Springer From Knowledge Extraction to Technological Forecasting: New Frontiers with Artificial Intelligence

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). From Knowledge Extraction to Technological Forecasting: New Frontiers with Artificial Intelligence 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.