用模糊集体积逼近法，高效建模概念关系，提升分类体系扩展效果
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion
- 将集合表示为模糊集的体积近似，满足所有集合运算
- 在分类体系扩展任务中性能提升最高达23%
- 首次实现模糊集嵌入的高效计算，适合知识图谱构建
分类体系扩展可建模为集合表示学习任务。传统集合泛化为模糊集能刻画不确定性并度量语义概念内的信息，更适用于复杂概念建模。现有方法多将集合表示为向量或几何体（如盒子），但不满足集合运算封闭性。本文提出基于模糊集体积近似的集合表示学习新范式，得到的嵌入框架FUSE（Fuzzy Set Embedding）满足所有集合运算，紧凑逼近底层模糊集，在保持信息完整的同时具备高效学习能力，仅需最小神经架构。实验证明，FUSE在分类体系扩展任务中相比基线模型性能提升最高达23%。本工作首次系统探索并高效计算模糊集嵌入。
原文摘要 · Abstract (English)
Taxonomy Expansion, which models complex concepts and their relations, can be formulated as a set representation learning task. The generalization of set, fuzzy set, incorporates uncertainty and measures the information within a semantic concept, making it suitable for concept modeling. Existing works usually model sets as vectors or geometric objects such as boxes, which are not closed under set operations. In this work, we propose a sound and efficient formulation of set representation learning based on its volume approximation as a fuzzy set. The resulting embedding framework, Fuzzy Set Embedding (FUSE), satisfies all set operations and compactly approximates the underlying fuzzy set, hence preserving information while being efficient to learn, relying on minimum neural architecture. We empirically demonstrate the power of FUSE on the task of taxonomy expansion, where FUSE achieves remarkable improvements up to 23% compared with existing baselines. Our work marks the first attempt to understand and efficiently compute the embeddings of fuzzy sets.
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