arXivPaperNeeds Review

OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques

A two-stage ensemble reconciles outputs from string, graph-embedding, and RAG-based ontology aligners. Composition matters: mixed paradigms favor precision, while LLM-only groups more often favor F1.

arXiv

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Source Summary

OntoAligner-Ensemble applies **voting-based fusion** and then post-fusion selection to candidate correspondences from string, KGE, and RAG-based aligners. It was evaluated on **eight tasks across five OAEI tracks**.

Practical Implication

For pipelines that reconcile structured data, treat ensemble composition as a tunable policy. Cross-paradigm groups generally favored precision, while **homogeneous LLM ensembles** more often delivered higher overall F1.

Agent-Ready Context

OntoAligner-Ensemble applies **voting-based fusion** and then post-fusion selection to candidate correspondences from string, KGE, and RAG-based aligners. It was evaluated on **eight tasks across five OAEI tracks**.

For pipelines that reconcile structured data, treat ensemble composition as a tunable policy. Cross-paradigm groups generally favored precision, while **homogeneous LLM ensembles** more often delivered higher overall F1.

The material provides no per-task scores, costs, or latency results. The findings are specific to ontology alignment, so their value for general coding-agent orchestration remains untested.

Context Map

agentdataresearch#harness-engineering#multi-agent

Uncertainty

The material provides no per-task scores, costs, or latency results. The findings are specific to ontology alignment, so their value for general coding-agent orchestration remains untested.