# Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems

arXiv: https://arxiv.org/abs/2112.04368 · HTML: https://mlsanity.com/paper/2112.04368/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [RKT : Relation-Aware Self-Attention for Knowledge Tracing](https://mlsanity.com/paper/2008.12736/) (2020)
- [TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources](https://mlsanity.com/paper/1911.09471/) (2019)
- [Goal-based Course Recommendation](https://mlsanity.com/paper/1812.10078/) (2018)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [Towards an Integrative Educational Recommender for Lifelong Learners](https://mlsanity.com/paper/1912.01592/) (2019)
- [Transforming Wikipedia into an Ontology-based Information Retrieval Search Engine for Local Experts using a Third-Party Taxonomy](https://mlsanity.com/paper/1511.01259/) (2015)

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

- [Scalable Educational Question Generation with Pre-trained Language Models](https://mlsanity.com/paper/2305.07871/) (2023)
- [TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models](https://mlsanity.com/paper/2502.10411/) (2025)
- [Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments](https://mlsanity.com/paper/2207.01504/) (2022)

## Abstract (arXiv)

In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness between knowledge topics, propagating latent information across semantically related topics. We introduce a novel learner model that exploits this semantic relatedness between knowledge components in learning resources using the Wikipedia link graph, with the aim to better predict learner engagement and latent knowledge in a lifelong learning scenario. In this sense, Semantic TrueLearn builds a humanly intuitive knowledge representation while leveraging Bayesian machine learning to improve the predictive performance of the educational engagement. Our experiments with a large dataset demonstrate that this new semantic version of TrueLearn algorithm achieves statistically significant improvements in terms of predictive performance with a simple extension that adds semantic awareness to the model.