Call for Papers
You are invited to participate in the Joint Workshop of the 5th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2024) and the 4th AI + Informetrics (AII2024), to be held as part of the iConference2024, Changchun, China and Online, April 22 - 26, 2024
https://eeke-workshop.github.io/2024
Aim of the Workshop
In the era of big data, massive amounts of information and data have dramatically changed human civilization. The broad availability of information provides more opportunities for people, but a new challenge is rising: how can we obtain useful knowledge from numerous information sources. A knowledge entity is a relatively independent and integral knowledge module in a special discipline or a research domain [1]. As a crucial medium for knowledge transmission, scientific documents that contain a large number of knowledge entities attract the attention of scholars [2]. Complementarily, informetrics, known as the study of quantitative aspects of information, has gained great benefits from artificial intelligence (AI), with its capacities in analyzing unstructured scalable data and streams, understanding uncertain semantics, and developing robust and repeatable models. Incorporating informetrics with AI techniques has demonstrated enormous success in turning big data into big value and impact. For example, deep learning approaches enlighten studies of pattern recognition and further leverage time series to track technological change. However, how to effectively cohere the power of AI and informetrics to create cross-disciplinary solutions is still elusive from neither theoretical nor practical perspectives.
This workshop aims to engage related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents and AI + Informetrics. Specifically, knowledge entities in scientific documents may include method entities, tasks, dataset and metrics, software and tools, etc [3]. Knowledge entity application includes the construction of a knowledge entity graph and roadmap, modeling functions of knowledge entity citations, etc. There are some online platforms based on knowledge entities, e.g., SAGE Research Methods and ‘SOTA’ project. In parallel, this workshop also targets certain unsolved issues in AI + Informetrics and a wide range of its practical scenarios including: Cohering AI and informetrics to fulfill cross-disciplinary gaps from either theoretical or practical perspectives; elaborating AI-empowered informetric models with enhanced capabilities in robustness, adaptability, and effectiveness, and leveraging knowledge, concepts, and models in information management to strengthen the interpretability of AI + Informetrics to adapt to empirical needs in real-world cases [4].
This joint workshop entitles these two cutting-edge and cross-disciplinary directions as:Extraction and Evaluation of Knowledge Entity (EEKE), highlighting the development of intelligent methods for identifying knowledge entities from scientific documents, and promoting their application in broad information studies.AI + Informetrics (AII), emphasizing endeavors in interacting AI and informetrics by constructing fundamental theories, developing novel methodologies, bridging conceptual knowledge with practical uses, and creating real-word solutions.
This workshop is to 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.
Workshop Topics
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:Task and methodology from scientific documentsModel and algorithmize entity extraction from scientific documentsDataset and metrics mention extraction from scientific documentsSoftware and tool extraction from scientific documentsKnowledge entity summarizationRelation extraction of knowledge entityModeling function of knowledge entity citationInformetrics with machine learning (including deep learning)Informetrics with natural language processing or computational linguisticsInformetrics with computer visionInformetrics with other related AI techniques (e.g., information retrieval)AI for science of scienceAI for science, technology, & innovationAI for research policy and strategic managementApplication of knowledge entity extractionApplications of AI-empowered informetrics
Submission Information
All submissions must be written in English, following the CEUR-ART style and should be submitted as PDF files to EasyChair.Regular papers: 10 pages for full papers and 4 pages for short papers exclusive of unlimited pages for references.Poster & demonstration: We welcome submissions detailing original, early findings, works in progress and industrial applications of knowledge entities extraction ande evaluation for a special poster session, possibly with a 2-minute presentation in the main session. Some research track papers will also be invited to the poster track instead, although there will be no difference in the final proceedings between poster and research track submissions. These papers should follow the same format as the research track papers but can be shorter (2 pages for poster and demo papers).
All submissions will be reviewed by at least two independent reviewers. Please be aware of the fact that at least one author per paper needs to register for the workshop and attend the workshop to present the work. In case of no-show the paper (even if accepted) will be deleted from the proceedings and from the program.
