Transformers for Complex Query Answering over Knowledge Hypergraphs

• URL: http://arxiv.org/abs/2504.16537v1
• Date: Wed, 23 Apr 2025 09:07:21 GMT
• Title: Transformers for Complex Query Answering over Knowledge Hypergraphs
• Authors: Hong Ting Tsang, Zihao Wang, Yangqiu Song,
• Abstract summary: Triple KGs, as the classic KGs composed of entities and relations of arity 2, have limited representation of real-world facts.<n>We propose a two-stage transformer model, the Logical Knowledge Hypergraph Transformer (LKHGT), which consists of a Projection for atomic projection and a Logical for complex logical operations.<n> Experimental results on CQA datasets show that LKHGT is a state-of-the-art CQA method over KHG and is able to generalize to out-of-distribution query types.
• Score: 48.55646194244594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modalities have been introduced. Triple KGs, as the classic KGs composed of entities and relations of arity 2, have limited representation of real-world facts. Real-world data is more sophisticated. While hyper-relational graphs have been introduced, there are limitations in representing relationships of varying arity that contain entities with equal contributions. To address this gap, we sampled new CQA datasets: JF17k-HCQA and M-FB15k-HCQA. Each dataset contains various query types that include logical operations such as projection, negation, conjunction, and disjunction. In order to answer knowledge hypergraph (KHG) existential first-order queries, we propose a two-stage transformer model, the Logical Knowledge Hypergraph Transformer (LKHGT), which consists of a Projection Encoder for atomic projection and a Logical Encoder for complex logical operations. Both encoders are equipped with Type Aware Bias (TAB) for capturing token interactions. Experimental results on CQA datasets show that LKHGT is a state-of-the-art CQA method over KHG and is able to generalize to out-of-distribution query types.

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