The Problem of Shortcuts in Inductive Knowledge Graph Completion Datasets and a Proposed Solution Using Graph Partitioning

Основні поняття

Current inductive knowledge graph completion datasets suffer from a shortcut where Personalized PageRank (PPR) can achieve high performance by exploiting differences in shortest path distances between entities, hindering the accurate evaluation of inductive reasoning capabilities of KGC models. This paper proposes a new dataset construction method using graph partitioning to mitigate this shortcut and provide more reliable benchmarks for inductive KGC.

Анотація

• Bibliographic Information: Shomer, H., Revolinsky, J., & Tang, J. (2024). Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion. arXiv preprint arXiv:2406.11898v2.

• Research Objective: This paper investigates the effectiveness of Personalized PageRank (PPR) on inductive knowledge graph completion (KGC) datasets and identifies a shortcut arising from the current dataset construction methods. The authors propose a new method based on graph partitioning to create more challenging and reliable inductive KGC datasets.

• Methodology: The authors analyze the performance of PPR on various existing inductive KGC datasets and compare it to state-of-the-art supervised methods. They investigate the relationship between PPR performance and the shortest path distance (SPD) between entities in positive and negative samples. To address the identified shortcut, they propose a new dataset construction method based on graph partitioning, aiming to maintain similar graph properties between the training and inference graphs. They then evaluate the performance of several KGC models on these newly constructed datasets.

• Key Findings: The study reveals that PPR achieves surprisingly high performance on existing inductive KGC datasets, often approaching or exceeding the performance of supervised methods. This high performance is attributed to a shortcut arising from the dataset construction process, where the SPD between entities in positive samples tends to be significantly lower than that in negative samples. The proposed graph partitioning method for dataset construction successfully mitigates this shortcut, leading to a significant decrease in PPR performance and a more realistic evaluation of inductive KGC models.

• Main Conclusions: The authors conclude that the current inductive KGC datasets are not reliable benchmarks for evaluating the inductive reasoning capabilities of KGC models due to the identified PPR shortcut. The proposed graph partitioning method offers a promising solution for constructing more challenging and reliable inductive KGC datasets, paving the way for more accurate evaluation and advancement of inductive KGC models.

• Significance: This research highlights a critical issue in the evaluation of inductive KGC models and proposes a practical solution to address it. The findings have significant implications for the development and benchmarking of future inductive KGC models, ensuring a more accurate assessment of their true capabilities.

• Limitations and Future Research: The study primarily focuses on a limited number of existing KGC datasets and models. Further research is needed to explore the effectiveness of the proposed graph partitioning method on a wider range of datasets and models, including those incorporating textual information or utilizing more complex reasoning mechanisms. Additionally, investigating alternative dataset construction methods beyond graph partitioning could further enhance the reliability and challenge of inductive KGC benchmarks.

Статистика

On average, PPR performs only 25-29% worse than SOTA on inductive datasets.
PPR performance on FB15k-237 sees a 1481% increase from its transductive form to its inductive derivatives.
Pearson correlation between ∆SPD and Hits@10 when using PPR is 0.87.
The average PPR performance is 78% lower on the new inductive datasets compared to the older datasets.

Цитати

"We find that on almost all inductive datasets, we can achieve competitive performance by using the Personalized PageRank [12] (PPR) score to perform inference."
"These findings are problematic as PPR has no basis in literature as a heuristic for KGC, since it completely overlooks the relational aspect of KGs."
"This suggests the potential existence of a shortcut that allows a simple non-learnable method like PPR to achieve high performance on almost all inductive datasets."
"Therefore, this suggests the potential existence of a shortcut that allows a simple non-learnable method like PPR to achieve high performance on almost all inductive datasets."

Ключові висновки, отримані з

Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion

by Harry Shomer... о arxiv.org 10-08-2024

https://arxiv.org/pdf/2406.11898.pdf

Глибші Запити

How can we develop evaluation metrics that are robust to such shortcuts and provide a more accurate assessment of the inductive reasoning capabilities of KGC models?

Developing evaluation metrics robust to shortcuts like the one highlighted with Personalized PageRank (PPR) in inductive Knowledge Graph Completion (KGC) requires a multi-pronged approach:

Beyond Distance-Based Metrics: Current metrics like Hits@k primarily rely on ranking and are susceptible to biases in the shortest path distance (SPD) distribution. We need to explore metrics that go beyond SPD and capture the model's ability to reason about:

Relational Paths: Metrics that evaluate the model's ability to identify and leverage multi-hop relational paths for inference. This could involve assessing the relevance of the paths used by the model to arrive at a prediction.
Logical Consistency: Metrics that measure the consistency of the model's predictions with the underlying logical rules and constraints of the knowledge graph.
Explanation-Based Evaluation: Incorporating mechanisms for models to provide explanations for their predictions. These explanations can then be evaluated for their plausibility and reliance on relational reasoning, rather than just path length.

