Title: MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Few-shot learning

few-shot relational learning

knowledge graphs
Abstract: Few-shot knowledge graph relational learning seeks to perform reasoning over relations given only a limited number of training examples.
While existing approaches largely adopt a meta-learning framework for enabling fast adaptation to new relations, they suffer from two key pitfalls. First, they learn relation meta-knowledge in isolation, failing to capture common relational patterns shared across tasks. Second, they struggle to effectively incorporate local, task-specific contexts crucial for rapid adaptation. To address these limitations, we propose MoEMeta, a novel meta-learning framework that disentangles globally shared knowledge from task-specific contexts to enable both effective model generalization and rapid adaptation. MoEMeta introduces two key innovations: (i) a mixture-of-experts (MoE) model that learns globally shared relational prototypes to enhance generalization, and (ii) a task-tailored adaptation mechanism that captures local contexts for fast task-specific adaptation. By balancing global generalization with local adaptability, MoEMeta significantly advances few-shot relational learning. Extensive experiments and analyses on three KG benchmarks show that MoEMeta consistently outperforms existing baselines, achieving state-of-the-art performance.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes MoEMeta, a meta-learning framework for few-shot relational learning (FSRL) in knowledge graphs. The key contributions include:\n1. A mixture-of-experts (MoE) module to learn shared relational prototypes across tasks, enabling global generalization.\n2. A task-tailored local adaptation mechanism to refine entity/relation embeddings via dynamic projection vectors, improving task-specific adaptation.\n\nSTRENGTHS AND WEAKNESSES:\nThe method is straightforward and well-designed, and has been proven effective through extensive experiments. However, regarding the originality and significance of the limitations mentioned in the paper, there seems to be a lack of supporting literature or experimental results.\n\nQUESTIONS:\n1. In the \"Limitations of prior work\" section, the authors only empirically identify two issues in previous methods without providing detailed referential basis. I wonder whether any existing methods have addressed these two issues or similar problems. It is expected that the discussion on limitations should incorporate more relevant references, which are essential for the paper's research motivation.  \n2. The \"Related Work\" section should not be a mere enumeration of papers, but rather reflect a summary of methodological lineages (including the problems each addresses).  \n3. At the methodological level of MoEMeta: 1) Do the \"attentive neighbor aggregation\" and the \"gated and attentive neighbor aggregator\" in GANA differ in implementation? 2) Does \"Task-Tailored Local Adaptation\" differ from TransD in implementation? The authors should clearly elaborate on the differences between each component and the referenced methods, as this is closely related to the innovativeness of the present approach.  \n4. The authors highlight MoEMeta's significant improvements on Nell-One. However, in Table 2, due to the unreleased information of RelAdapter, MoEMeta's notable enhancement is primarily demonstrated through comparisons with HiRe (a 2023 method). Such comparisons lack timeliness; thus, a reasonable comparison process between MoEMeta and the latest methods is required.\n5. In Table 2, MoEMeta shows marginal improvements in \"Hits@X\" on the Wiki-one dataset, and the authors should provide corresponding analyses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents MoEMeta, a novel meta-learning framework designed to improve few-shot relational learning in knowledge graphs (KGs). Few-shot KG relational learning is challenging due to the limited number of examples for some relations. Most existing solutions follow a meta-learning approach, but they often fail to capture common relational patterns across related tasks and overlook important task-specific information. MoEMeta addresses these issues with two key innovations: a mixture-of-experts (MoE) model that learns relational prototypes shared across tasks and a task-tailored adaptation mechanism for better local adaptation to task-specific contexts. By balancing global generalization and local adaptability, MoEMeta promises improved few-shot relational learning. The paper demonstrates the effectiveness of MoEMeta through extensive experiments and ablation studies on three KG benchmarks, where it outperforms existing state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\n## Paper Strength \n\n(1) The paper introduces a new meta-learning framework, MoEMeta, which successfully addresses two main limitations in current few-shot relational learning methods by emphasizing both global and local adaptability.\n\n(2) MoEMeta incorporates a mixture-of-experts model, which is a creative way to capture relational prototypes shared across tasks and thus enhances model generalization in few-shot scenarios.\n\n(3) The task-tailored adaptation mechanism adds a valuable dimension for capturing the unique task-specific information, offering superior local adaptation capabilities compared to existing methods.\n\n\n## Paper Weakness \n(1) Figure 1 lacks clarity – The current illustration does not clearly or sufficiently convey the proposed method or its components. It would be helpful to improve the figure's design and add more detailed annotations to enhance understanding.\n\n(2) Incremental contribution – The core idea of the paper appears to be a straightforward combination of Mixture-of-Experts (MoE) with task-specific modules. While the integration is reasonable, it does not offer significant innovation beyond existing approaches.\n\nQUESTIONS:\nsee above\n\nLIMITATIONS:\nsee above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes a MoEMeta model that introduces MoE mechanism into meta learning-based few-shot relational learning framework. Experimental results on three few-shot knowledge graph completion datasets illustrate the effectiveness of MoEMeta compared with other baseline models. However, the novelty of this paper should be improved and declared more clearly.

