2 citations · 3 across the 13 of their papers we have counts for
4 papers · 1 filter
Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-based Causal Discovery
Yuni Susanti, Michael Färber
Causal discovery aims to estimate causal structures among variables based on observational data. Large Language Models (LLMs) offer a fresh perspective to tackle the causal discove…
AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning
Michael Färber, David Lamprecht, Yuni Susanti
In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the…
Prompting or Fine-tuning? Exploring Large Language Models for Causal Graph Validation
Yuni Susanti, Nina Holsmoelle
This study explores the capability of Large Language Models (LLMs) to evaluate causality in causal graphs generated by conventional statistical causal discovery methods-a task trad…
Data Augmentation Techniques for Process Extraction from Scientific Publications
Yuni Susanti
We present data augmentation techniques for process extraction tasks in scientific publications. We cast the process extraction task as a sequence labeling task where we identify a…