A Novel Retrieve-Read-Group Paradigm for Open Knowledge Base Canonicalization - Yang, Binhan; Shen, Wei; Tian, Han (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Noun phrases (NPs) in open knowledge bases (OKBs) are not canonicalized, leading to scattered knowledge that necessitates the exploration of the OKB canonicalization task (i.e., clustering synonymous noun phrases into the same group and assigning them a unique identifier). However, existing OKB canonicalization methods typically adhere to a traditional embedding-centered pipeline, which fails to exploit the direct interaction between NPs for pairwise NP similarity calculations, resulting in suboptimal performance and instead relying extensively on external resources. To address these limitations, we introduce a groundbreaking retrieve-read-group paradigm that enables fine-grained pairwise NP similarity calculations by effectively leveraging the direct NP interaction via the reading stage, thereby relieving the reliance on external resources. As an instantiation of this paradigm, we propose DUVK, a novel self-supervised framework that fully integrates the dual-view knowledge involved in OKBs from the relational view and the semantic view. In the retriever component of DUVK, a dual-view cross-training strategy is designed to make two view-specific encoders mutually reinforce each other by capitalizing on the complementary knowledge delivered from both views. Experimental results demonstrate that, even without the need of any external resources, DUVK outperforms all state-of-the-art competitors that rely on such resources.