An automatic knowledge graph construction system for K-12 education

Abstract

Motivated by the pressing need of educational applications with knowledge graph, we develop a system, called K12EduKG, to automatically construct knowledge graphs for K-12 educational subjects. Leveraging on heterogeneous domain-specific educational data, K12EduKG extracts educational concepts and identifies implicit relations with high educational significance. More specifically, it adopts named entity recognition (NER) techniques on educational data like curriculum standards to extract educational concepts, and employs data mining techniques to identify the cognitive prerequisite relations between educational concepts. In this paper, we present details of K12EduKG and demonstrate it with a knowledge graph constructed for the subject of mathematics.