10 citations · 39 across the 16 of their papers we have counts for
21 papers
Agentic schema-guided extraction of materials process knowledge from scientific literature
Sameer Sadruddin, Jennifer D'Souza
Materials literature contains detailed experimental knowledge, but procedures, chemical entities and measurements remain difficult to aggregate because they are reported in heterog…
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…
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…
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…
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…
A FAIR and Free Prompt-based Research Assistant
Mahsa Shamsabadi, Jennifer D'Souza
This demo will present the Research Assistant (RA) tool developed to assist with six main types of research tasks defined as standardized instruction templates, instantiated with u…
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…
Toward FAIR Semantic Publishing of Research Dataset Metadata in the Open Research Knowledge Graph
Raia Abu Ahmad, Jennifer D'Souza, Matthäus Zloch +5
Search engines these days can serve datasets as search results. Datasets get picked up by search technologies based on structured descriptions on their official web pages, informed…
From Keywords to Structured Summaries: Streamlining Scholarly Information Access
Mahsa Shamsabadi, Jennifer D'Souza
This paper highlights the growing importance of information retrieval (IR) engines in the scientific community, addressing the inefficiency of traditional keyword-based search engi…
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…
Toward Semantic Publishing in Non-Invasive Brain Stimulation: A Comprehensive Analysis of rTMS Studies
Swathi Anil, Jennifer D'Souza
Noninvasive brain stimulation (NIBS) encompasses transcranial stimulation techniques that can influence brain excitability. These techniques have the potential to treat conditions…
Procedural Text Mining with Large Language Models
Anisa Rula, Jennifer D'Souza
Recent advancements in the field of Natural Language Processing, particularly the development of large-scale language models that are pretrained on vast amounts of knowledge, are c…
LLMs4OL: Large Language Models for Ontology Learning
Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer
We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, de…
Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph
Jennifer D'Souza, Moussab Hrou, Sören Auer
There have been many recent investigations into prompt-based training of transformer language models for new text genres in low-resource settings. The prompt-based training approac…
ORKG-Leaderboards: A Systematic Workflow for Mining Leaderboards as a Knowledge Graph
Salomon Kabongo, Jennifer D'Souza, Sören Auer
The purpose of this work is to describe the Orkg-Leaderboard software designed to extract leaderboards defined as Task-Dataset-Metric tuples automatically from large collections of…
Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections
Ming Jiang, Jennifer D'Souza, Sören Auer +1
The rapid growth of research publications has placed great demands on digital libraries (DL) for advanced information management technologies. To cater to these demands, techniques…
Zero-shot Entailment of Leaderboards for Empirical AI Research
Salomon Kabongo, Jennifer D'Souza, Sören Auer
We present a large-scale empirical investigation of the zero-shot learning phenomena in a specific recognizing textual entailment (RTE) task category, i.e. the automated mining of…
Computer Science Named Entity Recognition in the Open Research Knowledge Graph
Jennifer D'Souza, Sören Auer
Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annot…
Easy Semantification of Bioassays
Marco Anteghini, Jennifer D'Souza, Vitor A. P. Martins dos Santos +1
Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose…
Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions -- A Trial Dataset
Jennifer D'Souza, Sören Auer
Purpose: The aim of this work is to normalize the NLPCONTRIBUTIONS scheme (henceforward, NLPCONTRIBUTIONGRAPH) to structure, directly from article sentences, the contributions info…