Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences
This addresses the problem of information overload for researchers in academia, though it appears incremental as it applies existing deep learning techniques to a specific domain.
The paper tackles the challenge of extracting valuable information from the massive data generated by scientific and technological academic conferences, such as papers and researchers, by proposing a deep learning-based system to construct a knowledge graph and accurate portraits, enabling faster access to research information.
In recent years, with the continuous progress of science and technology, the number of scientific research achievements is increasing day by day, as the exchange platform and medium of scientific research achievements, the scientific and technological academic conferences have become more and more abundant. The convening of scientific and technological academic conferences will bring large number of academic papers, researchers, research institutions and other data, and the massive data brings difficulties for researchers to obtain valuable information. Therefore, it is of great significance to use deep learning technology to mine the core information in the data of scientific and technological academic conferences, and to realize a knowledge graph and accurate portrait system of scientific and technological academic conferences, so that researchers can obtain scientific research information faster.