Work

An Extendible Realism-Based Ontology for Kinship.

Year: 2023

Type: conference-paper

Abstract: Adequately representing kinship relations is crucial for a variety of medical and biomedical applications. Several kinship ontologies have been proposed but none of them have been designed thus far in line with the Basic Formal Ontology. In this paper, we propose a novel kinship ontology that exhibits the following characteristics: (1) it is fully axiomatized in First Order Logic following the rules governing predicate formation as proposed in BFO2020-FOL, (2) it is modularized in 6 separate files written in the Common Logic Interface Format (CLIF) each one of which can be imported based on specific needs, (3) it provides bridging axioms to and from SNOMED CT, and (4) it contains an extra module with axioms which would not be literally true when phrased naively but are crafted in such a way that they highlight the unusual kinship relations they represent and can be used to generate alerts on possible data entry mistakes. We describe design considerations and challenges encountered.

Source: PubMed

Authors: Michael Rabenberg, Anuwat Pengput, Werner Ceusters

Institution: University at Buffalo, State University of New York

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: National Institutes of Health, U.S. National Library of Medicine

Awards: University of Buffalo Clinical and Translational Science Institute, Buffalo Research Innovation in Genomic and Healthcare Technology Education (BRIGHT Education), Buffalo Research Innovation in Genomic and Healthcare Technology (BRIGHT) Short-Term Training and Education

OpenAlex ID: W4399148978