FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion

TL;DR

FUSE introduces a measure-theoretic fuzzy set embedding framework for taxonomy expansion, enabling efficient and accurate modeling of complex concepts and their relations by preserving set operations and information.

Contribution

This work presents the first efficient fuzzy set embedding method based on volume approximation, improving taxonomy expansion performance significantly.

Findings

FUSE achieves up to 23% improvement over baselines.

FUSE effectively models set operations and preserves information.

First to efficiently compute fuzzy set embeddings.

Abstract

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.