STRENGTHS AND WEAKNESSES:
Strengths:
1. This paper is well-written and easy to follow.
2. The authors develop a few-shot knowledge graph completion model with the widely-used MoE strategy to model various relation patterns.
3. The experimental results illustrate the effectiveness of MoEMeta.

Weaknesses:
1. This work is the extension of some existing models such as MetaR and GANA. However, simply introducing the MoE strategy into meta learning-based few-shot relational learning is not sufficient to demonstrate the novelty of the proposed approach.
2. The lack of experimental results on traditional datasets such as FB15K237 and WN18RR makes it difficult to demonstrate the generalization of the developed model.
3. Since the semantics and the amount of relations vary significantly across different datasets, setting the same number of experts uniformly is not very reasonable. The number of experts should be dynamically adjusted based on the specific characteristics of each dataset.

QUESTIONS:
1. How the proposed model performs on traditional datasets such as FB15K237 and WN18RR?
2. Why the amount of experts is set as 32 on all the three datasets?

LIMITATIONS:
The number of model parameters would increase significantly due to the utilization of MoE mechanism. It is suggested to conduct the analysis of the model's complexity.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new meta-learning method for few-shot relational learning. Specifically, the authors focus on task-specific modulation mechanism for both meta-knowledge representation and task-specific adaptation. For meta-knowledge part, each entity is represented by aggregating its neighborhood information, which is processed with the attention-like mechanism. Then, this entity representation is used for gating the MoE mechanism responsible for predicting the correct relation given the support set. For the task-specific adaptation part, the authors propose to task-specifically learn local projections for the embeddings of head/tail entities and relations. All the meta-parameters are learned through MAML-like algorithm. The experimental results demonstrate the effectivenss of the proposed method on some popular benchmark datasets, outperforming all the baselines considered in this paper.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- Paper is well written and well organized.\n- The proposed method is simple, intuitive and makes sense.\n- Each individual component is shown effective by the ablation study.\n- The proposed method achieves superior performance to the existing baselines.\n\nWeaknesses\n- While I'm very familiar with meta-learning, I admit that I'm not an expert in relational learning area. My overall impression is that the proposed method intuitively makes sense. However, it also feels like a little bit straightforward in terms of task-specific modulation. In meta-learning community, such task-specific modulation has long been studied since 2018. It's a bit surprising that no prior work has studied such task-specific modulation in the context of relational learning area. Since I'm not following any work in relational learning area, I need to consult with other reviewers and AC. \n- More specifically, it's surprising that no prior work has considered aggregating the neighbour information when processing each entity or relation. Task-specific MoE-like gating is not a new idea in meta-learning as well. The only difference seems whether it's for relational learning or not. Task-specific adaption is also not a new idea if we speak broadly, but the details seem novel. I deeply understand the importance of the task-specific modulation in meta-learning, but I also wonder why the previous literature of relational learning have not explored such a natural direction. Again, I'm not an expert in this area, so I politely ask this to both the authors and the other reviewers.\n\nQUESTIONS:\nOverall, the results seem appealing and I agree with the proposed method. Each component has carefully been demonstrated to be effective as well. The only concern is novelty, which I'm not sure. I'm willing to increase my score (up to 1) if my concern can be addressed properly.\n\nAnother suggestion is if the proposed method can be tested on more baselines and more public datasets. The most recent baselines are from two years ago, which makes me skeptical about the true SOTA-ness. Also, I'm not sure whether the two datasets, Nell-One and Wiki-One, are really sufficient for generalizing the effectiveness of the proposed method to other unseen datasets.\n\nLIMITATIONS:\nLimitations have not been addressed in this paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-06"}