3 citations · 6 across the 9 of their papers we have counts for
9 papers
ExeKGLib: A Platform for Machine Learning Analytics based on Knowledge Graphs
Antonis Klironomos, Baifan Zhou, Zhipeng Tan +4
Nowadays machine learning (ML) practitioners have access to numerous ML libraries available online. Such libraries can be used to create ML pipelines that consist of a series of st…
GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs
Martin Boeckling, Heiko Paulheim, Sarah Detzler
Many knowledge graphs contain a substantial number of spatial entities, such as cities, buildings, and natural landmarks. For many of these entities, exact geometries are stored wi…
ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literals
Antonis Klironomos, Baifan Zhou, Zhuoxun Zheng +3
Most knowledge graph embedding (KGE) methods tailored for link prediction focus on the entities and relations in the graph, giving little attention to other literal values, which m…
SnapE -- Training Snapshot Ensembles of Link Prediction Models
Ali Shaban, Heiko Paulheim
Snapshot ensembles have been widely used in various fields of prediction. They allow for training an ensemble of prediction models at the cost of training a single one. They are kn…
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning
Michael Schlechtinger, Damaris Kosack, Franz Krause +1
In the rapidly evolving landscape of eCommerce, Artificial Intelligence (AI) based pricing algorithms, particularly those utilizing Reinforcement Learning (RL), are becoming increa…
A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid
Martin Böckling, Heiko Paulheim, Sarah Detzler
Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data. While often spatial data is represented in a tabular…
Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction
Rita T. Sousa, Heiko Paulheim
Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the…
Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs
Nicolas Hubert, Pierre Monnin, Heiko Paulheim
Knowledge graphs (KGs) comprise entities interconnected by relations of different semantic meanings. KGs are being used in a wide range of applications. However, they inherently su…
Do Similar Entities have Similar Embeddings?
Nicolas Hubert, Heiko Paulheim, Armelle Brun +1
Knowledge graph embedding models (KGEMs) developed for link prediction learn vector representations for entities in a knowledge graph, known as embeddings. A common tacit assumptio…