提出新模型LKHGT，用Transformer解决知识超图的复杂查询问题。
Transformers for Complex Query Answering over Knowledge Hypergraphs
- 分两阶段设计：先投影后逻辑推理，用类型感知偏置捕捉实体交互
- 在两个新数据集上达到当前最好性能，还能泛化到未见过的查询类型
- 适合研究知识图谱推理、逻辑查询和多关系建模的学者
复杂查询回答（CQA）近年来受到广泛关注。为更贴近真实世界分布，引入了包含多种模态的知识图谱。传统三元组知识图谱（Triple KGs）仅由二元关系构成，难以刻画现实世界中复杂事实。尽管已提出超关系图谱，但对不同阶数关系且各实体贡献均等的情形仍存在表示局限。为此，我们构建了两个新数据集：JF17k-HCQA 和 M-FB15k-HCQA，涵盖投影、否定、合取、析取等多样查询类型。针对知识超图（KHG）中的存在性一阶逻辑查询，提出两阶段Transformer模型——逻辑知识超图变换器（LKHGT），包括用于原子投影的投影编码器和用于复杂逻辑操作的逻辑编码器，两者均引入类型感知偏置（TAB）以捕捉标记间交互。在多个CQA数据集上的实验表明，LKHGT是当前在知识超图上表现最优的复杂查询回答方法，且能有效泛化至分布外查询类型。
原文摘要 · Abstract (English)
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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