3 citations · 3 across the 11 of their papers we have counts for
12 papers
Do General NLP Embeddings Capture Ontological Reasoning?
Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer
General-purpose NLP embedding models perform well on linguistic tasks, but their ability to capture symbolic ontological structure remains unclear. We introduce AVA, a systematic f…
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…
NFDI4DS Shared Tasks for Scholarly Document Processing
Raia Abu Ahmad, Rana Abdulla, Tilahun Abedissa Taffa +18
Shared tasks are powerful tools for advancing research through community-based standardised evaluation. As such, they play a key role in promoting findable, accessible, interoperab…
YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering
Jennifer D'Souza, Hamed Babaei Giglou, Quentin Münch
Large Language Models (LLMs) drive scientific question-answering on modern search engines, yet their evaluation robustness remains underexplored. We introduce YESciEval, an open-so…
LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models
Sameer Sadruddin, Jennifer D'Souza, Eleni Poupaki +7
Extracting structured information from unstructured text is crucial for modeling real-world processes, but traditional schema mining relies on semi-structured data, limiting scalab…
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…
LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge
Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer
This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with…
Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
Hamed Babaei Giglou, Tilahun Abedissa Taffa, Rana Abdullah +4
This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4D…
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…