Leveraging Max-Pooling Aggregation and Enhanced Entity Embeddings for Few-Shot Knowledge Graph Completion

Meng Zhang () and Wonjun Chung
Additional contact information
Meng Zhang: College of Architecture and Design, Tongmyong University, Busan 48520, Republic of Korea
Wonjun Chung: College of Architecture and Design, Tongmyong University, Busan 48520, Republic of Korea

Mathematics, 2025, vol. 13, issue 21, 1-17

Abstract: Few-shot knowledge graph (KG) completion is challenged by the dynamic and long-tail nature of real-world KGs, where only a handful of relation-specific triples are available for each new relation. Existing methods often over-rely on neighbor information and use sequential LSTM aggregators that impose an inappropriate order bias on inherently unordered triples. To address these limitations, we propose a lightweight yet principled framework that (1) enhances entity representations by explicitly integrating intrinsic (self) features with attention-aggregated neighbor context, and (2) introduces a permutation-invariant max-pooling aggregator to replace the LSTM-based reference set encoder. This design faithfully respects the set-based nature of triples while preserving critical entity semantics. Extensive experiments on the standard few-shot KG completion benchmarks NELL-One and Wiki-One demonstrate that our method consistently outperforms strong baselines, including non-LSTM models such as MetaR, and delivers robust gains across multiple evaluation metrics. These results show that carefully tailored, task-aligned refinements can achieve significant improvements without increasing model complexity.

Keywords: knowledge graph completion; few-shot learning; entity embedding; attention (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/21/3486/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/21/3486/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:21:p:3486-:d:1785057

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().