# Comprehensible Artificial Intelligence on Knowledge Graphs: A survey arXiv: https://arxiv.org/abs/2404.03499 ยท HTML: https://mlsanity.com/paper/2404.03499/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 54 of its 132 references that are in the map. - First posted on arXiv 4 Apr 2024. ## Builds on (most cited first, 30 of 54) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016) - [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017) - [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016) - [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019) - [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019) - [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017) - [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020) - [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020) - [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020) - [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019) - [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019) - [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017) - [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020) - [XGNN: Towards Model-Level Explanations of Graph Neural Networks](https://mlsanity.com/paper/2006.02587/) (2020) - [PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2010.05788/) (2020) - [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018) - [GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2001.06216/) (2020) - [DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs](https://mlsanity.com/paper/1911.00055/) (2019) - [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020) - [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019) - [Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning](https://mlsanity.com/paper/1711.05851/) (2017) - [Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking](https://mlsanity.com/paper/2010.00577/) (2020) - [Multi-Hop Knowledge Graph Reasoning with Reward Shaping](https://mlsanity.com/paper/1808.10568/) (2018) - [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://mlsanity.com/paper/1906.05237/) (2019) - [Do Feature Attribution Methods Correctly Attribute Features?](https://mlsanity.com/paper/2104.14403/) (2021) - [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021) - [Knowledge Graph Embedding for Link Prediction: A Comparative Analysis](https://mlsanity.com/paper/2002.00819/) (2020) - [What Does Explainable AI Really Mean? A New Conceptualization of Perspectives](https://mlsanity.com/paper/1710.00794/) (2017) - [Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework](https://mlsanity.com/paper/2006.13365/) (2020) ## Built on by (most cited first, 8 of 8) - [Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling](https://mlsanity.com/paper/2509.17289/) (2025) - [ID-RAG: Identity Retrieval-Augmented Generation for Long-Horizon Persona Coherence in Generative Agents](https://mlsanity.com/paper/2509.25299/) (2025) - [Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis](https://mlsanity.com/paper/2406.18114/) (2024) - [From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrisma](https://mlsanity.com/paper/2406.01759/) (2024) - [Context-Awareness and Interpretability of Rare Occurrences for Discovery and Formalization of Critical Failure Modes](https://mlsanity.com/paper/2504.16117/) (2025) - [FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines](https://mlsanity.com/paper/2506.09107/) (2025) - [GRainsaCK: a Comprehensive Software Library for Benchmarking Explanations of Link Prediction Tasks on Knowledge Graphs](https://mlsanity.com/paper/2508.08815/) (2025) - [CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2607.19996/) (2026) ## Abstract (arXiv) Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since the beginning of the 21st century. However, in many applications, users require an explanation of the Artificial Intelligences decision. This led to increased demand for Comprehensible Artificial Intelligence. Knowledge Graphs epitomize fertile soil for Comprehensible Artificial Intelligence, due to their ability to display connected data, i.e. knowledge, in a human- as well as machine-readable way. This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs. Furthermore, we contribute by arguing that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning. By introducing the parent concept Comprehensible Artificial Intelligence, we provide a clear-cut distinction of both concepts while accounting for their similarities. Thus, we provide in this survey a case for Comprehensible Artificial Intelligence on Knowledge Graphs consisting of Interpretable Machine Learning on Knowledge Graphs and Explainable Artificial Intelligence on Knowledge Graphs. This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs. In addition, a comprehensive overview of the research on Comprehensible Artificial Intelligence on Knowledge Graphs is presented and put into the context of the taxonomy. Finally, research gaps in the field of Comprehensible Artificial Intelligence on Knowledge Graphs are identified for future research.