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

• URL: http://arxiv.org/abs/2211.13469v3
• Date: Fri, 31 Mar 2023 21:54:52 GMT
• Title: NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
• Authors: Haoran Luo, Haihong E, Yuhao Yang, Gengxian Zhou, Yikai Guo, Tianyu Yao, Zichen Tang, Xueyuan Lin, Kaiyang Wan
• Abstract summary: Complex query answering is an essential task for logical reasoning on knowledge graphs. We propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs) NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries. We generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K.
• Score: 1.415350927301928
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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