MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
Authors: Han Wu
Jie Yin
Venue: NeurIPS 2025 poster ()
Keywords: Few-shot learning
few-shot relational learning
knowledge graphs
Primary area: deep_learning

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.

MoEMeta: Mixture-of-Bxperts Meta Learning for Few-Shot Relational Learning
Han Wu Jie Yin
'The University of Sydney, Australia ?Peking University, China
[han. wu, jie, yindsydey, edu. au}
Abstract
Few-shot knowledge graph relational learning seeks to perform reasoning over relations given only a limited number of training examples. While existing ap- proaches largely adopt a meta-learning framework for enabling fast adaptation to 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 adap- tation. 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 exper- iments and analyses on three KG benchmarks show that MoEMeta consistently outperforms existing baselines, achieving state-of-the-al performance.
Introduction
Knowledge graphs (KGs) encode real-world knowledge in the form of factual triplets (h, r, where each triplet represents a relationship between head entity h and a tail entity (Bollacker et al., 2008; Suchanek al., 2007: Vrandečié and Krötzsch, 2014).KGr are crucial for powering various intelligent applications, such as question answering (Wu et al., 2016). Web search (Fang et al., 2017), and advanced recommendation systems (Cao al., 2019). Despite their widespread adoption, real-world KGs are inherently incomplete. Although numerous KG completion methods (Bordes et al., 2013; Yang et al., 2015; Trouillon et al., 2016) have been proposed to infer missing facts based on observed triplets, their effectiveness is severely hindered by data sparsity, especially for long-tail or emerging relations with only handful of known training triplets.
In few shot relational learning (FSRL) has emerged as promising direction for reasoning over few-shot relations with limited training triplets, enabling generalization novel relations in data-scarce scenarios. Most existing FSRL methods (Chen et al., 2019; Jiang et al., 2021; Niu et al., 2021; Wu et al., 2023) are built upon model-agnostic meta-learning (MAMI.) (Finn et al., 2017), gradient-based meta-learning framework that learns set of global parameters as initialization for rapid task adaptation using only one or a few steps gradient descent updates. FSRL, each task relation-centrie comprising only few training triplets of single relation, and the objective is to
"ooreespnning author.
'The code is available at: https: //t:://gtttttt com/
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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predict instances of novel relations not seen during meta- eett-trainiig. These methods focus on learning and encoding metu-knowledge among tasks to support rapid adaptation with minimal supervision.
Limitations of prior work. Despite promising results, current meta-learning based FSRL methods face two key limitations. First, they learn about meta-knowledge within individual tasks while neglecting shared patterns that exist across tasks. This limitation arises from the assumption that meta-training tasks are independent and identically distributed (i.i.d.), where a base model is adapted to each meta-training task using its support set, and then updated based on the performance on its query set. However, KG relations are heterogeneous and often semantically clustered, with some relations more closely related than others. For example, on Nell-One (Mitchell et al., 2018), FatherOfPer and BrotherOf share a strong thematic overlap (family ties). whereas ColorOf pertains to a different type of attribute (physical properties). Ignoring such underlying commonalities among related tasks hinders model generalization in few-shot scenarios. While accounting for task variance has been explored in other few-shot domains like image classification (Li et al., 2019; Zhou et al., 2021), remains largely unaddressed in the unique, non-i.i.d. context of relational learning.
Second, current methods are limited by their reliance on a single set of global parameters to encode meta-knowledge. With a shared initialization, a uniform adaptation process lacks the flexibility to capture the unique characteristics of individual tasks from only a few support triplets. This limitation critical because relations and entities in KGs exhibit diverse interaction patterns (Wang et al., 2014; Ji et al., 2015), such as one-to-one one-to- one-to-many, many-to-one, and many mann-y. An entity often reveals entirely different aspects depending on relational context. For example, the entity Elon Musk participates in the CeoOf relation (a professional role) and the FatherOf family role). Methods constrained by a shared initialization struggle to adapt to such locally distant contexts, and failing to modulate meta-knowledge for a specific task results in suboptimal adaptation.
relation (a
Present work and contributions. To overcome these limitations, we propose MoEMeta. a novel meta-learning framework to tackle a critical, non-trivial challenge not addressed by prior FSRL work: how to disentangle globally shared knowledge from task-specific contexts to achieve effective generalization and rapid adaptation under fed- few-shot settings. MoEMeta provides a principled solution through two novel components that work in synergy.
Global knowledge generalization: To capture what to generalize, we introduce a mixture- of-experts (MoE) model to dynamically learn common relational patterns shared across tasks, referred to relational prototypes, from a global pool of experts. The MoE is globally optimized, enabling the dynamic selection of experts that specialize in modeling the compositional nature of relations in KGs an explicit and novel design for enhancing generalization.
Local context adaptation: To address what to adapt, we propose novel task-tailored adaptation mechanism that leverages task-specific structural contexts to fine-tune relation-meta and entity embeddings for local adaptation. Unlike general task-aware modulation methods (Vuorio et al., 2019; Abdollahzadeh et al., 2021), which rely on task mode prediction to modulate the meta-learned prior parameters, our adaptation operates at the embedding level, offering lightweight yet effective way to capture diverse entity-relation interactions unique individual tasks.
Together, these two innovations strike effective balance between global knowledge generaliza- tion and fine-grained task-specific adaptation, which is crucial for FSRL with limited supervision. Experimental results demonstrate that MoEMeta achieves state-of-the-an performance on three KG benchmarks, significantly outperforming existing methods. In-depth ablation studies and visualiza- tions further validate the efficacy of each component in MoEMeta.
2 Related Work
Few-Shot Relation Learning. Existing approaches to FSRL can be categorized into metric-learning methods and meta-learning methods. Metric- methods learn a similarity metric between the support and query sets for adaptation. Approaches in this category include GMatching (Xiong et al., 2018), designed for one-shot relation learning; FSKGC (Zhang et al., 2020), which extends to few-shot settings using a recurrent autoencoder to aggregate support instances: FAAN (Sheng et al., 2020), which employs dynamic attention mechanism for improved one-hop neighbor aggregation; CSR (Huang et al., 2022), which utilizes subgraph matching for adaptation; and NP-FKGG FKG (Luo et al., 2023), which leverages neural processes to address distribution shifts in unseen relations.

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Most mainstream FSRL methods are based on meta-learning (Finn et al.. 2017), which learns an initial meta-prior from existing tasks for rapid adaptation to new few-shot relations. MetaR (Chen et al., 2019) learns relation-specific meta-information by simply averaging support triplet representations. MetaP (Jiang et al., 2021) utilizes meta- reta-patterm learning under one-shot settings. GANA (Niu et al., 2021) integrates meta-learning with TransH (Wang et al., 2014) and devises gated, attentive neighbor aggregator to handle sparse neighborhood information. HiRe (Wu et al., 2023) exploits multi- granularity relational information to learn more generalizable mnea--nowleege. RelAdapter (Ran et al., 2024) adopts a shallow feedforward network with a residual connection to adapt relation meta- knowledge within each target task. Despite their progress, these methods face two key limitations: (1) they often neglect common relational patterns when learning meta- meta-knowledde during meta- training, and (2) they fail to effectively incorporate local, tusk-specific contexts essential for rapid adaptation. Our work addresses these critical gaps by proposing a principled meta-learning framework that disentangles globally shared knowledge from task-specific adaptation, enabling both effective generalization and fast adaptation in FSRL. For a more extensive review of related work, including KG embedding learning and general few-shot learning techniques, please refer to Appendix ??.
Mixture of Experts. The mixture-of-expers (MoE) architecture, originally introduced by Jacobs et al. (1991), is modular machine learning framework designed to improve performance by dy- namically combining multiple specialized models, known as "experts". Sparse MoEs and their "soft" variants (Jordan and Jacobs, 1994; Puigcerver et al., 2024) have been widely adopted in deep learning, especially in large-scale models for natural language processing and computer vision, to improve model training efficiency and scalability through adaptive subnetwork selection or dynamic pruning (Shazeer et al., 2017; Du et al., 2022). In a related KG context, MoE has been applied to fuse different semanties of relations (structural ontology and textual descriptions) at the triplet level (Song et al., 2022). In contrast, our work innovatively proposes MoE as a meta-learner for FSRL that operates the task level to learn specialized experts capturing diverse relational patterns, thereby enabling superior cross-task knowledge transfer and task adaptation.
3 Preliminaries
3.1 FSRL Problem Formulation
A knowledge graph (KG) can be represented as collection of factual triplets g = {(h,r, r, t) E x R x E), where & denotes the set of entities, and R denotes the set of relations. Each triplet (h,r,t) indicates relationship r between the head entity h and the tail entity t.
Given a novel relation r ş and its corresponding support set S, = {(h,, r, (,)) where ha,te 4 E E, the objective of few-shot relational learning (FSRL) is to predict the missing tail entities in query set 2, = {(h; r, ?)}, given a head entity h2 E and a relation r. For each query triplet (hy,r, r, ?) Q,,, a candidate set Ch is provided, and the goal is to rank the true tail entity highest among all candidates. The support set S, and the query set Qr jointly define task T, (S,, 2r) for a relation r. In K-shot relation learning, the support set S, contains only a few training triplets (typically K- 1,3, 5).
3.2 Meta- Meranngg based FSRL
Given the strength of meta-learning in enabling rapid adaptation with minimal data, we build our approach upon a meta-learning based framework, involving two stages: meta-training and meta- testing. The model is expected to learn a meta-prior from seen tasks in the meta-training stage, which can then be adapted to novel tasks during the meta-testing stage.
Meta-Traie Training Stage. During meta- meta-training, an FSRL model is trained on set of relations R2 with the corresponding task data D, = [T, r E R.y. The model aims to learn a prior, which initializes relation-men meta learner for a new task. For each task T, (S,, 2,) E D... the learner extracts task-specific relation-meta Ry, E d based on the support set S., which is rapidly updated using gradient meta GT, computed from the loss on the support set:
(1) where is the learning rate. The updated relation-meta R,, is then applied to the query set with the ground-truth tail entities, where the query loss backpropagated to update both the prior and the entity embedding matrix.
Ry, + Ry, - aGT,,
3

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Meta- Testang Stage. During meta-testing the model is evaluated on novel relations Rie and their associated task data Die (T, E R. where Rr n Re 0. For each novel task T, (S,; 2r) Die, the model uses the learned prior to initialize the relation-mett learner and compute an initial relation-meta R... which then adapted using its support set S, to obtain R Finally, for each query triplet (h, r. ?). the model ranks all candidate tail entities in the candidate set Ca.r by computing score s(h, R,,,,) .) for each candidate & Ch,r and sorting them in ascending order.
4 The Proposed MoEMeta Framework
This section presents our proposed MoEMeta framework. Its design is underpinned by two key insights: First, relations in woal-world KGs exhibit heterogeneous characteristics, with some subsets of relations sharing greater similarity and stronger interconnections; Second, relations in KGs manifest diverse interaction patterns, such one-to-many, many-to-one, and many-to-man no-many. Building upon these insights, we introduce MoEMeta, novel meta-learning framework that (1) utilizes an MoE model to dynamically learn relational prototypes and generate relation- meta for each task; and (2) incorporates a task-tailored adaptation mechanism to facilitate local adaptation to each task.
MoEMeta consists of three core com- ponents: (1) attentive neighbor aggre- mewwlde learning, and (3) task-tailored local adap- tation. Figure 1 illustrates the detailed process of calculating the loss on the sup- port set, through these three components. In essence, MoEMeta O 77) contains two sets of learnable parameters: the parame- ters in attentive neighbor aggregation and MoE-based meta-knowledge learning globally optimized during meta-training, denoted c: while the parameters in task-tailored local adaptation are locally optimized within each task, denoted as 17.
Globally optimized:0
Locally optimized:
gation, (2)
Gate
Expert
L(S+)
Expert
Expert
h
Expert 4
Expert 5
Antentive nzighbour oggropation Meta knowlodge learning
Tosk sallored locol acoptation
The computation of the support loss. The support loss L(S,)) is used to update relation-mete R which is applied to the query set for model optimization.
Figure 1:
4.1 Attentive Neighbor Aggregation
Given a K-shot task T, with its support set S,- r(t)), propose an attentive neighbor encoder that aggregates the neighborhood information for both head entity h; and tail entity t, Specifically, for each entity, the encoder processes its neighboring tuples (neighboring entities and relations), combined with its own embeddings, to generate an enriched representation.
For a given entity its one-hop neighbors are represented as a set of neighboring tuples [(r,, <)) H)= where is the total number of neighbors, et is the neighboring entity and r; denotes the relation between ef and e. We first concatenate the embeddings of r; and e to form the embedding of the i-th tuple, c; r, @ e/, where denotes concatenation of vectors. To capture interactions among neighbors, we then apply linear transformation followed by non-linear activation function:
c/ Re.,)
(2)
where W learnable weight matrix. To enable the encoder to attend to informative neighbors, we compute an attention-like gate value 9i for each neighboring tuple using sigmoid function:
9: a c(),
where 3 is learnable parameter vector. The gate values control the influence of different neighbors the aggregation process. Finally, the weighted neighbor embeddings are aggregated and combined with the entity's own embedding e to obtain the final embedding:
Aggregate( ((g; c)) +
The neighbor encoder is applied to head entity and tail entity independently to ensure that the final embedding reflects both the entity's intrinsic information and its local neighborhood structure.

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4.2 MoE-based Meta-Knowledge Learning
To overcome the limitation of existing methods that neglect shared meta- eetta-nowledge across related tasks, we introduce globally learned MoE model that captures common relational patterns, termed relational prototypes. For each task, a subset of relevant experts dynamically selected from a shared pool based on its support triplets to learn the relation-meta. This mechanism enables adaptive relation decomposition and expert specialization, facilitating the learning of diverse, compositional relational patterns and promoting effective generalization across tasks.
Specifically, we design a sparsely-gated MoE model consisting of M experts. Each expert within the MoE is architecturally identical to a standard MLP (multilayer perceptron). For a given support triplet, gating mechanism dynamically routes the triplet to a selected subset of experts, assigning weights to each expert in the subset based on its relevance. The outputs of the top-N experts are then aggregated according to their weights, resulting in a representation of the relation associated with the triplet. To compute the relation- meta for a support set, we average the relation representations derived from all triplets within the support set. This approach enables the MoE to retrieve the most relevant meta-knowledge from the shared expert pool to model diverse relational patterns.
Given a K-shot task Tr and its support set S, {(h,, r, (4) the head and tail entities of each triplet, h, and 4,, are augmented via our attentive neighbor aggregator to obtain the embeddings h, and t.. The relation representation r; derived from (ha, 44) can be computed by our MoE as follows: M
r =
g (h,, t;: 0y;
(5)
N
j=1
st.j+ st E Top, (( | M),
9 0, otherwise,
(6)
$2,5 = softmax softmax(Gate(h,, ty; 6,),,,
(7)
where each (; Oj) is an expert network and M denotes the total number of experts. For each support triplet i, a gating network Gate(;; 0,) computes relevance score 81,5 for each expert j. conditioned on the corresponding inputs h, and t., These scores are then normalized via softmax function over all M experts. The function Top, N)) selects only the top N highest scores among M experts for each triplet setting the remaining scores to zero. This sparsity mechanism enables the model to selectively activate only subset of experts for each support triplet, facilitating the learning of more specialized relational prototypes globally shared across tasks.
The final relation- meta is computed as the average of relation representations from all support triplets:
K
RT.
r
(8)
K
1=1
4.3 Task-Tailored Local Adaptation
As discussed earlier, the global parameters alone-W W and /3 in attentive neighbor aggregation, and (0, )M and 0g in meta-knowledge learning- be insufficient for effective task-specific adaptation from only few support triplets. The unique characteristics of individual tasks difficult to capture with a shared initialization. To address this issue, we propose a dynamic learnable projection mechanism that facilitates local adaptation, allowing task-specific relational learning tailored to each task.
