mlsanityData to 7 Oct 2026

Comprehensible Artificial Intelligence on Knowledge Graphs: A survey

arXiv 2404.03499 ↗First posted 4 Apr 2024cs.AIcited by 8

Not on the map. Why?

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 54 of its 132 references that are in the map. First posted on arXiv 4 Apr 2024.

Builds on

54 of 132 references.

References · 54

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  4. 2016Complex Embeddings for Simple Link Predictioncited by 897
  5. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  6. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  7. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  8. 2020Knowledge Graphscited by 619
  9. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  10. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  11. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
  12. 2019Interpretable machine learning: definitions, methods, and applicationscited by 303
38 more
  1. 2017DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoningcited by 265
  2. 2020Neurosymbolic AI: The 3rd Wavecited by 253
  3. 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
  4. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  5. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 210
  6. 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
  7. 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 173
  8. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
  9. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 165
  10. 2017Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learningcited by 161
  11. 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 152
  12. 2018Multi-Hop Knowledge Graph Reasoning with Reward Shapingcited by 102
  13. 2019Reinforcement Knowledge Graph Reasoning for Explainable Recommendationcited by 102
  14. 2021Do Feature Attribution Methods Correctly Attribute Features?cited by 98
  15. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  16. 2020Knowledge Graph Embedding for Link Prediction: A Comparative Analysiscited by 85
  17. 2017What Does Explainable AI Really Mean? A New Conceptualization of Perspectivescited by 72
  18. 2020Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Frameworkcited by 70
  19. 2019Probabilistic Logic Neural Networks for Reasoningcited by 69
  20. 2021GraphSVX: Shapley Value Explanations for Graph Neural Networkscited by 63
  21. 2020RelEx: A Model-Agnostic Relational Model Explainercited by 53
  22. 2019Jointly Learning Explainable Rules for Recommendation with Knowledge Graphcited by 40
  23. 2019Interaction Embeddings for Prediction and Explanation in Knowledge Graphscited by 40
  24. 2018Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendationcited by 39
  25. 2021Counterfactual Graphs for Explainable Classification of Brain Networkscited by 33
  26. 2020Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?cited by 31
  27. 2016STransE: a novel embedding model of entities and relationships in knowledge basescited by 17
  28. 2017An Interpretable Knowledge Transfer Model for Knowledge Base Completioncited by 17
  29. 2020Knowledge Graph Embeddings and Explainable AIcited by 15
  30. 2021SAFRAN: An interpretable, rule-based link prediction method outperforming embedding modelscited by 15
  31. 2021Faithfully Explainable Recommendation via Neural Logic Reasoningcited by 14
  32. 2018What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systemscited by 11
  33. 2019Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendationcited by 10
  34. 2019How to make latent factors interpretable by feeding Factorization machines with knowledge graphscited by 7
  35. 2016Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Datacited by 6
  36. 2015RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cyclescited by 5
  37. 2018Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approachcited by 5
  38. 2022Rule Mining over Knowledge Graphs via Reinforcement Learningcited by 5

50 most cited of 54 All 54 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 1.

8 papers.

  1. 2025Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modellingcited by 9
  2. 2025ID-RAG: Identity Retrieval-Augmented Generation for Long-Horizon Persona Coherence in Generative Agentscited by 7
  3. 2024Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysiscited by 5
  4. 2024From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrismacited by 0
  5. 2025Context-Awareness and Interpretability of Rare Occurrences for Discovery and Formalization of Critical Failure Modescited by 0
  6. 2025FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelinescited by 0
  7. 2025GRainsaCK: a Comprehensive Software Library for Benchmarking Explanations of Link Prediction Tasks on Knowledge Graphscited by 0
  8. 2026CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphscited by 0