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Investigating and Optimizing MINDWALC Node Classification to Extract Interpretable Decision Trees from Knowledge Graphs

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dc.contributor.authorLegnar, Maximilian
dc.contributor.authorSiemoneit, Joern-Helge Heinrich
dc.contributor.authorVandewiele, Gilles
dc.contributor.authorHesser, Juergen
dc.contributor.authorPopovic, Zoran
dc.contributor.authorPorubsky, Stefan
dc.contributor.authorWeis, Cleo-Aron
dc.date.accessioned2025-04-01T06:43:14Z
dc.date.available2025-04-01T06:43:14Z
dc.date.issued2025-FEB 13
dc.description.wosFundingTextFirst of all, we would like to thank the inventors of MINDWALC, without whom this work would not exist. In particular, this includes Gilles Vandewiele et al., who developed MINDWALC while working at the research group IDLab, Ghent University-imec. The authors gratefully acknowledge the data storage service SDS@hd supported by the Ministry of Science, Research and the Arts Baden-Wurttemberg (MWK) and the German Research Foundation (DFG) through grant INST INST 35/1503-1 FUGG. The authors also thank the IT department staff of the Medical Faculty of Mannheim and especially Bohne-Lang for supervising our computer administration and infrastructure.
dc.identifier.doi10.3390/make7010016
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/45469
dc.publisherMDPI
dc.source.issue1
dc.source.journalMACHINE LEARNING AND KNOWLEDGE EXTRACTION
dc.source.numberofpages27
dc.source.volume7
dc.title

Investigating and Optimizing MINDWALC Node Classification to Extract Interpretable Decision Trees from Knowledge Graphs

dc.typeJournal article
dspace.entity.typePublication
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