3 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…