HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level

• URL: http://arxiv.org/abs/2305.06588v2
• Date: Mon, 15 May 2023 16:25:03 GMT
• Title: HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level
• Authors: Haoran Luo, Haihong E, Yuhao Yang, Yikai Guo, Mingzhi Sun, Tianyu Yao, Zichen Tang, Kaiyang Wan, Meina Song, Wei Lin
• Abstract summary: Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. We propose a novel Hierarchical Attention model for HKG Embedding (HAHE), including global-level and local-level attention. Experiment results indicate that HAHE achieves state-of-the-art performance in link prediction tasks on HKG standard datasets.
• Score: 7.96433065992062
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. HKG consists of hyper-relational facts (H-Facts), composed of a main triple and several auxiliary attribute-value qualifiers, which can effectively represent factually comprehensive information. The internal structure of HKG can be represented as a hypergraph-based representation globally and a semantic sequence-based representation locally. However, existing research seldom simultaneously models the graphical and sequential structure of HKGs, limiting HKGs' representation. To overcome this limitation, we propose a novel Hierarchical Attention model for HKG Embedding (HAHE), including global-level and local-level attention. The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers. Experiment results indicate that HAHE achieves state-of-the-art performance in link prediction tasks on HKG standard datasets. In addition, HAHE addresses the issue of HKG multi-position prediction for the first time, increasing the applicability of the HKG link prediction task. Our code is publicly available.

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