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

Forum topic · ✨步子哥 · 2025-11-27

Summary

This post explains MAYPL (Message pAssing framework for hYper-relational knowledge graph rePresentation Learning), a method presented by Lee & Whang at ICML 2025 for learning representations on hyper-relational knowledge graphs (HKGs). Unlike prior approaches that convert HKG facts into N-ary relations or knowledge hypergraphs—causing information loss—or treat facts as independent sequences, MAYPL is purely structure-driven: it initializes entity and relation representations solely from their structural roles, co-occurrence, and positions within facts, then refines them via an attention-based neural message-passing framework that jointly updates entities and relations. Because it learns structural patterns rather than per-entity embeddings, MAYPL generalizes to unseen entities and relations without retraining (inductive inference). Experiments across 10 benchmarks and 40 baselines show state-of-the-art results: on WD50K and WikiPeople' transductive link prediction it achieves MRR of 0.381 and 0.519, and on challenging inductive datasets (NL-50, WK-50, FB-50) it substantially outperforms InGram and HyRel. Ablations confirm each component's contribution, and case studies on Oscar Best Director queries demonstrate effective use of qualifiers. The post also discusses applications in QA, scientific discovery, and recommender systems, plus ethical considerations.

Key points

Problem: Traditional knowledge graphs store simple triples; hyper-relational knowledge graphs (HKGs) enrich each main triple with qualifiers (e.g., ((Finding Nemo, setting, Sydney), {(country, Australia), (state, NSW)})). Existing methods either convert HKGs to N-ary relations or knowledge hypergraphs (losing information), process facts as independent sequences (Transformer models like StarE, HyNT), or use shallow neighborhood aggregation / GNNs (e.g., HAHE) that ignore positional and global structural cues.
MAYPL's core idea — "structure is all you need": Instead of learning independent embeddings per entity/relation, MAYPL derives representations purely from structure: how entities and relations connect, co-occur, and are positioned (head, tail, or qualifier) within facts.
Architecture: A structure-driven initializer aggregates representations of entities/relations co-occurring in the same fact, respecting their positions; followed by attention-based neural message passing with three steps: (1) fact-level message computation, (2) attention-weighted entity updates, and (3) attention-weighted relation updates.
Inductive capability: Because it learns structural aggregation patterns rather than memorizing entity representations, MAYPL handles entirely new entities and relations without retraining.

Experimental results

Evaluated on 10 benchmark datasets against 40 baselines across transductive and inductive link prediction on HKGs and KGs.
Transductive link prediction (main triple entities):

| Dataset | Metric | StarE | HAHE | HyNT | MAYPL | |---------|--------|-------|------|------|-------| | WD50K | MRR | 0.349 | 0.368 | 0.357 | 0.381 | | WD50K | Hit10 | 0.496 | 0.516 | 0.501 | 0.544 | | WikiPeople' | MRR | 0.491 | 0.509 | - | 0.519 | | WikiPeople' | Hit10 | 0.648 | 0.639 | - | 0.657 |

Inductive link prediction on NL-50, WK-50, FB-50 (all entities unseen, half of triples contain new relations): MAYPL achieves MRR of 0.343, 0.109, and 0.205, clearly beating baselines like InGram (0.281 on NL-50) and HyRel (0.321 on NL-50).
Ablation studies confirm every component matters: removing the structural initializer drops WikiPeople' MRR from 0.521 to 0.388; removing message passing drops it to 0.109; removing attention or simplifying relation updates also degrades performance.

Case studies

Given ((?, won, Academy Award for Best Director), {(edition, 60th), (work, The Last Emperor)}), MAYPL correctly predicts Bernardo Bertolucci; with qualifiers changed to the film *A Beautiful Mind*, it correctly predicts Ron Howard — showing genuine use of qualifier context.
Representation-space analysis shows message passing refines semantics: "Vancouver" evolves from neighbors like Venice/Budapest (generic cities) to Toronto/Victoria/Ottawa (Canadian cities).

Implications

Supports the thesis that connection patterns, positions, and structure carry the deep semantics of knowledge — echoing cognitive science's structure-mapping theory.
Promising applications: knowledge-driven QA with evolving entities, scientific discovery support, and recommendation in dynamic environments.
The authors caution that strong knowledge-completion ability could be misused to infer private information and stress ethical safeguards.

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: Heterogeneous Graph Transformer for Knowledge Graph Completion. *arXiv preprint arXiv:2005.12861*. 3. Luo, Z., et al. (2023). HAHE: Hyper-relational Attribute and Hypergraph Edge Transformer for Knowledge Graph Completion. *arXiv preprint arXiv:2305.01544*. 4. Chung, J., et al. (2023). HyNT: A Hyper-Relational Transformer for Knowledge Graph Completion. *arXiv preprint arXiv:2302.08912*. 5. Lee, J., et al. (2023b). InGram: Inductive Knowledge Graph Completion via Subgraph Reasoning. *Proceedings of the 39th International Conference on Machine Learning*.