2 citations · 2 across the 6 of their papers we have counts for
6 papers
Tabular Foundation Models Can Learn Association Rules
Erkan Karabulut, Daniel Daza, Paul Groth +2
Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequen…
Discovering Association Rules in High-Dimensional Small Tabular Data
Erkan Karabulut, Daniel Daza, Paul Groth +1
Association Rule Mining (ARM) aims to discover patterns between features in datasets in the form of propositional rules, supporting both knowledge discovery and interpretable machi…
Neurosymbolic Association Rule Mining from Tabular Data
Erkan Karabulut, Paul Groth, Victoria Degeler
Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimension…
Learning Semantic Association Rules from Internet of Things Data
Erkan Karabulut, Paul Groth, Victoria Degeler
Association Rule Mining (ARM) is the task of discovering commonalities in data in the form of logical implications. ARM is used in the Internet of Things (IoT) for different tasks…
AE SemRL: Learning Semantic Association Rules with Autoencoders
Erkan Karabulut, Victoria Degeler, Paul Groth
Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data,…
Semantic Association Rule Learning from Time Series Data and Knowledge Graphs
Erkan Karabulut, Victoria Degeler, Paul Groth
Digital Twins (DT) are a promising concept in cyber-physical systems research due to their advanced features including monitoring and automated reasoning. Semantic technologies suc…