Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences

TL;DR

This paper proposes a method to construct a knowledge graph and accurate researcher portraits from conference data, facilitating faster access to scientific research information amid increasing conference data.

Contribution

It introduces a novel approach leveraging deep learning to mine core information and build knowledge graphs and researcher portraits from conference data.

Findings

Effective extraction of core conference information

Successful construction of knowledge graph and researcher portraits

Improved efficiency in accessing scientific research data

Abstract

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.