NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs

TL;DR

This paper introduces NQE, a novel model for complex query answering over hyper-relational knowledge graphs that handles n-ary facts and diverse logical queries with high flexibility and state-of-the-art performance.

Contribution

The paper proposes a new N-ary Query Embedding (NQE) model utilizing a dual-heterogeneous Transformer and fuzzy logic for flexible, comprehensive CQA over hyper-relational KGs, including a new dataset.

Findings

NQE achieves state-of-the-art results on WD50K-NFOL and other datasets.

The model effectively handles diverse n-ary FOL queries.

The parallel processing algorithm improves training and prediction efficiency.

Abstract

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.