NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
DOI:
https://doi.org/10.1609/aaai.v37i4.25576Keywords:
DMKM: Linked Open Data, Knowledge Graphs & KB Completion, DMKM: Graph Mining, Social Network Analysis & Community Mining, DMKM: Semantic Web, KRR: Automated Reasoning and Theorem Proving, KRR: Common-Sense Reasoning, KRR: Computational Complexity of Reasoning, KRR: Knowledge Engineering, KRR: Knowledge Representation Languages, KRR: Other Foundations of Knowledge Representation & Reasoning, SNLP: Interpretability & Analysis of NLP Models, SNLP: Question AnsweringAbstract
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.Downloads
Published
2023-06-26
How to Cite
Luo, H., E, H., Yang, Y., Zhou, G., Guo, Y., Yao, T., Tang, Z., Lin, X., & Wan, K. (2023). NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 4543-4551. https://doi.org/10.1609/aaai.v37i4.25576
Issue
Section
AAAI Technical Track on Data Mining and Knowledge Management