Complex query answering (CQA) is an essential task for multi-hop and logical
reasoning on knowledge graphs (KGs). Currently, most approaches are limited to
queries among binary relational facts and pay less attention to n-ary facts
(n>=2) containing more than two entities, which are more prevalent in the real
world. Moreover, previous CQA methods can only make predictions for a few given
types of queries and cannot be flexibly extended to more complex logical
queries, which significantly limits their applications. To overcome these
challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model
for CQA over hyper-relational knowledge graphs (HKGs), which include massive
n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and
fuzzy logic theory to satisfy all n-ary FOL queries, including existential
quantifiers, conjunction, disjunction, and negation. We also propose a parallel
processing algorithm that can train or predict arbitrary n-ary FOL queries in a
single batch, regardless of the kind of each query, with good flexibility and
extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including
diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and
other standard CQA datasets show that NQE is the state-of-the-art CQA method
over HKGs with good generalization capability. Our code and dataset are
publicly available.

本文提出了一种新的 n 元查询嵌入模型（NQE）用于超关系知识图上的复杂查询答案，包括存在量化器、合取、析取和否定，并提出一种新的 CQA 数据集（WD50K-NFOL），实验证明其是超关系知识图上最先进的 CQA 方法。