Joint Workshop of the 6^th AI + Informetrics (AII) & the 7^th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE)
- AII-EEKE 2026

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:

Keynote

Invited Speaker

Distinguished Professor Lisa M. Given

RMIT University, Melbourne

On Users, Use Cases, and the “Real World” in AI Research: An Interdisciplinary Imperative

Distinguished Professor Lisa M. Given (FASSA, FASIS&T) is Director of the Centre for Human-AI Information Environments, Director of the Social Change Enabling Impact Platform, and Distinguished Professor of Information Sciences at RMIT University (Melbourne). A Fellow of the Academy of the Social Sciences in Australia, her interdisciplinary research in human information behaviour brings a critical, social research lens to technology use and human centred design. Her studies embed social change, in workplaces and everyday life.

A Fellow, and former President, of the Association for Information Science and Technology (ASIS&T), she is Editor-in-Chief of the Journal of the Association for Information Science and Technology (JASIST) and lead author of Looking for Information: Examining Research on How People Engage with Information (2023).

Read more about Lisa Given

Programme

Submission Information

Submissions must be written in English and prepared using the Springer Lecture Notes in Computer Science (LNCS) template. Manuscripts should be submitted in PDF format via OpenReview.

All accepted papers must be presented in person by at least one registered author.

Paper types and length requirements (excluding references):

• Full Papers: at least 12 pages
• Poster & Demonstration Papers: at least 6 pages
• Abstract: up to 2 pages, no references required

Important Dates (AoE)

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.
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.
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.

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.
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.
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.
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.

Programme Committee

References

1. 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
2. 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
3. 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
4. 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

• Workshop on Extraction & Evaluation of Knowledge Entities (EEKE)
• Workshop on AI + Informetrics (AII)

Past Proceedings & Journal Special Issues