# Leveraging ontology structure for machine learning in knowledge graphs

Elliot Hugo Butz, Jérôme Azé, Konstantin Todorov, Antoine Toffano, Pierre Larmande

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Elliot Hugo Butz, Jérôme Azé, Konstantin Todorov, Antoine Toffano, Pierre Larmande. Leveraging ontology structure for machine learning in knowledge graphs. JOBIM 2024 - 24es Journées Ouvertes en Biologie, Informatique et Mathématiques, Jun 2024, Toulouse, France. hal-04636636

HAL Id: hal-04636636

https://hal.science/hal-04636636v1

Submitted on 5 Jul 2024

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# LEVERAGING ONTOLOGY STRUCTURE FOR MACHINE LEARNING IN KNOWLEDGE GRAPHS

Elliot Butz*, Antoine Toffano*, Pierre Larmande**, Jérôme Azé*, Konstantin Todorov*

*: LIRMM, Univ. Montpellier, CNRS, Montpellier France, **: DIADE, IRD, Univ. Montpellier, CIRAD, Montpellier, France

June 2024

In this poster, we aim to enhance the machine learning process for biological knowledge graphs by leveraging the ontologies present within these graphs. For this purpose, we use similarity measures between ontological concepts to quantify the errors made by the model during the learning process. The first part describes our baseline, while the second part describes the refinements we are trying.

<figure><img src="image_3.png" /><figcaption>## MACHINE LEARNING FOR LINK PREDICTION IN KNOWLEDGE GRAPH</figcaption></figure>