25 citations · 179 across the 54 of their papers we have counts for
7 papers · 1 filter
Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax
Marcel Hoffmann, Lukas Galke, Ansgar Scherp
Graph homophily has been considered an essential property for message-passing neural networks (MPNN) in node classification. Recent findings suggest that performance is more closel…
Semantic Source Code Segmentation using Small and Large Language Models
Abdelhalim Dahou, Ansgar Scherp, Sebastian Kurten +2
Source code segmentation, dividing code into functionally coherent segments, is crucial for knowledge retrieval and maintenance in software development. While enabling efficient na…
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
Nicolas Lell, Ansgar Scherp
Shallow node embeddings like node2vec (N2V) can be used for nodes without features or to supplement existing features with structure-based information. Embedding methods like N2V a…
CRAWLDoc: A Dataset for Robust Ranking of Bibliographic Documents
Fabian Karl, Ansgar Scherp
Publication databases rely on accurate metadata extraction from diverse web sources, yet variations in web layouts and data formats present challenges for metadata providers. This…
GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models
Justin Mücke, Ansgar Scherp
Semantic reasoning aims to infer new knowledge from existing knowledge, with OWL ontologies serving as a standardized framework for organizing information. A key challenge in seman…
Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset
Felix Burr, Marcel Hoffmann, Ansgar Scherp
Despite the ample availability of graph data, obtaining vertex labels is a tedious and expensive task. Therefore, it is desirable to learn from a few labeled vertices only. Existin…
A Transformer-based Autoregressive Decoder Architecture for Hierarchical Text Classification
Younes Yousef, Lukas Galke, Ansgar Scherp
Recent approaches in hierarchical text classification (HTC) rely on the capabilities of a pre-trained transformer model and exploit the label semantics and a graph encoder for the…