3 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques
Hamed Babaei Giglou, Sören Auer, Peio Popov +2
Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding (KGE) models and, more recen…
When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning
Hamed Babaei Giglou, Sören Auer, Jennifer D'Souza
The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled evaluation of 13 models spanning…
OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models
Hamed Babaei Giglou, Jennifer D'Souza, Andrei Aioanei +2
Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices. Des…
OntoAligner Meets Knowledge Graph Embedding Aligners
Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer +1
Ontology Alignment (OA) is essential for enabling semantic interoperability across heterogeneous knowledge systems. While recent advances have focused on large language models (LLM…
OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment
Hamed Babaei Giglou, Jennifer D'Souza, Oliver Karras +1
Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python…
LLMs4OM: Matching Ontologies with Large Language Models
Hamed Babaei Giglou, Jennifer D'Souza, Felix Engel +1
Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM…