Philipp Mayr – författare

This two-volume set LNCS 11437 and 11438 constitutes the refereed proceedings of the 41st European Conference on IR Research, ECIR 2019, held in Cologne, Germany, in April 2019.The 48 full papers presented together with 2 keynote papers, 44 short papers, 8 demonstration papers, 8 invited CLEF papers, 11 doctoral consortium papers, 4 workshop papers, and 4 tutorials were carefully reviewed and selected from 365 submissions. They were organized in topical sections named: Modeling Relations; Classification and Search; Recommender Systems; Graphs; Query Analytics; Representation; Reproducibility (Systems); Reproducibility (Application); Neural IR; Cross Lingual IR; QA and Conversational Search; Topic Modeling; Metrics; Image IR; Short Papers; Demonstration Papers; CLEF Organizers Lab Track; Doctoral Consortium Papers; Workshops; and Tutorials.

This two-volume set LNCS 11437 and 11438 constitutes the refereed proceedings of the 41st European Conference on IR Research, ECIR 2019, held in Cologne, Germany, in April 2019.The 48 full papers presented together with 2 keynote papers, 44 short papers, 8 demonstration papers, 8 invited CLEF papers, 11 doctoral consortium papers, 4 workshop papers, and 4 tutorials were carefully reviewed and selected from 365 submissions. They were organized in topical sections named: Modeling Relations; Classification and Search; Recommender Systems; Graphs; Query Analytics; Representation; Reproducibility (Systems); Reproducibility (Application); Neural IR; Cross Lingual IR; QA and Conversational Search; Topic Modeling; Metrics; Image IR; Short Papers; Demonstration Papers; CLEF Organizers Lab Track; Doctoral Consortium Papers; Workshops; and Tutorials.

The deal with topics ranging from information-system design on social concepts to analyzing complex social systems using computational methods or explore socio-technical techniques using social sciences methods.

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.