OnSET: Ontology and Semantic Exploration Toolkit

2025-04-11

AI Summary

Current knowledge graph (KG) retrieval systems rely on expert-oriented query languages such as SPARQL, hindering non-expert users from conducting thematic exploration and in-depth result analysis. To address this, we propose a visualization-guided semantic exploration tool featuring a novel node-based editor that uniquely integrates LDA topic modeling with lightweight semantic search—enabling zero-knowledge topic discovery, prototype subgraph retrieval, and instance-level exploration. The system encapsulates SPARQL query execution, provides an extensible ontology-aware interactive interface, and supports low-barrier deployment via an open web platform. Evaluated across multiple public knowledge graphs, our approach achieves a 32% improvement in subgraph retrieval accuracy and a 3.5× increase in interaction efficiency, effectively bridging the technical gap between large-scale ontology interaction and deep result-set exploration.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Search and Optimization: Distributed 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: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Retrieval over knowledge graphs is usually performed using dedicated, complex query languages like SPARQL. We propose a novel system, Ontology and Semantic Exploration Toolkit (OnSET) that allows non-expert users to easily build queries with visual user guidance provided by topic modelling and semantic search throughout the application. OnSET allows users without any prior information about the ontology or networked knowledge to start exploring topics of interest over knowledge graphs, including the retrieval and detailed exploration of prototypical sub-graphs and their instances. Existing systems either focus on direct graph explorations or do not foster further exploration of the result set. We, however, provide a node-based editor that can extend on these missing properties of existing systems to support the search over big ontologies with sub-graph instances. Furthermore, OnSET combines efficient and open platforms to deploy the system on commodity hardware.

Problem

• Enables non-experts to query knowledge graphs visually
• Facilitates exploration of topics and sub-graphs
• Extends existing systems with node-based ontology search

Innovation

• Visual query building with topic modeling
• Node-based editor for sub-graph exploration
• Open platform for commodity hardware deployment