Innovation Detection and Analysis for Interdisciplinary Research

The increasingly mature artificial intelligence technologies, such as big data, deep learning, and natural language processing, provide technical support for research on automatic text understanding and bring development opportunities for innovative measurement of scientific communication. Innovation detection and analysis is a challenging and cutting-edge direction in Informatics. It is interdisciplinary, requiring considering the characteristics of different disciplines and different types of scientific outcomes to establish a comprehensive evaluation metrics system. On the other hand, metadata and content features should be considered to reflect the innovation of scientific works objectively and comprehensively. This project aims to identify the main factors of science and developing predictive models to capture its evolution to provide a broader perspective on measuring and analyzing the innovative nature of science. Advanced AI techniques, such as knowledge reasoning, large-scale pre-trained language models will be explored for potential solutions.

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