Specifically, for each task, MoEMett maintains a task-specific set of local parameters, denoted as n). namely three randomly initialized projection vectors, Pa. Pr. and p.. which project the embeddings h,, Ry,, and t; into a task-specific subspace. Inspired by TransD (Ji et al., 2015), which addresses the limitations of fixed mapping strategies using per-entity and per-relation projections, our approach leverages task-specific projections to avoid overparametrizztion under few-shot settings. As all triplets within a task share the same relation, we adopt a unified projection vector Pt, for all head entities, and p, for all tail entities within the task. This provides lightweight solution for enabling efficient task-speeific adaptation with minimal parameter overhead.
Unlike previous FSRL methods including RelAdapter (Ran et al., 2024), which do not account for diverse interactions between entities and relations tailored to different tasks, our approach allows for pelattoonsppciffe adaptation, with projection operations defined below:
5

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h h + RT.) RT,, R/. = R,, + (p, RT,)) Ry, E/ = t, (p. RT,,) R,y,
(9)
_
(10)
(11)
Above, the dot product P. Rr, yields scalar alignment score between each projection vector Px, E {h, r, th, and the relation-mett Ry.. This scalar score is then used to scale RT,, yiclding an offset vector that modulates corresponding features in the entity embeddings and relation-meta The updated embeddings (h/, R/., t() thus capture task-specific nuances while maintaining consistency with the generalizable meta-Kinowledge. Although the three projection vectors could functionally be merged into a single perturbation term, maintaining separate projections for the head entity, relation- meta, and tail entity enhances modularity and flexibility. This design allows for more expressive or heterogeneous projection functions that can incorporate richer inductive biases or task-specific modeling needs in future extensions. Overall, this local adaptation scheme ensures that each task can adaptively focus on its unique aspects while preserving shared commonalities across related tasks.
To further optimize the adapted embeddings h R,y,- and t,, we define a scoring function in the projected space to measure the plausibility of each triplet (hş T, fj), which is given by:
score(h4, 4) = h R',
(12)
where ||-1|2 denotes the εg norm. h/. RT,, and t/ are the projected embeddings of the head entity, relation-meta, and tail entity, respectively. This formulation ensures that, in the task-specific projection space, the distance between the adapted head entity embedding h' and the adapted tail entity embedding t. is minimized after being translated by the relation-mett embedding R',.
Consider the entire support set, we define a margin- based loss function as follows:
((S.)
Σ
max score(h;, 44) + score(h;, 4)),
(13)
(....
where y is a margin hyperparameter, and (he, ft) represents a negative triplet generated by replacing the tail entity t, with randomly sampled entity fz E E such that (h4, f)) & g.
Once the gradients of the support loss L(S,) are computed, we update the projection vectors Pa. Pr- Pr, together with the relation-meta RT,, to ensure generalization to the support set. Specifically, the projection vectors can be updated in unified manner as follows:
OC(S,) Px + DR. 3p.
E th
(14)
Similarly, the relation- meta Ry, is updated as:
OL(S.)
RT, RT.
(15)
aRT,
where a is the learning rate. These updates adapt the model using the support set, which is then evaluated the query set Qr. For each query triplet (,g,,,,t)) Q,, we evaluate its plausibility for each b from the candidate set Cha,r. using the score function Eq. (12). Then, the query loss is similarly defined as:
(()))
Σ
max (0, score(hy; (n)) + - score(hy,: (,))
(16)
(b
where (hq, kg) is negative triplet generated by corrupting the tail entity ty with a randomly sampled entity fg E E. The optimization objective for training the entire model is to minimize L(
The task-specific projection vectors Ps- Pr. and P: are randomly initialized and trained for each task on the support set S,,, enabling effective local adaptation. This mechanism provides two key advantages. (1) Task-specific adaptation: By projecting embeddings into a task-specific latent space, the model captures fine-grained task-specific local contexts. (2) Complementarity to MoE: While the MoE captures globally shared relational prototypes, dynamic learnable projections provide finer-grained local adaptation, thereby improving model generalization under few-shot settings.

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4.4 MoEMeta Meta- Traannriinnn and Meta-Testing
During meta-training. MoEMeta optimizes two sets of parameters: global parameters + = {W. ,3 (0) 0g), for attentive neighbor aggregation and MoE-based meta-knowledge learning, which are globally optimized; and task-specific adaptation parameters 7 = {Pr. Pr. Pt which are optimized locally for each task. The meta-training algorithm of MoEMeta detailed in Algorithm 1.
During meta-testing, the learned global parameters c are used to initialize the model and remain frozen. For each novel task, only the local parameters 7 are fine-tuned on the support set before evaluation on the query set by ranking candidate tail entities, following the same inference procedure as in meta-training. The complexity analysis of MoEMeta's components is provided in Appendix A.
Algorithm 1: Meta-training Algorithm of MoFMeta
Input: Meta- tetta-trannig task set Du. knowledge graph g Output:
Optimized global parameters + Initialize global parameters
(W. (0), 0y):
2 while not converged do
Sample task Tr {Sr, 2r) Dus
foreach (ha, 4;) E S, do
// Attentive Neighbar Aggregation Attentively aggregate neighbors using Eq. 2-Eq. 4; Learning
// Mosed Meta-Knowlesd
Compute expert scores via Eq. 7;
Select top-N experts and compute relation representation
Compute the initial relation-men meta Ry, via Eq.
// Inner Loop: Initialize specifc adaptation parameters = (Pa, Pr. P:]; foreach (h, r, t.) E S, do
Tailored Local Adaptation
9
10
11
Compute projected embeddings using Eq. 9-Eq. 11;
Compute the support loss ((S,)) via Eq. 13;
Update task-specific projection vectors Ps: Pr, Pe via Eq. 14; Update relation-meta R,, via Eq. 15;
// Outer Loop: Meta Meta-Update Compute the query loss ((2r) over all (hy, tg) E Qr via Eq. 16;
15
16
Update global parameters via gradient descent on L(Q,).
5 Experiments
Datasets. We evaluate our method three widely used KG benchmarks specifically for few-shot relational learning: Nell-One and Wiki-One (Xiong et al., 2018), as well as FB15K-One (Ran et al., 2024). The vaanningvallddationnesss splits include 5 51/5/11, 133/16/34, and 75/11/33 tasks on Nell-One, Wiki-One, and FB 1KK-One respectively. Detailed dataset statistics are summarized in Table 1.
Evaluation Metrics. We report both MRR (mean reciprocal rank) and Hits@k (k = 5,5, MRR captures the average recip- rocal rank of correct entities, while Hits @k measures the proportion of correct entities ap- pearing in the top i predictions. Following prior studies, method is evaluated against baselines under the I-shot and 5-shot settings on Nell-One and Wiki-One, which commonly used for evaluating FSRL, and under the 3-shot setting on FB 5Keene, following the setup of RelAdapter (Ran et al., 2024).
Table 1: Statistics of datasets.
Dataset
# Relations # Entities # Tripllts
Tassss
Nell-One
358
68,545
181,109
67
Wiki-One
822
4,838,244
5,859,240
183
FB15K-One
237
14,541
281,624
119
Baselines. We compare our proposed method with two categories of state-of-the-a basclines:
Conventional KG embedding methods: This group focuses modeling relational structures KGs to learn entity and relation embeddings. We compare with four widely-used baselines including TransE (Bordes et al.. 2013), TransD (Ji et al., 2015), DistMult (Yang et al., 2015), and
7

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Table 2: Comparison against fftte-of-the-nne methods Nell-One and Wiki-One. MetaR-I and MetaR-P indicate the In-train and Pre-train of MetaR (Chen et al., 2019).
