Fuzzy Ontology Embeddings and Visual Query Building for Ontology Exploration

2025-08-11

AI Summary

Non-expert users face significant challenges in browsing, understanding, and querying large, complex ontologies—particularly in domains such as biomedicine and law. To address this, we propose a semantic exploration method integrating fuzzy logic with knowledge graph embedding. Our approach features: (1) a fuzzy-membership-based ontology embedding model enabling concept-level semantic approximate matching; (2) an interactive, visual query interface that supports natural composition of logical operators (conjunction, disjunction, negation), eliminating the need for SPARQL expertise or prior knowledge of ontology structure; and (3) end-to-end semantic similarity inference and exploratory learning. Experimental evaluation demonstrates substantial improvements in fine-grained concept discovery and ontology accessibility, achieving high expressivity while significantly lowering the usability barrier for non-specialist users.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Semantic Web
• Search and Optimization: Heuristic Search

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Enables intuitive exploration of complex ontologies for non-experts
• Combines fuzzy logic and visual interface for flexible query building
• Supports approximate concept-level similarity search without formal syntax

Innovation

• Fuzzy logic-based querying model
• Interactive visual query interface
• Fuzzy membership-based embeddings