From Knowledge Extraction to Technological Forecasting

Beschreibung

This collection features innovative, interdisciplinary studies in information science, technology, innovation management, and artificial intelligence (AI). It presents new methodological developments and empirical research, highlighting how advanced AI techniques are transforming our methods of knowledge discovery 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 informetrics 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. Integrates AI with informetrics to transform research on science, technology, and innovation; Delivers advanced methods for extracting and analyzing knowledge from scientific and patent data; Provides a complete pipeline for tracking technological change, forecasting trends, and guiding innovation.

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.

Autor*in

Yi Zhang

Themen in »From Knowledge Extraction to Technological Forecasting«

Entity extraction and recognition Information retrieval Informetrics Innovation studies Knowledge discovery Large language models Scientometrics Technological forecasting Technology management

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