1 citations · 1 across the 6 of their papers we have counts for
6 papers
Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts
Julius Neumann, Robert Lange, Yuni Susanti +1
Sentiment classification in short text datasets faces significant challenges such as class imbalance, limited training samples, and the inherent subjectivity of sentiment labels --…
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
Yuni Susanti, Michael Färber
Inferring causal relationships between variable pairs is crucial for understanding multivariate interactions in complex systems. Knowledge-based causal discovery -- which involves…
Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
Michael Färber, David Lamprecht, Yuni Susanti
Graph Neural Networks (GNNs) have substantially advanced the field of recommender systems. However, despite the creation of more than a thousand knowledge graphs (KGs) under the W3…
Can LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMs
Yuni Susanti, Michael Färber
Causal discovery traditionally relies on statistical methods applied to observational data, often requiring large datasets and assumptions about underlying causal structures. Recen…
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…