# EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph

arXiv: https://arxiv.org/abs/2210.12228 · HTML: https://mlsanity.com/paper/2210.12228/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Semantic Segmentation of Legal Documents via Rhetorical Roles](https://mlsanity.com/paper/2112.01836/) (2021)
- [CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese](https://mlsanity.com/paper/2001.04351/) (2020)
- [Course Concept Expansion in MOOCs with External Knowledge and Interactive Game](https://mlsanity.com/paper/1909.07739/) (2019)
- [CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata](https://mlsanity.com/paper/2101.09969/) (2021)
- [EduCOR: An Educational and Career-Oriented Recommendation Ontology](https://mlsanity.com/paper/2107.05522/) (2021)

## Built on by (most cited first, 3 of 3)

- [GoAI: Enhancing AI Students' Learning Paths and Idea Generation via Graph of AI Ideas](https://mlsanity.com/paper/2503.08549/) (2025)
- [An Optimized Pipeline for Automatic Educational Knowledge Graph Construction](https://mlsanity.com/paper/2509.05392/) (2025)
- [From SQL Errors to Concept Gaps: An AI-Powered Knowledge Graph Analytics Platform for Personalized Feedback](https://mlsanity.com/paper/2608.03118/) (2026)

## Abstract (arXiv)

Web and artificial intelligence technologies, especially semantic web and knowledge graph (KG), have recently raised significant attention in educational scenarios. Nevertheless, subject-specific KGs for K-12 education still lack sufficiency and sustainability from knowledge and data perspectives. To tackle these issues, we propose EDUKG, a heterogeneous sustainable K-12 Educational Knowledge Graph. We first design an interdisciplinary and fine-grained ontology for uniformly modeling knowledge and resource in K-12 education, where we define 635 classes, 445 object properties, and 1314 datatype properties in total. Guided by this ontology, we propose a flexible methodology for interactively extracting factual knowledge from textbooks. Furthermore, we establish a general mechanism based on our proposed generalized entity linking system for EDUKG's sustainable maintenance, which can dynamically index numerous heterogeneous resources and data with knowledge topics in EDUKG. We further evaluate EDUKG to illustrate its sufficiency, richness, and variability. We publish EDUKG with more than 252 million entities and 3.86 billion triplets. Our code and data repository is now available at https://github.com/THU-KEG/EDUKG.