Nell-One
Wiki-One
MRR
61
H
Hits@
MRR
@its@1
Hits@5
Hits
Methods
I-shot
5-shot
I-shot
S-shot
I-shot
Sishot
I-shot
5-shot
1-shot
5-shot
I-shot
5-shot
I-shot
5-shot
I-shot
5-shot
TransE
0.105
0.168
0,041
0.082
0,111
0.186
0.226
0.345
0.036
0.052
0.011
0.042
0.024
0.057
0.059
0.090
TransD
0.159
0.276
0.125
0.159
0.162
0.322
0.234
0.431
0.063
0.101
0.034
0.061
0.062
0.104
0.135
0.181
DistMult
0.165
0.214
0,106
0.140
0.174
0.246
0.285
0.319
0.046
0.077
0.014
0.035
0.034
0.078
0.087
0.134
Complix
0.179
0.239
0.112
0.176
0.212
0.253
0.299
0.055
0.070
0.021
0.030
0.044
0.063
0.100
0.124
GMatching
0.185
0.201
0.119
0.143
0.260
0.264
0.313
0.311
0.200
0.120
0.272
0.336
FSKGC
0.184
0.136
0.234
0.272
0.158
0.097
0.206
0.287
FAAN
0.279
0.200
0.428
0.341
0.281
0.436
MetaR-1
0.250
0:261
0.170
0.168
0.336
0.350
0.401
0.437
0.193
0.221
0.152
0.178
0.233
0.264
0.280
0.302
MetaR-P
0.164
0.209
0,093
0.141
0.238
0.280
0.331
0.355
0.314
0.323
0.266
0.270
0.375
0.385
0,404
0.418
MetaPt
0.232
0.179
0.281
0.330
GANA
0.236
0.245
0.173
0. 166
0.293
0.334
0.347
0,390
0.260
0.261
0:221
0.317
0.307
0.333
0.334
0.384
HiRe
0.258
0.306
0,181
0
0.207
0:403
439
0.472
0
0.322
0.371
0.271
0.319
0.383
0.419
0.433
0.469
RelAdapteri
0.247
0.305
0.209
0.245
0.281
0,365
0.307
0.415
MoEMeta
0.322 0.3391
0,228* 0.236 0.429* 0.440
0.501
0.523
0.3355
0,377
0.276 0.324
0.385 0.420
0.434
0.473
t: MetaP and RelAdapter's 's results are not fully available due to unrelcased pre-trained information.
ComplEx (Trouillon et al., 2016). Using OpenKE (Han et al., 2018), these models are reproduced with hyperparameters specified in the original papers.
Few-shot relational learning methods: We compare our method against state-of-the-ar FSRL baselines evaluated under the same framework, including (1) metric-learning methods; GMatch- ing (Xiong et al., 2018), FA (Sheng et al., 2020), FSKGC (Zhang et al., 2020); and (2) strong meta-learning competitors: MetaR (Chen et al., 2019), GANA (Niu et al., 2021), HiRe (Wu et al., 2023), and RelAdapter (Ran al., 2024). We exclude methods like CSR (Huang et al., 2022) and NP-FKGC (Luo et al., 2023) as they employ a different training and evaluation protocol.
All results are obtained after models are trained using all triplets from background relations (Xiong al., 2018) and meta-training tasks, and then evaluated on meta-testing tasks.
Experimental Setup. Following prior work, we set the embedding dimension to 100 for Nell-One and FB15K-One and 50 for Wiki-One, initialized using Lunns--pretrained weights provided by GMatching (Xiong et al., 2018). The number of neighbors per entity is capped at 50 for aggregation. For MoE, cach expert network is two-layer MLP (hidden dimension: 64, ReLU). The gating network is also a two-layer MLP (hidden dimension: 64, output dimension: 1, and ReLU).. We set the number of experts M as 32 and the number of selected experts N as 5. The margin y in Eq. 16 is set to 1. MoEMeta is trained using the Adam optimizer (batch size: 1, 024, learning rate: 0.001). All results are averaged over independent runs with different random seeds. All models are implemented in PyTorc and trained on single Tesla P100 GPU. More details for reproducing our results are provided in Appendix B.I.
5.1 Comparison with Baseline Methods
Tables 2 and 3 compare MoEMeta against state-of-the-art methods on Nell-One and Wiki-One under the 1-shot and 5-shot settings, and on FB15K-One under the 3-shot setting. The best and the second best performers are respectively marked by grey and underline. Our proposed MoËMeta con- sistently achieves ofate-of-the-art results across all datasets and settings. On Nell-One, MoEMeta surpasses HiRe, the second best performer, by 11.8% MRR. 23.9% Hits 1, 6.4% Hits 5, and 6. 1% @its@ 10 under 1-shot setting, and 10.8% MRR, 14.0% Hits @ 1, 2.3% Hits@5. and 0.6% @its@ 10 under 5-shot setting. On FB 15K-One, MoEMeta outperforms the second best method by 4.4% MRR, 0.3% Hits@I I, and 13.2% Hits@5 5 under s-ohot setting. We further conduct paired t-tests between MoEMeta and the second best performers on all datasets. The improvements of MoEMeta over the second best method with statistical significance at the 0.05 level are marked with * These results highlight MoEMeta's efffcctiveness in learning
Table 3: Comparison under 3-shot set- ting on FB15K-One.
FB15K-On
Methods
MRR
Hits@ I
Hits@ 10
TransF:
0.294
0.204
0.437
TransD
0.301
0.203
0.439
DistMult
0.234
0.208
0.364
ComplEx
0.239
0.205
0.359
GMatching
0.309
0.245
0.441
MetaR
0.368
0.251
0.536
FAAN
0.363
0.279
0.542
GANA
0.388
0.301
0.553
HiRe
0.378
0.281
0.571
RelAdapter
0.405
0.297
0.575
MoEMeta
0.423
0.302
0.651
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Table 4: Ablation study on Nell-One and FB15K-One.
Nell-One
FB15K-One
1-shot
5-shot
3-shot
Ablation on
MRR Hits@1
1 H
Hits@ 10]
MRR
Hits@1
@
Hits@ 10
MRR Hits@1 I Hits@ 10
MoEMeta
0.322
0.228
0.429
0.501
0.339
0.236
0.440
0.523
0.422
0.298
0.652
w/o N.A
0.311
0.219
0.418
0.489
0.328
0.231
0.436
0.509
0.413
0.286
0.631
w/o MoF
0.291
0.199
0.392
0.467
0.293
0.201
0.399
0.484
0.401
0.273
0.624
w/o L.A
0.301
0.206
0.398
0.476
0.315
0.213
0.415
0.501
0.408
0.285
0.628
N.A indicates "attentive neighbor aggregation" and L.A indicates "task-tailored local adaptation".
generalizable meta-knowledge that adapts well to novel relations and proving its competitiveness against other baselines.
Across three datasets, the most substantial improvements are on Nell-One, particularly in MRR under both 1-shot and 5-shot settings. The primary gain is attributed to the increase in the challenging Hits@I metric, which gauges the model's ability to rank the correct tail entity as the top candidate among all choices. This is especially notable since triplets in Nell-One are constructed in natural language, providing richer semantics that can be more effectively captured by our relational prototypes. These results validate the efficacy of our proposed MoE-based relation-meta learner in learning globally shared, fine- ane-grained relational patterns and adapting these prototypes to new, unseen tasks. We additionally provide runtime analysis for both meta-training and meta-testing in Appendix B.2.
5.2 Ablation Studies
To assess the contribution of MoEMeta's three key components, attentive neighbor aggregation, MoE-based meta-knowledge learning, and task-tailored local adaptation, report ablation results on Nell-One under the I-shot and 5-shot settings, and FB 15K-One under the 3-shot setting in Table 4.
Removing attentive Neighbor Aggregation (w/o N.A), which removes the first term in Eq. 4, results in a moderate performance decrease across all settings. This suggests the importance of incorporating neighboring information to enhance entity representations.
Replacing MoE (w/o MoE) with an ML.--ased relation-meta learner from (Chen et al., 2019) leads to significant performance decline; MRR drops by 9.6% and 13.6% under 1-shot and 5-shot settings on Nell-One, and 5.0% under 3-shot setting on FB15K-Om This underscores MoE's indispensable role in acquiring relational prototypes as generalizable meta-knowleege.
Local Adaptation (w/o L.A), where the learned relation-mett is directly applied to the query set, also causes a significant performance drop across datasets. This suggests that global meta- mnta-knowledde alone is insufficient, highlighting the crucial role of task-tailored adaptation for capturing local distinct patterns and improving generalization across tasks.
