From Knowledge Extraction to Technological Forecasting

New Frontiers with Artificial Intelligence
Erscheinungsjahr 2026
ISBN: 978-3-032-29302-2
Verlag: Springer

Buch, Englisch, 74 Seiten, Format (B × H): 155 mm x 235 mm

This collection features innovative, interdisciplinary studies in information science, technology and innovation management, and artificial intelligence (AI). It presents new methodological developments and empirical research, highlighting how advanced AI techniques are transforming our methods of scientific knowledge extraction and technological forecasting. The book explores a wide range of AI-driven informetric approaches, including large language model (LLM)-enhanced topic modeling, interdisciplinary analysis, reference extraction, machine learning-based diffusion measurement, and self-prompted technological forecasting. It addresses how AI can be integrated into the informetric context to convert data into valuable insights, fostering a deeper understanding of science, technology, and innovation (ST&I).

is designed for researchers, analysts, practitioners, and policymakers interested in AI for information and ST&I studies. It synthesizes methodological advances and real-world applications, showcasing AI's analytical power for knowledge discovery and exploring new directions in AI + Informetrics, with a focus on extracting and evaluating knowledge entities from scientific documents.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Yi

Zhang, Chengzhi

Mayr, Philipp

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Management Forschung & Entwicklung (F&E), Innovation
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

Preface.- Chatting With Papers: A Hybrid Approach Using LLMs and Knowledge Graphs.- LLM-based Approaches to Canonical Reference Extraction in Academic Texts: Initial Results.- Using Large Language Models in Literature Screening for Bibliometrics and Reviews: A Case Study on Health Communication Gap.- Promptsight: Forecasting Emerging Technologies via Iterative Self-prompting in Large Language Models.- Measuring Technology Diffusion Dynamics Using Patent Full-Text Data and Machine Learning.- Research on Technology Evolution Analysis Method From the Problem-Solution-Effect (PSE) Three-Dimensional Perspective.- An Innovative Topic Modeling Method Integrating Large Language Models for Topic Recognition and Evolutionary Analysis.- Interdisciplinarity and Scientific Output Quality in AI-for-Science: Implications From a Large-scale Literature Analysis of AI4Science.

Über Autor(innen)

Yi Zhang is an Associate Professor at the Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney. He holds dual PhDs in Management Science & Engineering and Software Engineering. His research interests align with intelligent bibliometrics, integrating artificial intelligence and data science techniques with bibliometric indicators to support broad studies in science, technology, and innovation. 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 and Social Change, Associate Editor for the IEEE Transactions on Engineering Management, Scientometrics, and International Journal of Computational Intelligent Systems, and the Specialty Chief Editor for Frontiers in Research Metrics and Analytics.

Chengzhi Zhang is a Professor of the Department of Information Management, Nanjing University of Science and Technology. He received his PhD degree in Information Science from Nanjing University. He has published more than 100 papers in journals including JASIST, IPM, LISR, Aslib JIM, JOI, OIR, SCIM, ACL, EMNLP, and NAACL. His current research interests include scientific text mining, knowledge entity extraction and evaluation, and social media mining. He serves as an Editorial Board Member and Managing Guest Editor for 10 international journals (Patterns, IPM, SCIM, OIR, TEL, IDD, JDIS, DIM, and DI) and a Program Committee (PC) member of several international conferences in the fields of natural language processing and scientometrics. Currently, he is focusing on scientific text mining, knowledge entity extraction and evaluation, and social media mining. He is also a visiting scholar at the University of Pittsburgh's School of Information Sciences (iSchool) and the Department of Linguistics and Translation at the City University of Hong Kong.

Philipp Mayr is a team leader in the Department of Knowledge Technologies for the Social Sciences (KTS) at the GESIS – Leibniz-Institute for the Social Sciences. He received his PhD in Applied Informetrics and Information Retrieval from the Berlin School of Library and Information Science at Humboldt University of Berlin (HU Berlin). He has published in top conferences and prestigious journals in the areas informetrics, information retrieval and digital libraries. His research group “Information and Data Retrieval” is working on methods and techniques of interactive information and dataset retrieval and maintains and further develops information systems for the social sciences. He is the main organizer of the International Workshop on Bibliometric-enhanced Information Retrieval (BIR) and Scholarly Document Processing workshop series.