Accurate Portraits of Scientific Resources and Knowledge Service Components

TL;DR

This paper discusses constructing precise representations of scientific resources using knowledge graph technologies to better manage and extract valuable information from the rapidly growing scientific data on the internet.

Contribution

It proposes a method to create accurate portraits of scientific resources by integrating structured and unstructured data with knowledge graph techniques.

Findings

Enhanced resource representation accuracy

Improved information extraction from scientific texts

Effective integration of structured and unstructured data

Abstract

With the advent of the cloud computing era, the cost of creating, capturing and managing information has gradually decreased. The amount of data in the Internet is also showing explosive growth, and more and more scientific and technological resources are uploaded to the network. Different from news and social media data ubiquitous in the Internet, the main body of scientific and technological resources is composed of academic-style resources or entities such as papers, patents, authors, and research institutions. There is a rich relationship network between resources, from which a large amount of cutting-edge scientific and technological information can be mined. There are a large number of management and classification standards for existing scientific and technological resources, but these standards are difficult to completely cover all entities and associations of scientific and technological resources, and cannot accurately extract important information contained in scientific and technological resources. How to construct a complete and accurate representation of scientific and technological resources from structured and unstructured reports and texts in the network, and how to tap the potential value of scientific and technological resources is an urgent problem. The solution is to construct accurate portraits of scientific and technological resources in combination with knowledge graph related technologies.