A Ablating
5.3 Analysis on Complex Relations
To further evaluate the significance of task-tailored local adaptation for complex relations, we conduct a detailed analysis on Nell-One under the 5-shot setting by categorizing the meta-testing set into three relation types: I-N, N-1, and N-N. In particular, we compare MoEMeta with a variant that removes task-tailored local adaptation (w/o L.A). As shown in Table 5, the removal of local adaptation leads to substantially larger performance drop 1-N and N-N relations than on N-1 relations. This is because, in N-1 relations, the tail entity is relatively predictable, with a smaller search space and more stable mapping patterns. In contrast, 1-N and N-N relations exhibit higher ambiguity and more dispersed distribution of tail entities, making them more difficult to predict. Notably, removing task-tailored adaptation results in the largest performance drop for the N-N relation type. This analysis confirms the critical role of our task-tailored adaptation mechanism in enhancing generalization to complex and highly ambiguous relational patterns.
In addition, we visualize the distributions of gating values of different relations in Figure 2 to provide a more intuitive understanding of why learning relational prototypes works. As observed, relations with similar patterns (in the upper figure) exhibit a comparable gating value distribution and activate similar subsets of experts, whereas dissimilar relations (in the lower figure) demonstrate a markedly different pattern. This observation highlights our model's 's ability to distinguish diverse relational patterns but capture globally shared relational prototypes across related tasks.

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Helaellste identillie te sirmlari liies
Aconncssettaetta anincruuity New becaiicuuttt
25
Relations with langer divergence
Ascomodee
Copert 2d80
Two gating value distributions over 32 experts generated by our MoE-based meta-knowledge learner. The upper figure shows that three similar relations exhibit similar gating value distributions, suggesting that similar set of experts are activated. In contrast, the lower figure demonstrates that for relations with distinct relational patterns, the activated experts differ significantly. This highlights the presence of shared relational patterns, underscoring the necessity of learning relation prototypes.
Figure 2:
Table Performance analysis under 5-shot setting on Nell-One forl-N, N-1 and N-N relation types.
Methods Rel. types
MRR Hits @
Hits Hits@ 10
1-N
0.406
0.310
0,506
0.577
MoEMeta
N-1
0.945
0.913
0.986
0,986
N-N
0.404
0.279
0.536
0.618
I-N
0.391
0.292
0.500
0.565
w/o L.A
N-1
0.874
0.871
0.986
0.986
Figure 3: Hyperparameter sensitivity study of
N-N
0.372
0.256
0.485
0.586
MoE under 5-shot setting on Nell-One.
5.4 Hyperparameter Analysis on MoE
To assess the impact of MoE hyperparameters, the number of experts M and the number of top selected experts N, we conduct sensitivity analysis on Nell-One under the 5-shot setting as case study. When fixing N and varying M E {16, 32, 64). MoEMeta achieves the best performance with 32 experts. This aligns with the guideline of setting the number of experts M smaller than the total number of tasks to encourage learning shared relational prototypes and prevent overfitting with limited support triplets. When fixing M 32 and varying N E {3, 5, 10), we observe that selecting slightly more experts (e.g., 5 vs. 3) leads to improved performance, yet increasing N 10 causes only slight drop, suggesting that MoEMeta is relatively insensitive to N within a reasonable range.
6
Conclusion
This work presents MoEMeta, novel meta-learning framework for few-shot relational learning in KGs. By integrating an MoE model to capture globally shared relational prototypes with a task- tailored adaptation mechanism to refine embeddings locally, MoEMeta effectively balances cross-task generalization and rapid task-specific adaptation. Extensive experiments on three real-world KG datasets demonstrate that MoEMeta consistently outperforms state-of-the-i methods, showcasing its effectiveness for few-shot relation learning. Comprehensive ablation studies and visualizations further validate the importance MoEMeta's 's key components, confirming its efficacy in modeling complex relational data in KGs under limited supervision.
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1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The main claims the abstract and introduction accurately reflect the paper's contributions and scope. Specifically, we propose MoFMeta with a mixture-of-ex model and a task-tailored adaptation mechanism learn relational prototypes shared across tasks and improve few-shot relational learning.
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Appendix
A Complexity Analysis
In this section, we analyze the computational complexity of our proposed MoEMeta for its key components. Table 6 summarizes the computational complexity of each component, where d denotes the dimension of entity embeddings, n denotes the number of neighbors in attentive neighbor aggregation, and K denotes the number of support triplets per task. For MoB-based meta-knowledge learning, M indicates the total number of experts, and N indicates the number of experts activated in each forward pass.
As shown in Table 6, the complexity of MoEMeta scales linearly with the number of neighbors per entity during the aggregation stage (O K ndd)) and linearly with the number of support triplets during MoE-based meta-knowledge learning (O Kd.)). The task-tailored local adaptation introduces only minimal overhead of (O(d) in both parameters and computation. Overall, the computational cost dominated by neighbor aggregation (O(Knd)),, )) which is an essential component in current state-of-the the-thtt art FSRL methods. The additional computational and parameter overhead introduced by MoEMeta is negligible, making it efficient for FSRL in KGs.
Table 6: Complexity analysis of MoEMeta components with respect to the number of model parame- ters and the number of multiplication operations in each epoch. denotes the dimension of entity embeddings. n denotes the number of neighbors per entity, and K denotes the number of support triplets per task, where K < n, d. M is the total number of experts, while N is the number of experts activated in each forward pass.
Component
# Parameters
Multiplication Operations
Attentive neighbor aggregation
(
O (Knd
MoE-based kntwledg learning Task-tailore local adaptation
((MP)
(NN
((d)
O(d)
Total
((MP)
OnKKd²)
B Additional Experimental Details and Results
B.1
Reproducibility Details
In our experiments, our method and all baselines are compared using the same evaluation protocol. The results of baseline methods are reproduced by using their official open-sourced implementations or adopted from their respective papers.
For MetaR (Chen et al., 2019) (both In- Tr-Train and Pre-Train) FFAiNN (Sheng et al., 2020)', MetaP (Jiang al., 2021)* and HiRe (Wu al., 2023).. we directly use the results reported in their papers. The results for GMatching (Xiong et al., 2018) under 1-shot and 5-shot settings are taken from (Chen al., 2019). Since FSKGC (Zhang et al., 2020)? was originally evaluated under different conditions (with smaller candidate set), we use the re-implemented results provided by FAAN to ensure consistency. For GANA (Niu et al., 2021)S, we reproduce its results by setting the number of neighbors to 50, aligning with the settings of other methods. We reproduce the results of RelAdapter (Ran et al., 2024)" under the I-shot and 5-shot settings on Wiki-One. However, as the tre-trained contextual information required for Nell-One is not released, the performance of RelAdapter on this dataset is unavailable for evaluation. Following RelAdapter, the comparison
com com 2 MetaR: http:///ggggtuubocco//ssss/////e /MetaRR TBAANi/tgpe://iittbb.ooo////
4 httpp//awpprrgggg.b...om////////
'HiRe: httpp://ggithab..mmm//eest//////
Leaaninn
"FSKGC: T/S.SCCCC..r/////s..o.ooo/oaaaaa FSRL
$GANA: hct.kh/wwwpp///h..u....o/ooooola
"RelAdapter: http://ggthhttcooo/////////aaaa/
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results on FB15K-OP under 3-shot setting are reported in Table 3, with all baseline results sourced from RelAdapter (Ran al., 2024). All models are implemented in PyTorch and trained on a single Tesla P100 GPU.
B.2 Runtime Analysis
We also perform a runtime analysis comparing MoEMeta with other baselines. For fair comparison, we evaluate MoEMeta on an RTX3090 GPU to keep consistency with baseline runtimes reported in RelAdapter (Ran et al., 2024). Table reports the total runtime of meta-training and the inference time per triplet during meta-testing in seconds.
During meta-training, MoEMeta is only slightly slower than MetaR and RelAdapter, while being faster than other baselines such as GANA and FSKGC. MoEMeta requires 26,277 seconds to train on the NELL-One dataset, compared to 22,691 seconds for MetaR and 24,085 seconds for RelAdapter. The moderate increase in training time mainly comes from the two additional MLPs used in the MoE. Despite this additional overhead, MoEMeta achieves significantly better performance. For example. under the 1 --shot setting on NELL-One, MoEMeta outperforms MetaR, by 28.8% MRR, 34.1% 1% Hits@ 27.7% Hi and 24.9% Hits@@ 10, respectively (see Table 2). For the inference during meta-testing, all methods including MoEMeta complete in milliseconds per triplet.
Table 7: Runtime comparison of meta-training and meta-testing time Wiki-One and FB15K-ORE
Meta-training Time (seconds)
Meta-testing Time per Triplet (seconds) Wiki-One
Methods
Wiki-One
FB15K-One
FB15K-One
GMatching
28,941
19,678
0.009
0.004
FSKGC
28,742
20,765
0.013
0.004
GANA
35,374
28,167
0.017
0.006
FAAN
32,036
23,675
0.016
0.005
HiRe
34,257
27,736
0.036
0.007
MetaR
22,691
16,504
0.012
0.005
RelAdapter
24,085
17,529
0.045
0.008
MoEMeta
26,277
18,321
0.019
0.006
C Detailed and Additional Related Work
For a more comprehensive discussion of related literature beyond the main paper, we provide additional related work in this section.
C.1 Supervised KG Embedding Learning
KG embedding methods aim to learn a low-dimensional embedding space for entities and relations in KGs using input triplets as supervision. They broadly fall into two categories for KG relational learning: (1) Translation-baan based methods, such as TransE (Bordes al., 2013), TransH (Wang et al., 2014), TransD (Ji et al., 2015), and TransR (Lin et al., 2015), which assume translational relationship between entities and relations for learning unified embedding space; and (2) Semantic matching methods, such as ComplEx (Trouillon et al.. 2016), RotatE (Sun et al., 2019). and ComplEx- vector or complex
N3 (Lacroix et al., 2018), 8), which improve the modeling relational patterns space. However, these methods rely on large amounts of triplets for training. and their performance degrades significantly under few-shot settings, where only handful of training triplets are available for each relation.
C.2 Few-Shot Learning and Meta Learning
Few-shot learning aims to train supervised model that can generalize new tasks with only limited amounts of training data (Fei-Fei et al., 2006). Meta-learning (Finn et al., 2017; Santoro et al., 2016; Ravi and Larochelle, 2017; Nichol and Schulman, 2018; 8; Ren al., 2018) has emerged as powerful paradigm for enabling rapid learning on few-shot tasks by leveraging meta-knowledde
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learned from meta-training tasks. Relative to metric- based approaches (Jake Snell and Zemel, 2017; Vinyals al., 2016). optimization- meta-learning methods have gained significant attention due to their strong generalization capabilitiis. These methods cast meta-learning into a bi-level optimization problem; the inner loop fine-tunes base model on individual tasks, and the outer loop optimizes the base model for quick adaptation across tasks. MAML (Finn et al., 2017) is the most representative algorithm in this category, which learns global parameters as an initialization to be shared among task-specific models for fast adaptation using one or a few steps of gradient descent updates. Due to its flexible formulation, MAML has been applied to various domains, including computer vision, robotics, lifelong learning, and beyond. Subsequent variants of MAML (Finn et al., 2018; Von Oswald et al., 2021) 1) improve upon its adaptability by optimizing model initialization or learning adaptive learning rates for faster convergence (Sun and Gao, 2023; Zou et al., 2021; Finn et al., 2018). Our work focuses on learning well-generalized meta-knowledge across tasks while integrating task-specific local adaptation improve few-shot relational learning in KGs.
C.3 Few-Shot Relation Learning
Few-shot relation learning (FSRL) addresses the challenges of learning with limited training examples and relational information in KGs. Current FSRL approaches can be broadly categorized into two groups: metric-learning methods and meta-learning methods. Metric-learning methods focus on learning similarity metric between the support and query sets for adaptation. GMatching (Xiong et al., 2018) introduces one-shot relation learning, where a one-hop neighbor encoder with equal weights is used to learn entity embeddings and matching network is designed compare similarity between support and query entity pairs. FSKGC (Zhang et al., 2020) generalizes the setting to more shots and employs recurrent autoencoder aggregate few-shot instances in the support set, yet imposing an unrealistic sequential dependencies among support triplets. FAAN (Sheng et al., 2020) proposes a dynamic attention mechanism improve the aggregation of one-hop neighbors. CSR (Huang et al., 2022) employs subgraph matching as a hypothesis testing to adapt from the support triplets the query set. NP-FKGC (Luo et al., 2023) employs neural process to better handle distribution shifts for unseen relations.
Most mainstream FSRL methods are based on meta-Icarning (Finn al., 2017). which focuses on learning an initial meta-prior from existing tasks that can generalize to new few-shot relations. MetaR (Chen et al., 2019) learns relation-specific meta-information for adaptation, by simply aver- aging the representations of all support triplets. GANA (Niu et al., 2021) integrates meta-learning with TransH (Wang al., 2014) and devises gated and attentive neighbor aggregator address sparse neighbors for learning informative entity embeddings. HiRe (Wu et al., 2023) exploits multi- granularity relational information to learn meta-knowledge that better generalizes unseen relations. RelAdapter (Ran et al., 2024) adopts a shallow feedforward network with a residual connection to adapt relation-mest meta within each target task. Additionally, pre-trained context information of related entities is used to augment entity embeddings.
Despite their great progress, the aforementioned meta-learning based methods suffer from two key limitations. First, these methods neglect common relational patterns shared across tasks when learning meta-knowledge during meta-training Second, they fail to effectively incorporate local, task-specific information essential for fast adaptation, as global meta-knowledge wedge may not be optimally tailored to the unique characteristics of individual tasks. Our work is thus proposed address these critical gaps within the meta-learning framework, enabling more effective model generalization and adaptation under few-shot settings.
Our work is also related to recent graph-based and contrastive meta-learning methods such as (Liu et al., 2022) and (Liu et al., 2024). While these approaches are relevant to few-shot learning and meta-learning on node-level tasks for homogeneous graphs, they are not directly applicable to our setting, which focuses few-shot relational learning in KGs- that centers on relation-level tasks. Our method, MoEMeta, specifically targets the key challenges of few-shot relational learning, such as the complex and diverse relational patterns in KGs. This distinction our work apart from general graph few-shot learning or meta-learning methods that target node-level tasks.
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Reviews:
[{"review_id": "JLAL00ugbe", "reviewer": "Reviewer_XdzE", "summary": "This 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.", "questions": "Overall, 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.", "limitations": "Limitations have not been addressed in this paper.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "Authors' rebuttal was satisfactory", "rebuttal": "We sincerely thank the reviewer for providing supportive comments and acknowledging the strengths of our work. We respond to your questions and provide clarifications as follows: \n\n> 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. \n\nWe agree with the reviewer that while task-specific modulation has been well studied in meta learning, its application to relational learning has been surprisingly limited. We believe that this gap exists precisely because a direct application of modulation from general meta-learning is inapplicable or insufficient for the structured, non-i.i.d. domain of knowledge graphs (KGs) for few-shot relational learning (FSRL), where only a handful of training triplets are available per task. Our work is thus proposed to fill this critical gap by proposing a novel unified framework that enables both generalization across similar tasks and fine-grained local adaptation.\n\nTo our best knowledge, RelAdapter (published in 2024) is the only baseline using task-specific adaptation. However, its simple approach--using an MLP to generate a single adaptation vector--fails to account for diverse interactions between entities and relations tailored to individual tasks. Our proposed approach addresses this limitation through explicitly projecting entity and relation embeddings into a task-specific subspace, offering a more principled solution for task-specific adaptability.\n\n> More specifically, it's surprising that no prior work has considered aggregating the neighbor 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.\n\nThank you for this insightful feedback. We would like to clarify the distinct contributions of our work by addressing each point.\n\n- **On neighbor aggregation**: We clarify that neighbor aggregation is not novel; it is an essential component used by SOTA methods. Our work builds upon this standard practice, which is why we did not claim it as a contribution.\n\n- **Technical novelty of our work**: We agree that the high-level concepts of task-specific modulation and MoE are not new in isolation in general meta-learning. Our core technical novelty lies in designing a principled framework that solves a critical, non-trivial challenge unaddressed by prior FSRL work: **How to disentangle shared, global knowledge from task-specific, local context to enable effective generalisability and fast adaptation under few-shot settings.**\n\nOur MoEMeta framework provides a principled solution to this challenge: \n- **Global knowledge (what to generalize)**: This is achieved by our MoE-based relational prototypes that capture **common relational patterns shared across similar tasks** for learning relation-meta representations. Unlike Meta-DMoE [1] where each expert is specialized for a specific task domain, our MoE is designed to learn common relational patterns shared across tasks for better generalization. Our experts are trained to learn distinct relational prototypes. The gating network then learns to represent each new, unseen relation as a weighted combination of these shared, fundamental prototypes. This is a novel formulation that explicitly models the compositional nature of relations in KGs.\n\n[1] Zhong, Tao, et al. \"Meta-DMoE: Adapting to domain shift by meta-distillation from mixture-of-experts.\" NeurIPS 2022: 22243-22257.\n\n- **Local knowledge (what to adapt)**: This is achieved by our task-tailored adaptation mechanism that leverages task-specific structural context--unique to each task--to fine-tune relation-meta and entity embeddings. As the reviewer noted, \"the details seem novel.\" and this distinction is crucial. Unlike general task-specific modulation methods [2, 3], which focus on predicting task mode to modulate the meta-learned prior parameters, our adaptation is a lightweight solution operating at the embedding level, specifically designed to capture the diverse interactions between entities and relations in individual tasks. For each task, we locally learn three randomly initialized projection vectors, allowing for highly specific and efficient adaptation.\n\n[2] R. Vuorio et al., Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation, NeurIPS 2019: 14632-14644.\n\n[3] Mi. Abdollahzadeh et al., Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning, NeurIPS 2021: 1-12.\n\nThis dual approach forms a novel unified framework that enables both generalization across similar tasks and fine-grained local adaptation. We will revise the paper to make these points more explicit and better contextualize our contributions with respect to the broader meta-learning literature. \n\n> More baselines and more public datasets. \n\nWe appreciate your valuable suggestion. We aimed to provide a thorough comparison against the most relevant and current methods. To clarify, our evaluation includes RelAdapter (published in 2024), which is, to our best knowledge, the latest SOTA baseline within the meta-learning paradigm for this task.\n\nFor fair comparison, our use of Nell-One and Wiki-One is consistent with the standard evaluation protocol used by the majority of prior work in few-shot relational learning. To further validate the generalizability of our method, we have also expanded our experiments to include a third dataset, FB15K-One, adopting the same setup introduced by the SOTA method, RelAdapter. The results are reported in Table 3."}, {"review_id": "8vzeKqPhZp", "reviewer": "Reviewer_QaZM", "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.", "questions": "1. How the proposed model performs on traditional datasets such as FB15K237 and WN18RR?\n2. 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. This paper is well-written and easy to follow.\n2. The authors develop a few-shot knowledge graph completion model with the widely-used MoE strategy to model various relation patterns.\n3. The experimental results illustrate the effectiveness of MoEMeta.\n\nWeaknesses:\n1. 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.\n2. The lack of experimental results on traditional datasets such as FB15K237 and WN18RR makes it difficult to demonstrate the generalization of the developed model.\n3. 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.", "was_revised": true, "final_justification": "Some of the concerns have been addressed.", "rebuttal": "We respectfully reiterate that MoEMeta is **NOT** a simple extension of existing methods such as MetaR or GANA. Instead, we have proposed a novel unified framework that enables both generalization across similar tasks and fine-grained local adaptation. Below, we would like to clarify the technical novelty of our approach, which addresses two fundamental limitations of prior methods.\n\n- **Capturing shared knowledge across tasks**: Unlike existing methods that learn meta-knowledge only within individual tasks, our MoE-based framework is explicitly designed to capture common relational patterns shared across similar tasks. This is a key departure from previous work and allows for better generalization.\n\n- **Task-tailored adaptation**: Prior methods often use a single set of global parameters for initialization (a uniform MAML approach). Our work introduces a task-tailored adaptation mechanism that fine-tunes the shared knowledge using the unique structural context of each task.\n\nImportantly, our work is **NOT** a simple introduction of MoE into few-shot relational learning. It is a new framework carefully designed to disentangle what to generalize (globally shared patterns across tasks) from what to adapt (local task specifics) --- a distinction that prior work does not address. We believe that this is not only novel, but also opens up a new research direction in few-shot relational learning, encouraging future work to explore structured task relationships, rather than treating each task as i.i.d.\n\n> How the proposed model performs on traditional datasets such as FB15K237 and WN18RR?\n\nThanks for your question. We have provided experimental results on **FB15K-One**, a few-shot version derived from FB15K237, to evaluate the proposed MoEMeta. In the literature on few-shot relational learning, **NELL-One** and **Wiki-One** are commonly used benchmarks, whereas **WN18RR** is rarely adopted. One key reason is that WN18RR contains only **11 relations**, making it unsuitable for constructing meaningful few-shot tasks with sufficient diversity.\n\n> Why the amount of experts is set as 32 on all the three datasets?\n\nThank you for this thoughtful question. To clarify, we did not uniformly fix the number of experts across different datasets. Instead, we treated the number of experts as a key hyperparameter and performed a sensitive analysis, as detailed in Section 5.4. On each dataset, we tuned the number of experts in the range of $\\{16, 32, 64\\}$ based on practical guidance from the number of relations and task diversity on that dataset. Our empirical results reveal a consistent finding: performance on all three datasets peaks or plateaus when the number of experts was set to 32, striking a good balance between model capacity and generalization.\n\n> It is suggested to conduct the analysis of the model's complexity.\n\nThanks for this valuable suggestion. We would like to draw your attention to our detailed analysis of model complexity and runtime in the appendix. \n\n- **Complexity Analysis (Appendix A.2)**: We analyze the complexity of all MoEMeta components with respect to the number of parameters and the number of multiplication operations in each epoch. The table below summarizes this analysis.\n\n| Component           | # Parameters      | # Operations       |\n|---------------------|-------------------|---------------------|\n| Neighbor aggregator | $\\mathcal{O}(d^2)$ | $\\mathcal{O}(nkd^2)$ |\n| MoE                 | $\\mathcal{O}(d^2)$ | $\\mathcal{O}(nd^2)$  |\n| Local adaptation    | $\\mathcal{O}(d)$   | $\\mathcal{O}(d)$     |\n| **Total**           | $\\mathcal{O}(d^2)$ | $\\mathcal{O}(nkd^2)$ |\n\n\nThe MoE's parameter overhead of $\\mathcal{O}(d^2)$ ($d$ is the embedding dimension) is on par with the complexity of the neighbor aggregator, which is an essential component in most SOTA methods. Thus, this additional parameter overhead is marginal relative to the model's overall complexity.\n\n- **Runtime Performance (Appendix B.2)**: We provide a runtime comparison with baselines in Appendix B.2. Table 7 reports the total runtime for meta-training and the inference time per triplet during meta-testing in seconds. The table below summarizes this comparison.\n\n| Model              | Meta-training Wiki-One (second) | Meta-training FB15K-One (second) | Meta-testing per triplet on Wiki-One (second) | Meta-testing per triplet on FB15K-One (second) |\n| ------------------ | ------------------------------- | -------------------------------- | --------------------------------------------- | ---------------------------------------------- |\n| GMatching          | 28941                           | 19678                            | 0.009                                         | 0.004                                          |\n| FSRL               | 28742                           | 20765                            | 0.013                                         | 0.004                                          |\n| GANA               | 35374                           | 28167                            | 0.017                                         | 0.006                                          |\n| FAAN               | 32036                           | 23675                            | 0.016                                         | 0.005                                          |\n| HiRe               | 34257                           | 27736                            | 0.036                                         | 0.007                                          |\n| MetaR              | 22691                           | 16504                            | 0.012                                         | 0.005                                          |\n| RelAdapter         | 24085                           | 17529                            | 0.045                                         | 0.008                                          |\n| **MoEMeta (ours)** | **26277**                       | **18321**                        | **0.019**                                     | **0.006**                                      |\n\nDuring meta-training, MoEMeta is only slightly slower than MetaR and RelAdapter, but faster than other baselines such as GANA and FSKGC. On NELL-One, MoEMeta requires 26,277 seconds to train, compared to 22,691 seconds for MetaR and 24,085 seconds for RelAdapter. This moderate increase in training time mainly comes from the two MLPs in the MoE. During meta-testing, MoEMeta, like all baselines, is highly efficient, with an inference time of milliseconds per triplet."}, {"review_id": "wZfulrY4Rq", "reviewer": "Reviewer_oYNg", "summary": "This 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.", "questions": "see above", "limitations": "see above", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "The authors' responses are clear. I maintain my borderline accept score.", "rebuttal": "> Figure 1 lacks clarity.\n\nThank you for your advice. The figure layout is constrained by space limit. we will revise Figure 1 to improve clarity and provide more details to illustrate the main components of the proposed method.\n\n> Incremental contribution. While the integration is reasonable, it does not offer significant innovation beyond existing approaches.\n\nWe understand that our work might initially appear as a combination of existing components. However, we respectfully argue that our technical novelty lies in proposing a principled meta-learning framework to solve a non-trivial, previously unaddressed challenge in FSRL: how to disentangle globally shared patterns across tasks from local task specifics to facilitate fast adaptation to unseen tasks under few-shot settings. \n\nWe would like to reiterate the novel aspects of our work. First, we have correctly identified valid research limitations in the existing few-shot relational learning. These strongly motivate a compelling need for novel methodology to address these limitations. Second, we have proposed two key components to explicitly address these important research gaps:\n\n- **A novel MoE for learning shared relational prototypes:** Unlike existing methods that learn meta-knowledge only within individual tasks, our MoE-based meta-learner is explicitly designed to capture common relational patterns shared across similar tasks for learning generalizable meta-representations. This is a novel formulation that explicitly models the compositional nature of relations in KGs, but also a clear distinction from previous work.  \n\n- **A novel task-tailored adaptation mechanism**: Unlike prior FSRL methods that relies on a single set of global parameters for initialization (a uniform MAML approach). Our work introduces a task-tailored adaptation mechanism that fine-tunes the meta-representations and entity embeddings tailored to the unique structural context of each task. Our adaptation is a novel, lightweight solution, specifically designed to capture the diverse interactions between entities and relations in individual tasks.\n\nTherefore, our key innovation is a novel unified framework that enables global generalization across similar tasks and local adaptability. Our experimental evaluation demonstrates the state-of-the-art performance of our proposed solution, affirming the novelty of our contributions. We will revise the paper to clarify our contributions more explicitly."}, {"review_id": "YGkcIn1Pf5", "reviewer": "Reviewer_fYbM", "summary": "This 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.", "questions": "1. 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.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "The authors' responses are clear and comprehensive. Therefore, I maintain my score of borderline accept.", "rebuttal": "> The discussion on limitations should incorporate more relevant references.\n\nThank you for your valuable comments. Below, we provide further justification for the two limitations identified in prior work and will incorporate this discussion into the final version of our paper.\n\nFirst, we argue that prior FSRL methods typically learn relation-specific meta-knowledge separately for each task, but lack mechanisms to capture patterns that are globally shared across similar tasks. This issue has also been echoed in the broader few-shot and meta-learning literature. For example, works like [4] and [5] have demonstrated the benefits of modeling task similarities or intra-class commonalities within each task. However, this principle has not been systematically explored for FRSL. Our work is the first to introduce a novel MoE for explicitly learning relational prototypes and leverage these shared patterns across tasks for knowledge transfer.\n\nSecond, we identify that existing meta learning-based FSRL methods typically adopt the generic Model-Agnostic Meta-Learning (MAML) framework without accounting for complex relation patterns in KGs. The KG literature has long established that relations exhibit complex, heterogeneous patterns (e.g., 1-to-N, N-to-N mappings) [6, 7], which pose a core challenge for relational learning. A standard MAML-style approach, which learns a single global initialization for all tasks, is ill-equipped to handle this structural diversity. Our work addresses this by introducing a task-tailored adaptation mechanism that explicitly uses the local context of the few-shot examples to adapt the model, a crucial step for handling the diverse nature of relations that has been overlooked for FRSL.\n\n[4] P. Zhou et al., \"Task similarity aware meta learning: theory-inspired improvement on MAML.\" In UAI 2021, PMLR 161:23-33.\n[5] H. Li et al., \"Finding task-relevant features for few-shot learning by category traversal.\" In CVPR 2019: 1-10.\n[6] Z. Wang et al., \"Knowledge graph embedding by translating on hyperplanes.\" In AAAI 2014: 1112-1119.\n[7] G. Ji et al., \"Knowledge graph embedding via dynamic mapping matrix.\" In ACL/IJCNLP 2015: 687-696.\n\n> The \"Related Work\" section should reflect a summary of methodological lineages (including the problems each addresses).\n\nThank you for the valuable feedback. In our Related Work section, we have aimed not simply to list prior papers but to summarize the key limitations of existing methods, which clearly motivates the research gaps that our work targets. The discussion around methodological lineages is kept concise in the main paper mainly due to space limit. Yet, we have provided a more detailed discussion in Appendix C.3. We will further strengthen the Related Work section to ensure the narrative clearly emphasizes these aspects.\n\n> 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? \n\nThank you for the insightful questions. We address both points as follows:\n\n1). **Attentive neighbor aggregation vs. GANA’s gated and attentive aggregator:** Our attentive neighbor aggregation module is designed as a lightweight mechanism to incorporate local entity context, using a simple attention scheme over one-hop neighbors. This component is not the focus of our work; thus, we intentionally adopt a simple design to ensure that improvements stem from our core contributions (i.e., the MoE-based meta-knowledge and task-specific adaptation). In contrast, GANA's main contribution lies in designing a more sophisticated gated and attentive aggregation module with multiple gating and transformation layers to better handle sparse neighbours. We do not use the gated structure or GANA’s architecture in our implementation.\n   \n2). **Task-Tailored Local Adaptation vs. TransD:** Our task-specific adaptation mechanism is inspired by TransD in using projections to model the interaction between entities and relations. However, there is a key difference in how these projection vectors are treated:\n   - In TransD, the projection vectors are learned globally as part of the shared model parameters, and remain fixed across tasks or relations once trained.\n   - In contrast, our projection vectors are randomly initialized for each task and optimized only within that task, allowing for more flexible and task-specific adaptation in the few-shot setting. This design enables MoEMeta to better capture task-level semantics without relying on global sharing, which may not generalize well to unseen relations.\n\n> A reasonable comparison process between MoEMeta and the latest methods is required.\n\nThank you for your comment. We would like to clarify that RelAdapter is a recent 2024 method, and we have included comparative results with it on both FB15K-One and Wiki-One, as shown in Table 2 and Table 3. As RelAdapter does not report results on NELL-One, we compare MoEMeta against the strongest available baseline on NELL-One, HiRe. Despite being a 2023 method, HiRe remains a competitive baseline in meta-learning based FSRL literature, especially on NELL-One.\n\nWe acknowledge that meta-learning based FSRL has seen limited updates in recent years. We will continue to track and include future methods as they become available, and are prepared to include additional comparisons in an updated version of the paper if new implementations or results are released.\n\n> Marginal improvements in \"Hits@X\" on the Wiki-one dataset.\n\nThank you for valuable suggestion. We agree that the improvements of MoEMeta on the Wiki-One dataset are less pronounced compared to NELL-One. A key reason lies in the nature of relation representations in Wiki-One: In Wiki-One, relations are represented using non-semantic identifiers that lack natural language descriptions. In contrast, NELL-One uses textual relation names, which often exhibit explicit semantic regularities and correlations. Since MoEMeta is designed to model and leverage commonalities across relations through expert sharing, its advantage is more evident when relation semantics can be meaningfully captured. We will add this analysis to the revised version to clarify the differences across datasets."}]

Author rebuttal:

Meta-review: This paper introduces a meta-learning approach for few-shot relational learning that incorporates task-specific modulation in both meta-knowledge representation and task-level adaptation. For meta-knowledge, entities are represented by aggregating neighborhood information and these representations are used to gate a mixture-of-experts (MoE) module for relation prediction given a support set. For task-specific adaptation, the method learns local projection functions for head and tail entity embeddings as well as relation embeddings. All meta-parameters are optimized via a MAML-style algorithm. Experiments on standard benchmark datasets show improved performance from the proposed method.

After discussion, all reviewers recommended acceptance, though some limitations and concerns remain. The use of MoE is an interesting contribution though the general approaches adopted in this work has been widely used (e.g., task-specific modulation).
Decision: Accept (poster)