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

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

This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. For this, we introduce methods to optimize MINDWALC for a specific use case, in which the proc...

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Bibliographic Details
Main Authors: Maximilian Legnar, Joern-Helge Heinrich Siemoneit, Gilles Vandewiele, Jürgen Hesser, Zoran Popovic, Stefan Porubsky, Cleo-Aron Weis
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Machine Learning and Knowledge Extraction
Subjects:
knowledge graphs
data mining
explainable AI
interpretable machine learning
decision tree
feature extraction
Online Access:https://www.mdpi.com/2504-4990/7/1/16
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