Adversarial Dataset Creation:

Controlled SPD Distribution: Design datasets where the SPD distribution between positive and negative samples is carefully controlled to prevent models from exploiting this shortcut. This might involve generating negative samples that are farther away in terms of SPD but still semantically plausible.
Structure-Preserving Negative Sampling: Develop negative sampling techniques that preserve the local and global structural properties of the knowledge graph, ensuring that negative samples are not trivially distinguishable from positive ones based on graph structure alone.

Evaluating Generalization Across Graph Properties:

Diverse Dataset Characteristics: Benchmark models on datasets with varying characteristics, such as different graph densities, relation distributions, and semantic domains. This would test the model's ability to generalize its inductive reasoning capabilities to diverse knowledge graph structures.
Open-World Knowledge Graph Completion: Move towards evaluating models in more realistic open-world settings where the knowledge graph is incomplete and constantly evolving. This would require models to handle unseen entities and relations effectively.

By incorporating these strategies, we can develop more robust evaluation metrics that provide a more accurate assessment of the true inductive reasoning capabilities of KGC models, moving beyond superficial shortcuts and towards genuine knowledge-driven inference.

Could the insights about the limitations of current inductive KGC datasets be applied to other graph-based machine learning tasks beyond KGC?

Yes, the insights about the limitations of current inductive KGC datasets, particularly the reliance on shortcuts like SPD biases, are highly relevant to other graph-based machine learning tasks. Here's how:

Node Classification: In tasks like social network analysis or protein function prediction, models might implicitly learn to classify nodes based on their structural position in the graph (e.g., degree centrality) rather than their actual features. This can lead to overfitting and poor generalization to unseen graphs.

Graph Classification: Models trained to classify graphs based on their overall structure might exploit spurious correlations in the training data, such as the presence of certain subgraphs, without learning the underlying graph properties that determine the class label.

Link Prediction in Other Domains: Similar to KGC, link prediction tasks in areas like recommender systems or social network analysis can also be affected by SPD biases. Models might recommend items or connect users based on their proximity in the user-item interaction graph, rather than their actual preferences or compatibility.
Addressing these limitations in other graph-based tasks requires:

Awareness of Potential Shortcuts: Researchers and practitioners need to be cognizant of the potential for models to exploit structural biases in graph data.

Careful Dataset Design and Evaluation: Similar to KGC, datasets for other graph-based tasks should be carefully designed to minimize the influence of spurious correlations and structural biases. Evaluation metrics should also be robust to these shortcuts.

Model Interpretability and Explainability: Developing techniques to understand and interpret the decision-making process of graph-based models can help identify and mitigate the reliance on shortcuts.
By applying the lessons learned from inductive KGC, we can improve the robustness and reliability of graph-based machine learning models across various domains.

What are the potential implications of developing highly effective inductive KGC models on other domains that rely heavily on knowledge graphs, such as drug discovery or personalized medicine?

Developing highly effective inductive KGC models holds immense potential to revolutionize domains heavily reliant on knowledge graphs, such as drug discovery and personalized medicine. Here's how:
Drug Discovery:

Drug Repurposing: Inductive KGC can identify novel relationships between existing drugs and diseases, even if these relationships are not explicitly present in the training data. This can accelerate drug repurposing efforts by suggesting new therapeutic uses for existing medications.
Target Identification: By reasoning over the complex interactions between genes, proteins, and diseases, inductive KGC can pinpoint promising drug targets for specific diseases, even if the target's role in the disease pathway is not fully understood.
Combination Therapy Design: Inductive KGC can predict synergistic effects between different drugs, enabling the development of more effective combination therapies for complex diseases.
Personalized Medicine:

Patient Stratification: By integrating patient data with knowledge graphs of diseases, genes, and treatments, inductive KGC can identify subgroups of patients who are most likely to respond to specific therapies based on their unique molecular profiles.
Treatment Recommendation: Inductive KGC can assist clinicians in making more informed treatment decisions by predicting the likelihood of treatment success and potential side effects for individual patients.
Precision Medicine Development: By enabling the discovery of new drug targets and the development of more targeted therapies, inductive KGC can contribute to the advancement of precision medicine, where treatments are tailored to the individual needs of each patient.
Beyond Drug Discovery and Personalized Medicine:
The impact of effective inductive KGC extends to other domains like:

Material Science: Predicting novel material properties and designing new materials with desired characteristics.
Social Good: Understanding social dynamics, predicting the spread of misinformation, and developing targeted interventions for social problems.
However, realizing this potential requires addressing challenges like:

Data Quality and Integration: Ensuring the accuracy, completeness, and consistency of knowledge graphs used in these domains.
Model Interpretability and Trust: Building trust in the predictions of inductive KGC models by providing explanations for their reasoning process.
Ethical Considerations: Addressing potential biases in the data and models to ensure fairness and equity in healthcare and other applications.
By overcoming these challenges, we can unlock the transformative power of inductive KGC to accelerate scientific discovery, improve healthcare outcomes, and address pressing societal issues.