用分块推理提升分类体系扩展的准确率和效率
Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion
- 先排序后分块，逐批筛选候选词并迭代优化
- 在四个基准上准确率提升12%，相似度指标提高5%
- 适合需要高效扩展知识分类体系的推荐系统应用
分类体系是推荐系统与网络应用中至关重要的层次化知识图谱。随着数据增长，分类体系的扩展至关重要，但现有方法面临两大挑战：(1) 判别式模型受限于表征能力与泛化性能；(2) 生成式方法或一次性处理全部候选词导致噪声引入与上下文超限，或因选择噪声候选而丢弃相关实体。本文提出LORex（Lineage-Oriented Reasoning for Taxonomy Expansion），一个即插即用的框架，融合判别式排序与生成式推理，实现高效分类体系扩展。不同于以往方法，LORex将候选词按优先级排序并分块处理，通过推理其层级关系迭代优化选择，有效过滤噪声并保障上下文效率。在四个基准与十二个基线上的广泛实验表明，相较于最先进方法，LORex在准确率上提升12%，在Wu & Palmer相似度上提升5%。
原文摘要 · Abstract (English)
Taxonomies are hierarchical knowledge graphs crucial for recommendation systems, and web applications. As data grows, expanding taxonomies is essential, but existing methods face key challenges: (1) discriminative models struggle with representation limits and generalization, while (2) generative methods either process all candidates at once, introducing noise and exceeding context limits, or discard relevant entities by selecting noisy candidates. We propose LORex (Lineage-Oriented Reasoning for Taxonomy Expansion), a plug-and-play framework that combines discriminative ranking and generative reasoning for efficient taxonomy expansion. Unlike prior methods, LORex ranks and chunks candidate terms into batches, filtering noise and iteratively refining selections by reasoning candidates' hierarchy to ensure contextual efficiency. Extensive experiments across four benchmarks and twelve baselines show that LORex improves accuracy by 12% and Wu & Palmer similarity by 5% over state-of-the-art methods.
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