Retrieval of Scientific and Technological Resources for Experts and Scholars

TL;DR

This paper reviews methods for building expert and scholar information databases to improve retrieval and matching of scientific resources, addressing issues of information asymmetry and timely connection.

Contribution

It systematically analyzes research in text relation extraction, knowledge representation learning, text vector retrieval, and visualization for expert and scholar resource retrieval.

Findings

Identifies key techniques in text relation extraction and knowledge representation.

Highlights the importance of vector retrieval methods for scientific resource matching.

Discusses visualization systems to enhance resource accessibility.

Abstract

Institutions of higher learning, research institutes and other scientific research units have abundant scientific and technological resources of experts and scholars, and these talents with great scientific and technological innovation ability are an important force to promote industrial upgrading. The scientific and technological resources of experts and scholars are mainly composed of basic attributes and scientific research achievements. The basic attributes include information such as research interests, institutions, and educational work experience. However, due to information asymmetry and other reasons, the scientific and technological resources of experts and scholars cannot be connected with the society in a timely manner, and social needs cannot be accurately matched with experts and scholars. Therefore, it is very necessary to build an expert and scholar information database and provide relevant expert and scholar retrieval services. This paper sorts out the related research work in this field from four aspects: text relation extraction, text knowledge representation learning, text vector retrieval and visualization system.