Fuzzy Ontology Embeddings and Visual Query Building for Ontology Exploration

TL;DR

FuzzyVis is a system that combines fuzzy logic-based ontology embeddings with a visual interface to facilitate intuitive, flexible, and approximate exploration of complex ontologies, especially in large domains.

Contribution

It introduces a novel fuzzy embedding approach integrated with an interactive visual tool for ontology exploration, improving accessibility and expressiveness over traditional methods.

Findings

Supports approximate, concept-level similarity search

Enables intuitive query building without formal syntax

Assists users in uncovering relevant concepts in large ontologies

Abstract

Ontologies play a central role in structuring knowledge across domains, supporting tasks such as reasoning, data integration, and semantic search. However, their large size and complexity, particularly in fields such as biomedicine, computational biology, law, and engineering, make them difficult for non-experts to navigate. Formal query languages such as SPARQL offer expressive access but require users to understand the ontology's structure and syntax. In contrast, visual exploration tools and basic keyword-based search interfaces are easier to use but often lack flexibility and expressiveness. We introduce FuzzyVis, a proof-of-concept system that enables intuitive and expressive exploration of complex ontologies. FuzzyVis integrates two key components: a fuzzy logic-based querying model built on fuzzy ontology embeddings, and an interactive visual interface for building and interpreting queries. Users can construct new composite concepts by selecting and combining existing ontology concepts using logical operators such as conjunction, disjunction, and negation. These composite concepts are matched against the ontology using fuzzy membership-based embeddings, which capture degrees of membership and support approximate, concept-level similarity search. The visual interface supports browsing, query composition, and partial search without requiring formal syntax. By combining fuzzy semantics with embedding-based reasoning, FuzzyVis enables flexible interpretation, efficient computation, and exploratory learning. Case studies demonstrate how FuzzyVis supports subtle information needs and helps users uncover relevant concepts in large, complex ontologies.