Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis

• URL: http://arxiv.org/abs/2308.07942v2
• Date: Sun, 24 Mar 2024 12:00:31 GMT
• Title: Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis
• Authors: Akash Anil, Víctor Gutiérrez-Basulto, Yazmín Ibañéz-García, Steven Schockaert,
• Abstract summary: Rule-based methods significantly outperform state-of-the-art methods based on Graph Neural Networks (GNNs) We study a number of variants of a rule-based approach, which are specifically aimed at addressing the aforementioned issues. We find that the resulting models can achieve a performance which is close to that of NBFNet.
• Score: 18.11743347414004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of inductive knowledge graph completion requires models to learn inference patterns from a training graph, which can then be used to make predictions on a disjoint test graph. Rule-based methods seem like a natural fit for this task, but in practice they significantly underperform state-of-the-art methods based on Graph Neural Networks (GNNs), such as NBFNet. We hypothesise that the underperformance of rule-based methods is due to two factors: (i) implausible entities are not ranked at all and (ii) only the most informative path is taken into account when determining the confidence in a given link prediction answer. To analyse the impact of these factors, we study a number of variants of a rule-based approach, which are specifically aimed at addressing the aforementioned issues. We find that the resulting models can achieve a performance which is close to that of NBFNet. Crucially, the considered variants only use a small fraction of the evidence that NBFNet relies on, which means that they largely keep the interpretability advantage of rule-based methods. Moreover, we show that a further variant, which does look at the full KG, consistently outperforms NBFNet.

Related papers

• A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs [10.22476128465699]
  We investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion. For the two most common benchmarks, we find that a substantial fraction of the performance difference can be explained by one unique negative pattern on each dataset.
  arXiv  Detail & Related papers  (2024-12-06T15:15:18Z)
• Learning Latent Graph Structures and their Uncertainty [63.95971478893842]
  Graph Neural Networks (GNNs) use relational information as an inductive bias to enhance the model's accuracy. As task-relevant relations might be unknown, graph structure learning approaches have been proposed to learn them while solving the downstream prediction task.
  arXiv  Detail & Related papers  (2024-05-30T10:49:22Z)
• Improving the interpretability of GNN predictions through conformal-based graph sparsification [9.550589670316523]
  Graph Neural Networks (GNNs) have achieved state-of-the-art performance in solving graph classification tasks. We propose a GNN emphtraining approach that finds the most predictive subgraph by removing edges and/or nodes. We rely on reinforcement learning to solve the resulting bi-level optimization with a reward function based on conformal predictions.
  arXiv  Detail & Related papers  (2024-04-18T17:34:47Z)
• Uncertainty in Graph Neural Networks: A Survey [50.63474656037679]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels [81.93520935479984]
  We study a new problem, GNN model evaluation, that aims to assess the performance of a specific GNN model trained on labeled and observed graphs. We propose a two-stage GNN model evaluation framework, including (1) DiscGraph set construction and (2) GNNEvaluator training and inference. Under the effective training supervision from the DiscGraph set, GNNEvaluator learns to precisely estimate node classification accuracy of the to-be-evaluated GNN model.
  arXiv  Detail & Related papers  (2023-10-23T05:51:59Z)
• When Do We Need Graph Neural Networks for Node Classification? [38.68793097833027]
  Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) In some cases, GNNs have little performance gain or even underperform graph-agnostic NNs.
  arXiv  Detail & Related papers  (2022-10-30T23:10:23Z)
• Neural Structured Prediction for Inductive Node Classification [29.908759584092167]
  This paper studies node classification in the inductive setting, aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs. We present a new approach called the Structured Proxy Network (SPN), which combines the advantages of both worlds.
  arXiv  Detail & Related papers  (2022-04-15T15:50:27Z)
• Training Free Graph Neural Networks for Graph Matching [103.45755859119035]
  TFGM is a framework to boost the performance of Graph Neural Networks (GNNs) based graph matching without training. Applying TFGM on various GNNs shows promising improvements over baselines.
  arXiv  Detail & Related papers  (2022-01-14T09:04:46Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations [25.954303305216094]
  We introduce the first axiomatic framework for theoretically analyzing, evaluating, and comparing state-of-the-art GNN explanation methods. We leverage these properties to present the first ever theoretical analysis of the effectiveness of state-of-the-art GNN explanation methods.
  arXiv  Detail & Related papers  (2021-06-16T18:38:30Z)
• Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking [63.49779304362376]
  Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. We introduce a post-hoc method for interpreting the predictions of GNNs which identifies unnecessary edges. We show that we can drop a large proportion of edges without deteriorating the performance of the model.
  arXiv  Detail & Related papers  (2020-10-01T17:51:19Z)
• Towards an Efficient and General Framework of Robust Training for Graph Neural Networks [96.93500886136532]
  Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. Despite GNNs' impressive performance, it has been observed that carefully crafted perturbations on graph structures lead them to make wrong predictions. We propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs.
  arXiv  Detail & Related papers  (2020-02-25T15:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.