Joint Workshop of the 5th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2024) and the 4th AI + Informetrics (AII2024)
at the iConference2024, Changchun, China and Online
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
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 . As a crucial medium for knowledge transmission, scientific documents that contain a large number of knowledge entities attract the attention of scholars . 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 . 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 .
This joint workshop entitles these two cutting-edge and cross-disciplinary directions as:
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:
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).
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.
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
Chengzhi Zhang (firstname.lastname@example.org) 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/)
Yi Zhang (email@example.com) 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 ( firstname.lastname@example.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 (email@example.com) 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://22.214.171.124/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 (firstname.lastname@example.org)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/)
Ying Ding (email@example.com)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/)
Proceedings can be accessed at http://ceur-ws.org/.
We have organized the related special issues on the topic of extraction and evaluation of knowledge entities in the following journals:
We have organized the related special issues on the topic of AI + Informetrics in the following journals: