Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, Mahongxia, Yanghua Xiao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Open Knowledge Bases (Open KBs) are fundamental to knowledge-driven applications, including semantic search, knowledge reasoning, and recommendation systems. However, the presence of redundant and ambiguous expressions within Open KBs significantly hinders their application. This highlights the urgent need for Open KB canonicalization, particularly of attribute values, which comprise nearly 40% of the facts within Open KBs. Unlike entities and predicates, attribute values are inherently sparse and diverse, posing unique challenges for their canonicalization. However, existing studies mainly focus on entities or predicates, leaving attribute value-level noun phrase canonicalization (NPC-AV) underexplored. Large language models (LLMs), with their strengths in common-sense reasoning and fault tolerance, have shown promise in Open KB canonicalization. Yet, current LLM-based approaches often rely heavily on LLM responses, overlooking their high computational cost and potential errors. In this paper, we introduce Caf4AVC, a collaborative framework that integrates clustering-based methods and LLMs for the NPC-AV task. We further propose an innovative two-factor authentication correction mechanism and an adaptive threshold-based selection strategy to address these limitations. Extensive experiments on multiple real-world Open KB datasets demonstrate the effectiveness of our framework, achieving a 17.52% reduction in LLM call costs and a 6.3% average performance improvement compared to competitive methods. The code and dataset are available athttps://github.com/hedyHe/Caf4AV.

Semantic drift is a common problem in iterative information extraction. Previous approaches for minimizing semantic drift may incur substantial loss in recall. We observe that most semantic drifts are introduced by a small number of questionable extractions in the earlier rounds of iterations. These extractions subsequently introduce a large number of questionable results, which lead to the semantic drift phenomenon. We call these questionable extractions Drifting Points (DPs). If erroneous extractions are the “symptoms” of semantic drift, then DPs are the “causes” of semantic drift. In this paper, we propose a method to minimize semantic drift by identifying the DPs and removing the effect introduced by the DPs. We use isA (concept-instance) extraction as an example to describe our approach in cleaning information extraction errors caused by semantic drift, but we perform experiments on different relation extraction processes on three large real data extraction collections. The experimental results show that our DP cleaning method enables us to clean around 90 percent incorrect instances or patterns with about 90 percent precision, which outperforms the previous approaches we compare with.