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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Semantic similarity and machine learning with ontologies.

Maxat Kulmanov¹, Fatima Zohra Smaili¹, Xin Gao²

¹King Abdullah University of Science and Technology.

Briefings in Bioinformatics

|October 13, 2020

Summary

This summary is machine-generated.

This study explores using ontologies to enhance machine learning in life sciences. It details methods for semantic similarity and ontology embeddings to improve model performance with background knowledge.

Keywords:

knowledge representation machine learning neuro-symbolic integration ontology semantic similarity

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Area of Science:

Life Sciences
Bioinformatics
Computational Biology

Background:

Ontologies are crucial for representing biological knowledge in databases.
Ontologies are increasingly utilized to enrich similarity-based analysis and machine learning models.
Combining ontologies with machine learning is an emerging and evolving research area.

Purpose of the Study:

To provide an overview of methods integrating ontologies into machine learning.
To explain how semantic similarity measures and ontology embeddings leverage ontological background knowledge.
To demonstrate how ontologies can impose constraints to enhance machine learning models.

Main Methods:

Overview of methods for computing semantic similarity using ontologies.
Explanation of ontology embedding techniques for machine learning.
Description of how ontologies provide constraints for machine learning models.

Main Results:

Semantic similarity measures effectively exploit ontological background knowledge.
Ontology embeddings offer a novel way to represent ontological information for machine learning.
Ontology-based constraints can improve the performance and interpretability of machine learning models.

Conclusions:

Integrating ontologies significantly enhances machine learning in the life sciences.
The presented methods offer practical approaches for applying ontologies in machine learning workflows.
Executable notebooks and resources are available for further research and application.