degree from the University of Turku and holds an Officers basic degree from the National Defence University of Finland. (https://cris.vtt.fi/en/persons/arho-suominen) |
 Haihua Chen | Haihua Chen (haihua.chen@unt.edu)is an assistant professor in the Departmentof Information Science at the University of North Texas. He has expertise in applied data science, natural language processing, information retrieval, and text mining. He co-authored more than 40 publications in academic venues in both information science and computer science. He is serving as co-editor for The Electronic Library, the guest editor of Information Discovery & Delivery and Frontiers in Big Data special issues, and the reviewer for 14 peer reviewed journals and several international conferences. (https://iia.ci.unt.edu/haihua-chen/) |
Programme Committee
| Name | Affiliation |
|---|---|
| Alireza Abbasi | University of New South Wales (Canberra) |
| Iana Atanassova | CRIT, Université de Bourgogne Franche-Comté |
| Marc Bertin | Université Claude Bernard Lyon 1 |
| Katarina Boland | GESIS - Leibniz Institute for the Social Sciences |
| Yi Bu | Peking University |
| Guillaume Cabanac | IRIT - Université Paul Sabatier Toulouse 3 |
| Caitlin Cassidy | Search Technology Inc |
| Chong Chen | Beijing Normal University |
| Guo Chen | Nanjing University of Science and Technology |
| Hongshu Chen | Beijing Institute of Technology |
| Gong Cheng | Nanjing University |
| Jian Du | Peking University |
| Edward Fox | Virginia Tech |
| Ying Guo | China University of Political Science and Law |
| Arash Hajikhani | VTT Technical Research Centre of Finland |
| Jiangen He | University of Tennessee |
| Zhigang Hu | South China Normal University |
| Bolin Hua | Peking University |
| Lu Huang | Beijing Institute of Technology |
| Ying Huang | Wuhan University |
| Yong Huang | Wuhan University |
| Yuya Kajikawa | Tokyo University of Technology |
| Vivek Kumar Singh | Banaras Hindu University, Varanasi, India |
| Chenliang Li | Wuhan University |
| Kai Li | University of Tennessee |
| Chao Lu | Hohai University |
| Shutian Ma | Tencent |
| Jin Mao | Wuhan University |
| Xianling Mao | Beijing Institute of Technology |
| Chao Min | Nanjing University |
| Wolfgang Otto | GESIS - Leibniz Institute for the Social Sciences |
| Xuelian Pan | Nanjing University |
| Dwaipayan Roy | GESIS - Leibniz Institute for the Social Sciences |
| Philipp Schaer | TH Köln (University of Applied Sciences) |
| Mayank Singh | IIT Gandhinagar |
| Bart Thijs | ECOOM, MSI, KU Leuven |
| Suppawong Tuarob | Mahidol University |
| Dongbo Wang | Nanjing Agricultural University |
| Xuefeng Wang | Beijing Institute of Technology |
| Yuzhuo Wang | Anhui University |
| Dietmar Wolfram | University of Wisconsin-Milwaukee |
| Jian Wu | Old Dominion University |
| Mengjia Wu | University of Technology Sydney |
| Tianxing Wu | Southeast University |
| Xiaolan Wu | Nanjing Normal University |
| Yanghua Xiao | Fudan University |
| Jian Xu | Sun Yat-sen University |
| Shuo Xu | Beijing University of Technology |
| Erjia Yan | Drexel University |
| Heng Zhang | Central China Normal University |
| Jinzhu Zhang | Nanjing University of Science and Technology |
| Xiaojuan Zhang | Southwest University |
| Yingyi Zhang | Soochow University |
| Zhixiong Zhang | National Science Library, Chinese Academy of Sciences |
| Qingqing Zhou | Nanjing Normal University |
| Yongjun Zhu | Yonsei University |
References
- Chang, X., Zheng, Q. (2008). Knowledge Element Extraction for Knowledge-Based Learning Resources Organization. Advances in Web Based Learning - ICWL 2007, LNCS 4823. Springer. https://doi.org/10.1007/978-3-540-78139-4_10
- Ying, D., Min, S., Jia, H., Qi, Y., Erjia, Y., Lili, L., Tamy, C. (2013). entitymetrics: measuring the impact of entities. PLOS ONE, 8(8), e71416. https://doi.org/10.1371/journal.pone.0071416
- Zhang, C., Mayr, P., Lu, W., & Zhang, Y. (2022). JCDL2022 workshop: EEKE2022. JCDL ’22, Article 54, 1-2. https://doi.org/10.1145/3529372.3530917
- Zhang, Y., Zhang, C., Mayr, P., & Suominen, A. (2022). An editorial of “AI + informetrics”. Scientometrics, 127, 6503-6507. https://doi.org/10.1007/s11192-022-04561-w
Links
Past Proceedings & Journal Special Issues
Proceedings can be accessed at CEUR-WS.