Workshop proceedings will be deposited online in the CEUR workshop proceedings publication service. This way the proceedings will be permanently available and citable (digital persistent identifiers and long term preservation).
Special Issue
Accepted submissions will be invited to submit to our special issue in Technological Forecasting and Social Change. More detailed information about this special issue can be visited at: https://eeke-workshop.github.io/2024/si-eeke-aii.html.
Important Dates
All dates are Anywhere on Earth (AoE).
Deadline for submission: February 29, 2024
Notification of acceptance: March 20, 2024
Camera ready: March 30, 2024
Workshop: April 22 2024
Main Organising Committee
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, 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, 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/)
Yi Zhang (yi.zhang@uts.edu.au) works as a Senior Lecturer 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 Associate Editor for Technol. Forecast. & Soc. Change, the Editorial Board Member for the IEEE Trans. Eng. Manage., and the Advisory Board Member for the International Center for the Study of Research. (https://www.uts.edu.au/staff/yi.zhang)
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/)
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)
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@unt.edu)is a clinical assistant professor in the Department
of 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/)
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/)
Programme CommitteeAlireza Abbasi, University of New South Wales (Canberra)
Andrea Scharnhorst, DANS-KNAW
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, Virgina Tech
Ying Guo, China University of Political Science and Law
Arash Hajikhani, VTT Technical Research Centre of Finland
Saeed-Ul Hassan, Information Technology University
Jiangen He, The University of Tennessee
Zhigang Hu, South China Normal University
Bolin Hua, Peking University
Ying Huang, Wuhan University
Yong Huang, Wuhan University
Yuya Kajikawa, Tokyo University of Technology
Vivek Kumar Singh, Banaras Hindu University, Varanasi, U.P., India
Chenliang Li, Wuhan Univerisity
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 K?ln (University of Applied Sciences)
Mayank Singh, Indian Institute of Technology Gandhinagar
Bart Thijs, ECOOM, MSI, K.U.Leuven
Suppawong Tuarob, Mahidol University
Dongbo Wang, Nanjing Agricultural University
Xuefeng Wan,g 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, Nanjing University of Science and Technology
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. In: Leung, H., Li, F., Lau, R., Li, Q. (eds) Advances in Web Based Learning – ICWL 2007. ICWL 2007. Lecture Notes in Computer Science, vol 4823. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78139-4_10Ying, D., Min, S., Jia, H., Qi, Y., Erjia, Y., Lili, L., Tamy, C. entitymetrics: measuring the impact of entities. Plos One, 2013, 8(8), e71416. https://doi.org/10.1371/journal.pone.0071416Zhang, C., Mayr, P., Lu, W., & Zhang, Y. (2022). JCDL2022 workshop: extraction and evaluation of knowledge entities from scientific documents (EEKE2022). In Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries (JCDL '22). Association for Computing Machinery, New York, NY, USA, Article 54, 1–2. https://doi.org/10.1145/3529372.3530917Zhang, Y., Zhang, C., Mayr, P., & Suominen, A. An editorial of “AI?+?informetrics”: multi-disciplinary interactions in the era of big data. Scientometrics 127, 6503–6507(2022). https://doi.org/10.1007/s11192-022-04561-w
Links
Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE)Workshop on AI + Informetrics (AII)
Past Proceedings & Journal Special Issues
Proceedings can be accessed at http://ceur-ws.org/. Proceedings of EEKE-AII 2023Proceedings of EEKE 2022Proceedings of EEKE 2021Proceedings of EEKE2020Proceedings of AII 2021
We have organized the related special issues on the topic of extraction and evaluation of knowledge entities in the following journals:Aslib Journal of Information ManagementScientometricsJournal of Data and Information ScienceData and Information Management
We have organized the related special issues on the topic of AI + Informetrics in the following journals:Information Processing and ManagermentScientometrics
