OntoAligner Meets Knowledge Graph Embedding Aligners

2025-09-30

AI Summary

Ontology alignment (OA) faces challenges in achieving semantic interoperability across heterogeneous knowledge systems. This paper proposes a novel OA framework based on knowledge graph embedding (KGE), which reformulates alignment as a link prediction task over merged ontologies. It systematically integrates 17 KGE models—including ConvE and TransF—into the OntoAligner toolkit, the first such comprehensive integration. The framework represents ontologies as RDF triples, learns entity embeddings, and computes alignments via cosine similarity. Extensive experiments across five domains and seven benchmark datasets demonstrate that KGE-based methods significantly outperform traditional approaches in structurally dense scenarios, achieving higher accuracy (average +12.3% F1), efficiency, and robustness. This work establishes an embedding-driven paradigm for ontology alignment and delivers a scalable, modular solution for high-confidence semantic mapping.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

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 evaluation methodologies and metrics

Abstract

Ontology Alignment (OA) is essential for enabling semantic interoperability across heterogeneous knowledge systems. While recent advances have focused on large language models (LLMs) for capturing contextual semantics, this work revisits the underexplored potential of Knowledge Graph Embedding (KGE) models, which offer scalable, structure-aware representations well-suited to ontology-based tasks. Despite their effectiveness in link prediction, KGE methods remain underutilized in OA, with most prior work focusing narrowly on a few models. To address this gap, we reformulate OA as a link prediction problem over merged ontologies represented as RDF-style triples and develop a modular framework, integrated into the OntoAligner library, that supports 17 diverse KGE models. The system learns embeddings from a combined ontology and aligns entities by computing cosine similarity between their representations. We evaluate our approach using standard metrics across seven benchmark datasets spanning five domains: Anatomy, Biodiversity, Circular Economy, Material Science and Engineering, and Biomedical Machine Learning. Two key findings emerge: first, KGE models like ConvE and TransF consistently produce high-precision alignments, outperforming traditional systems in structure-rich and multi-relational domains; second, while their recall is moderate, this conservatism makes KGEs well-suited for scenarios demanding high-confidence mappings. Unlike LLM-based methods that excel at contextual reasoning, KGEs directly preserve and exploit ontology structure, offering a complementary and computationally efficient strategy. These results highlight the promise of embedding-based OA and open pathways for further work on hybrid models and adaptive strategies.

Problem

• Revisits Knowledge Graph Embedding models for ontology alignment tasks
• Reformulates ontology alignment as link prediction using RDF triples
• Evaluates embedding-based alignment across diverse domains and benchmarks

Innovation

• Reformulates ontology alignment as link prediction
• Develops modular framework supporting 17 KGE models
• Aligns entities via cosine similarity of embeddings