An Optimized Pipeline for Automatic Educational Knowledge Graph Construction

2025-09-05

AI Summary

Existing approaches to automatically constructing educational knowledge graphs (EduKGs) from PDF learning materials suffer from poor scalability, low accuracy, and unreliable knowledge representation. To address these challenges, this paper proposes an end-to-end automated framework: first, it precisely extracts slide-level knowledge units from PDFs to build a fine-grained knowledge graph; second, it generates a course-level EduKG via semantic alignment and hierarchical fusion. The framework holistically integrates information extraction, document-structure-aware parsing, context-enhanced joint entity-relation extraction, and multi-source knowledge fusion—each component incorporating domain-specific optimizations. Evaluated on the CourseMapper platform, the method improves graph accuracy by 17.5% and processing efficiency by 10×, significantly enhancing the reliability, reusability, and contextual adaptability of educational knowledge representation.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Engineering
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

The automatic construction of Educational Knowledge Graphs (EduKGs) is essential for domain knowledge modeling by extracting meaningful representations from learning materials. Despite growing interest, identifying a scalable and reliable approach for automatic EduKG generation remains a challenge. In an attempt to develop a unified and robust pipeline for automatic EduKG construction, in this study we propose a pipeline for automatic EduKG construction from PDF learning materials. The process begins with generating slide-level EduKGs from individual pages/slides, which are then merged to form a comprehensive EduKG representing the entire learning material. We evaluate the accuracy of the EduKG generated from the proposed pipeline in our MOOC platform, CourseMapper. The observed accuracy, while indicative of partial success, is relatively low particularly in the educational context, where the reliability of knowledge representations is critical for supporting meaningful learning. To address this, we introduce targeted optimizations across multiple pipeline components. The optimized pipeline achieves a 17.5% improvement in accuracy and a tenfold increase in processing efficiency. Our approach offers a holistic, scalable and end-to-end pipeline for automatic EduKG construction, adaptable to diverse educational contexts, and supports improved semantic representation of learning content.

Problem

• Automatic Educational Knowledge Graph construction from PDF materials
• Scalable and reliable approach for EduKG generation challenge
• Low accuracy in educational context knowledge representation reliability

Innovation

• Automatic pipeline for Educational Knowledge Graph construction
• Optimized components improve accuracy and processing efficiency
• End-to-end scalable solution for diverse educational contexts