OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques
[Sztuczna inteligencja] Autorzy: Hamed Babaei Giglou, Sören Auer, Peio Popov i 2 więcej
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 proces…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja