基于双视图对比学习与子模优化的实体规范化方法OA

Entity redundancy,where multiple nodes represent the same real-world entity due to heterogeneous data sources or extraction errors,severely affects the quality and utility of Knowledge Graphs(KG).To address the problem of Entity Canonicalization(EC)within a single knowledge graph,we propose a two-stage method whose core innovations are threefold.1)We propose a Contrastive Representation-Guided Clustering(CRGC)method that performs contrastive learning by leveraging the dual-view information(context and definition)of entities and adaptively cuts the hierarchical clustering results using the Minimum Description Length(MDL)principle,thereby avoiding the need for manual threshold setting.2)We design a Submodular Redundancy Minimization(SRM)algorithm that formulates the representative entity selection problem as a submodular coverage maximization under partition matroid constraints.This method,denoted as CRGC-SRM,provides an approximation guarantee while explicitly optimizing the trade-off between the Knowledge Coverage Rate(KCR)and redundancy.3)Tailored for the EC task,we introduce a type-consistency penalty and a hard-negative mining strategy to effectively suppress the"over-merging"problem caused by homographic(or polysemous)entities.Experiments on multiple public and internal datasets demonstrate that CRGC-SRM improves clustering quality by approximately 2.7 percentage points over the strongest baselines,subsequently reducing the Entity Redundancy Rate(ERR)from 29.7％to 7.8％on average(reducing redundancy by 73.7％relative to that of the original graph)while maintaining ≥98％KCR.Furthermore,CRGC-SRM significantly improves query performance,increasing the Mean Reciprocal Rank(MRR)by approximately 15.4％,Hits@1 by approximately 18.5％,and reducing the 95th Percentile(P95)query latency by 27.7％-35.9％.CRGC-SRM offers an efficient,theoretically grounded,and practical solution for single-graph EC.