Work

HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs

Year: 2025

Type: conference-paper

Abstract: With the ubiquity of hyper-relational facts in modern Knowledge Graphs (KGs), existing link prediction techniques mostly focus on learning the sophisticated relationships among multiple entities and relations contained in a fact, while ignoring the multimodal information, which often provides additional clues to boost link prediction performance.Nevertheless, traditional multimodal fusion approaches, which are mainly designed for triple facts under either entity-centric or relation-guided fusion schemes, fail to integrate the multimodal information with the rich context of the hyperrelational fact consisting of multiple entities and relations.Against this background, we propose HyperFM, a Hyper-relational Factcentric Multimodal Fusion technique.It effectively captures the intricate interactions between different data modalities while accommodating the hyper-relational structure of the KG in a fact-centric manner via a customized Hypergraph Transformer.We evaluate Hy-perFM against a sizeable collection of baselines in link prediction tasks on two real-world KG datasets.The results show that HyperFM consistently achieves the best performance, yielding an average improvement of 6.0-6.8% over the best-performing baselines on the two datasets.Moreover, a series of ablation studies systematically validate our fact-centric fusion scheme.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2025.acl-long.142

Authors: Yuhuan Lu, Weijian Yu, Xin Jing, Dingqi Yang

Institution: University of Macau

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Universidade de Macau

Award: G5622060990