OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques
2026-08-31 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors introduce OntoAligner-Ensemble, a flexible system that combines different methods for matching concepts between ontologies to improve accuracy. Their approach merges results from various matchers, including simple string-based ones, knowledge graph embeddings, and language models, using a two-step voting and selection process. Testing on multiple benchmark tasks, they show that combining these methods usually leads to better balance between finding correct matches and avoiding wrong ones than using any single method alone. They also find that mixing different types of matchers improves precision, while using only language model-based matchers tends to boost overall accuracy.
Ontology alignmentKnowledge graph embeddingLarge language modelsEnsemble learningPrecisionRecallF1-scoreVoting-based fusionPost-fusion selectionOAEI benchmarks
Authors
Hamed Babaei Giglou, Sören Auer, Peio Popov, Mahsa Sanaei, Jennifer D'Souza
Abstract
Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding (KGE) models and, more recently, Large Language Model (LLM)-based approaches. Although modern OA frameworks provide unified ecosystems for deploying these heterogeneous aligners, mechanisms for systematically reconciling their complementary and sometimes conflicting predictions remain relatively underexplored. We present OntoAligner-Ensemble, a modular and aligner-agnostic framework that combines candidate correspondences through a configurable two-stage process comprising voting-based fusion strategies followed by post-fusion selection policies. The framework supports any aligner implemented within OntoAligner that produces candidate correspondences, enabling diverse alignment paradigms to be integrated through a unified decision process. To demonstrate its effectiveness, we instantiate the framework using representative lightweight string-aligner, KGE-based, and Retrieval-Augmented Generation aligners powered by both open-weight and API-based LLMs. We evaluate individual aligners and ensemble configurations across eight benchmark tasks from five OAEI tracks spanning biomedical to beyond-equivalence. The results show that ensemble fusion consistently improves the balance between precision and recall and frequently outperforms standalone aligners across diverse domains. Furthermore, our analysis reveals that ensemble composition directly affects the precision-recall trade-off: heterogeneous cross-paradigm ensembles generally improve precision, whereas homogeneous LLM ensembles more often achieve higher overall F1-scores. These findings demonstrate that systematic ensemble learning offers a robust and reproducible strategy for OA while providing practical guidance for selecting ensemble compositions under different alignment scenarios.