Semantic similarity and machine learning with ontologies. Issue 4 (13th October 2020)

• Record Type: Journal Article
• Authors: Kulmanov, Maxat; Smaili, Fatima Zohra; Gao, Xin; Hoehndorf, Robert
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 4(2021)
• Publication Date: 2020-10-13
• Subjects: machine learning -- semantic similarity -- ontology -- knowledge representation -- neuro-symbolic integration; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa199
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies .