Structure Is All You Need: MAYPL Brings Structure-Driven Learning to Hyper-Relational Knowledge Graphs

Forum topic · ✨步子哥 · 2025-11-27

Summary

A KAIST research team introduced MAYPL (Message pAssing framework for hYper-relational knowledge graph rePresentation Learning), a fully structure-based representation learning framework for hyper-relational knowledge graphs (HKGs), presented in the ICML 2025 paper "Structure Is All You Need". Unlike Transformer-based methods (StarE, HyNT) or GNN approaches (HAHE) that only partially exploit HKG structure, MAYPL uses a structure-driven initializer that computes entity and relation embeddings purely from graph topology, followed by attention-based neural message passing across fact, entity, and relation levels. Evaluated on 10 benchmark datasets against 40 baselines, MAYPL achieves state-of-the-art transductive link prediction (MRR 0.488 on WikiPeople, +8.4% over the best baseline) and dramatic gains in inductive settings (MRR 0.486 vs 0.107 for QBLP on WD20K(100)v1, a 350%+ improvement), demonstrating strong generalization to unseen entities and relations. Ablation studies confirm the necessity of structural initialization, message passing, and attention mechanisms. The framework bridges symbolic and neural approaches and points toward structure-based knowledge reasoning foundation models.

Structure Is All You Need: MAYPL for Hyper-Relational Knowledge Graph Representation Learning

This post introduces MAYPL (Message pAssing framework for hYper-relational knowledge graph rePresentation Learning), proposed by KAIST researchers in the ICML 2025 paper *Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs* (Lee & Whang, 2025).

Background: The Expressiveness Gap in Knowledge Representation

Traditional knowledge graphs store simple triples (subject–relation–object), while hyper-relational knowledge graphs (HKGs) enrich each fact with qualifiers — auxiliary key-value pairs that add context. For example, the fact "Finding Nemo — set in — Sydney" can carry qualifiers such as "country — Australia" and "state — New South Wales". Although HKGs encode knowledge more precisely, existing AI methods underutilize this structural richness:

Transformer-based methods (StarE, HyNT) process facts in isolation, missing cross-fact correlations.
GNN-based methods (HAHE) use neighborhood information but ignore the positional roles of entities and relations within facts.
Simplification approaches convert HKGs to knowledge hypergraphs (KHG) or n-ary relations (NRR), discarding structural links such as shared qualifier relations.

MAYPL: A Fully Structure-Driven Framework

MAYPL operates in three stages:

1. Structure-Driven Initialization

Initial embeddings are computed purely from graph structure — co-occurring entities, connected relations, and their positional roles (head, tail, or qualifier). Formally:

\mathbf{v}_i^{(l)} = \text{AGGR}_{\text{ent}}^{(l)}\left(\{\mathbf{u}_j^{(l-1)} | j \in V_i\}, \{\mathbf{r}_k^{(l-1)} | k \in R_i\}\right)

Because no external features are required, MAYPL naturally handles brand-new entities and relations.

2. Attention-Based Neural Message Passing

Representations are iteratively refined at three levels:

Fact level: which entities/relations compose each fact and their positions
Entity level: which facts an entity belongs to and its role in each
Relation level: which facts a relation participates in and how it connects entities

3. Link Prediction

Missing entities in incomplete HKG facts are predicted by ranking candidate entities by similarity to the query representation, leveraging qualifier context.

Experimental Results

MAYPL was compared against 40 baselines across 10 benchmark datasets:

Transductive link prediction: MRR of 0.488 on WikiPeople (+8.4% over HJE's 0.450); consistent gains on WD50K and other datasets.
Inductive link prediction (unseen entities/relations): MRR of 0.486 on WD20K(100)v1 vs. 0.107 for the best baseline QBLP — an improvement of over 350%.

Ablation Findings

Replacing structural initialization with learnable vectors drops MRR on WikiPeople' from 0.521 to 0.388.
Removing message passing collapses performance to 0.109 on WikiPeople'.
Replacing attention with mean aggregation, or Hadamard products with concatenation, both degrade results.

Qualitative analysis shows that after message passing, similar entities cluster more meaningfully (e.g., "Vancouver" becomes nearest to other Canadian cities like "Toronto" and "Victoria", rather than just any city).

Implications and Limitations

Structure-first learning can outperform heavier external features, challenging the "more data, bigger models" mindset.
Pure structural learning enables strong inductive reasoning, relevant for few-shot and zero-shot settings.
MAYPL bridges symbolic graph logic and connectionist neural representation.
Limitations: computational cost of message passing/attention; future directions include efficiency optimization, applications in QA, generation, and robot navigation, and scaling toward knowledge reasoning foundation models.
The authors note ethical considerations: knowledge completion capabilities could be misused for privacy inference.

References

1. Lee, J., & Whang, J. J. (2025). Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs. *Proceedings of the 42nd International Conference on Machine Learning*. 2. Galkin, M., et al. (2020). StarE. *EMNLP 2020*. 3. Luo, L., et al. (2023). HAHE. *AAAI 2023*. 4. Chung, Y., et al. (2023). HyNT. *EMNLP 2023*. 5. Lee, J., et al. (2023b). InGram. *EMNLP 2023*.