3 citations · 12 across the 15 of their papers we have counts for
17 papers
Mined from Scientific Literature: Process Schemas for Atomic Layer Deposition and Etching in Materials Science
Sameer Sadruddin, Eleni Poupaki, Alex Watkins +5
Atomic layer deposition (ALD) and atomic layer etching (ALE) are reported heterogeneously across experimental and simulation literature in materials science, hindering comparison a…
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…
ICDAR 2026 Competition on Information Extraction from Atomic Layer Deposition/Etching (ALD/E) Scientific Figures
Fahad Ahmed, Sören Auer, Jennifer D'Souza
Scientific figure comprehension and reasoning using multimodal AI requires integrating visual perception with domain-specific reasoning to extract meaningful knowledge, often not p…
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…
Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence
Jennifer D'Souza, Soren Auer, Eleni Poupaki +6
Scientific reviews are central to knowledge integration in materials science, yet their key insights remain locked in narrative text and static PDF tables, limiting reuse by humans…
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…
Leveraging Large Language Models for Realizing Truly Intelligent User Interfaces
Allard Oelen, Sören Auer
The number of published scholarly articles is growing at a significant rate, making scholarly knowledge organization increasingly important. Various approaches have been proposed t…
LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesis
Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer
In response to the growing complexity and volume of scientific literature, this paper introduces the LLMs4Synthesis framework, designed to enhance the capabilities of Large Languag…
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…
Fine-tuning and Prompt Engineering with Cognitive Knowledge Graphs for Scholarly Knowledge Organization
Gollam Rabby, Sören Auer, Jennifer D'Souza +1
The increasing amount of published scholarly articles, exceeding 2.5 million yearly, raises the challenge for researchers in following scientific progress. Integrating the contribu…
Exploring the Latest LLMs for Leaderboard Extraction
Salomon Kabongo, Jennifer D'Souza, Sören Auer
The rapid advancements in Large Language Models (LLMs) have opened new avenues for automating complex tasks in AI research. This paper investigates the efficacy of different LLMs-M…
Large Language Models as Evaluators for Scientific Synthesis
Julia Evans, Jennifer D'Souza, Sören Auer
Our study explores how well the state-of-the-art Large Language Models (LLMs), like GPT-4 and Mistral, can assess the quality of scientific summaries or, more fittingly, scientific…
Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study
Salomon Kabongo, Jennifer D'Souza, Sören Auer
This paper explores the impact of context selection on the efficiency of Large Language Models (LLMs) in generating Artificial Intelligence (AI) research leaderboards, a task defin…
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…
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology
Mahsa Shamsabadi, Jennifer D'Souza, Sören Auer
In this paper, we champion the use of structured and semantic content representation of discourse-based scholarly communication, inspired by tools like Wikipedia infoboxes or struc…