Automatic Construction of Educational Knowledge Graphs: A Word Embedding-Based Approach

Abstract

Knowledge graphs (KGs) are widely used in the education domain to offer learners a semantic representation of domain concepts from educational content and their relations, termed as educational knowledge graphs (EduKGs). Previous studies on EduKGs have incorporated concept extraction and weighting modules. However, these studies face limitations in terms of accuracy and performance. To address these challenges, this work aims to improve the concept extraction and weighting mechanisms by leveraging state-of-the-art word and sentence embedding techniques. Concretely, we enhance the SIFRank keyphrase extraction method by using SqueezeBERT and we propose a concept-weighting strategy based on SBERT. Furthermore, we conduct extensive experiments on different datasets, demonstrating significant improvements over several state-of-the-art keyphrase extraction and concept-weighting techniques.

Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain

“…This is especially challenging when based on a small sample set. Overcoming this challenge is essential for constructing a high-quality domain knowledge graph [11]. This paper capitalizes on the untapped potential of distant supervision methods to diminish the reliance on annotation resources, effectively curtailing the time and cost associated with manual annotation of domain knowledge text data.…”