2 citations · 3 across the 13 of their papers we have counts for
13 papers
The zbMATH Open Knowledge Graph: Tracing Centuries of Mathematical Research
Yuni Susanti, Moritz Schubotz
We present the zbMATH Open Knowledge Graph, a large-scale RDF knowledge graph (KG) covering more than 250 years of mathematical scholarship. Unlike existing scholarly knowledge gra…
Graph-Based Discovery of Mathematical Software Communities and Publication-to-Community Prediction
Maxence Azzouz-Thuderoz, Yuni Susanti, Moritz Schubotz
Research software forms distinct co-usage communities that span traditional disciplinary boundaries, yet the structure of these communities remains largely unexplored. We present a…
Syntax Meets Semantics: Understanding Scientific Formulae
Yuni Susanti, Moritz Schubotz
Scientific formulae are a fundamental component of scholarly communication, yet their dual nature -- as structured syntax and carriers of semantics -- remains underexplored in scho…
SemRepo: A Knowledge Graph for Research Software and Its Scholarly Ecosystem
Abdul Rafay, Yuni Susanti, David Lamprecht +1
We present SemRepo, an RDF knowledge graph comprising over 81 million triples describing nearly 200,000 GitHub repositories associated with scientific research. SemRepo captures re…
Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models
Florian Kelber, Matthias Jobst, Yuni Susanti +1
Scientific knowledge discovery increasingly relies on large language models, yet many existing scholarly assistants depend on proprietary systems with tens or hundreds of billions…
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…
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…