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KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion (2404.03893v1)

Published 5 Apr 2024 in cs.AI

Abstract: Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph embedding (KGE) models have demonstrated superior predictive performance on KGC tasks, these models infer missing links in a black-box manner that lacks transparency and accountability, preventing researchers from developing accountable models. Existing KGE-based explanation methods focus on exploring key paths or isolated edges as explanations, which is information-less to reason target prediction. Additionally, the missing ground truth leads to these explanation methods being ineffective in quantitatively evaluating explored explanations. To overcome these limitations, we propose KGExplainer, a model-agnostic method that identifies connected subgraph explanations and distills an evaluator to assess them quantitatively. KGExplainer employs a perturbation-based greedy search algorithm to find key connected subgraphs as explanations within the local structure of target predictions. To evaluate the quality of the explored explanations, KGExplainer distills an evaluator from the target KGE model. By forwarding the explanations to the evaluator, our method can examine the fidelity of them. Extensive experiments on benchmark datasets demonstrate that KGExplainer yields promising improvement and achieves an optimal ratio of 83.3% in human evaluation.

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References (56)
  1. Realistic re-evaluation of knowledge graph completion methods: An experimental study. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 1995–2010.
  2. Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 12 (2021), 8825–8845.
  3. Complex Query Answering with Neural Link Predictors. In International Conference on Learning Representations. 1–14.
  4. Vasileios Baltatzis and Luca Costabello. 2023. KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation. arXiv preprint arXiv:2310.01065 (2023).
  5. Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers. Nature Communications 14, 1 (2023), 3570.
  6. Adversarial explanations for knowledge graph embeddings. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. 2820–2826.
  7. Rajarshi Bhowmik and Gerard de Melo. 2020. Explainable link prediction for emerging entities in knowledge graphs. In The Semantic Web–ISWC 2020: 19th International Semantic Web Conference. 39–55.
  8. Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 1247–1250.
  9. Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems 26 (2013), 1–9.
  10. Translating Embeddings for Modeling Multi-Relational Data. In Proceedings of the 26th International Conference on Neural Information Processing Systems. 2787–2795.
  11. Learning to explain: An information-theoretic perspective on model interpretation. In International conference on machine learning. 883–892.
  12. Xiang Deng and Zhongfei Zhang. 2020. Can students outperform teachers in knowledge distillation based model compression? (2020).
  13. AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In Proceedings of the 22nd international conference on World Wide Web. 413–422.
  14. Knowledge distillation: A survey. International Journal of Computer Vision 129 (2021), 1789–1819.
  15. Link prediction with attention applied on multiple knowledge graph embedding models. In Proceedings of the ACM Web Conference 2023. 2600–2610.
  16. Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In International Conference on Learning Representations.
  17. Rule learning from knowledge graphs guided by embedding models. In The Semantic Web–ISWC 2018: 17th International Semantic Web Conference. 72–90.
  18. Trustworthy knowledge graph completion based on multi-sourced noisy data. In Proceedings of the ACM Web Conference 2022. 956–965.
  19. RCENR: A Reinforced and Contrastive Heterogeneous Network Reasoning Model for Explainable News Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1710–1720.
  20. Tinybert: Distilling bert for natural language understanding. International Conference on Machine Learning and Natural Language Processing (2019).
  21. Unmesh Joshi and Jacopo Urbani. 2020. Searching for embeddings in a haystack: link prediction on knowledge graphs with subgraph pruning. In Proceedings of The Web Conference 2020. 2817–2823.
  22. Knowledge Base Completion: Baselines Strike Back. In Proceedings of the 2nd Workshop on Representation Learning for NLP. 69–74.
  23. Fact checking in knowledge graphs with ontological subgraph patterns. Data Science and Engineering 3, 4 (2018), 341–358.
  24. Investigating Advertisers’ Domain-changing Behaviors and Their Impacts on Ad-blocker Filter Lists. In Proceedings of the ACM Web Conference 2022. 576–587.
  25. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29. 1–7.
  26. KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction. IEEE Transactions on Knowledge and Data Engineering 35, 7 (2023), 7068–7081.
  27. George A. Miller. 1995. WordNet: A Lexical Database for English. Commun. ACM 38, 11 (nov 1995), 39–41.
  28. Link Prediction on Latent Heterogeneous Graphs. In Proceedings of the ACM Web Conference 2023. 263–273.
  29. A three-way model for collective learning on multi-relational data.. In International Conference on Machine Learning, Vol. 11. 3104482–3104584.
  30. Deep learning for drug repurposing: Methods, databases, and applications. Wiley Interdisciplinary Reviews: Computational Molecular Science 12, 4 (2022), e1597.
  31. Automatic differentiation in pytorch. (2017).
  32. Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications. In North American Chapter of the Association for Computational Linguistics. 1–12.
  33. Rnnlogic: Learning logic rules for reasoning on knowledge graphs. International Conference on Learning Representations (2021), 1–20.
  34. Explaining link prediction systems based on knowledge graph embeddings. In Proceedings of the 2022 International Conference on Management of Data. 2062–2075.
  35. Drum: End-to-end differentiable rule mining on knowledge graphs. Advances in Neural Information Processing Systems 32 (2019), 1–13.
  36. xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction. In Proceedings of the ACM Web Conference 2023. 349–359.
  37. Rotate: Knowledge graph embedding by relational rotation in complex space. In ICLR. 1–18.
  38. Inductive relation prediction by subgraph reasoning. In International Conference on Learning Representations. 9448–9457.
  39. Complex embeddings for simple link prediction. In International Conference on Machine Learning. 2071–2080.
  40. HyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural Networks. In Proceedings of the ACM Web Conference 2023. 188–198.
  41. Knowledge graph convolutional networks for recommender systems. In The World Wide Web Conference. 3307–3313.
  42. Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315 (2019).
  43. Graph-based extractive explainer for recommendations. In Proceedings of the ACM Web Conference 2022. 2163–2171.
  44. Towards multi-grained explainability for graph neural networks. Advances in Neural Information Processing Systems 34 (2021), 18446–18458.
  45. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 28. 1112–1119.
  46. Explaining Graph Neural Networks via Non-parametric Subgraph Matching. International Conference on Machine Learning (2023), 1–13.
  47. Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 285–294.
  48. Embedding entities and relations for learning and inference in knowledge bases. International Conference on Learning Representations (2015), 1–12.
  49. Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1434–1443.
  50. Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning. In Proceedings of the ACM Web Conference 2023. 3680–3688.
  51. PaGE-Link: Path-based graph neural network explanation for heterogeneous link prediction. In Proceedings of the ACM Web Conference 2023. 3784–3793.
  52. Explaining Knowledge Graph Embedding via Latent Rule Learning. OpenReview.Net 26 (2021), 1–14.
  53. Iteratively learning embeddings and rules for knowledge graph reasoning. In The World Wide Web Conference. 2366–2377.
  54. Rethinking graph convolutional networks in knowledge graph completion. In Proceedings of the ACM Web Conference 2022. 798–807.
  55. KE-X: Towards subgraph explanations of knowledge graph embedding based on knowledge information gain. Knowledge-Based Systems 278 (2023), 110772.
  56. Dgl-ke: Training knowledge graph embeddings at scale. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 739–748.
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