Ontology of language, with applications to demographic data

Author:

Dowland S. Clint12,Smith Barry3,Diller Matthew A.2,Landgrebe Jobst3,Hogan William R.2

Affiliation:

1. Department of Biomedical Informatics, University of Arkansas for Medical Sciences, AR, USA

2. Department of Health Outcomes and Biomedical Informatics, University of Florida, FL, USA

3. Department of Philosophy, State University of New York at Buffalo, NY, USA

Abstract

Here we present what we believe is a novel account of what languages are, along with an axiomatically rich representation of languages and language-related data that is based on this account. We propose an account of languages as aggregates of dispositions distributed across aggregates of persons, and in doing so we address linguistic competences and the processes that realize them. This paves the way for representing additional types of language-related entities. Like demographic data of other sorts, data about languages may be of use to researchers in a number of areas, including biomedical research. Data on the languages used in clinical encounters are typically included in medical records, and capture an important factor in patient-provider interactions. Like many types of patient and demographic data, data on a person’s preferred and primary languages are organized in different ways by different systems. This can be a barrier to data integration. We believe that a robust framework for representing language in general and preferred and primary language in particular – which has been lacking in ontologies thus far – can promote more successful integration of language-related data from disparate data sources.

Publisher

IOS Press

Subject

Linguistics and Language,Language and Linguistics,General Computer Science

Reference34 articles.

1. Arp, R., Smith, B. & Spear, A.D. (2015). Building Ontologies with Basic Formal Ontology. MIT Press.

2. Bittner, T., Donnelly, M. & Smith, B. (2004). Individuals, universals, collections: On the foundational relations of ontology. In A. Varzi and L. Vieu (Eds.), Formal Ontology in Information Systems. Proceedings of the Third International Conference (FOIS 2004), 2004 (pp. 37–48). Amsterdam: IOS Press.

3. Vague reference and approximating judgments;Bittner;Spatial Cognition & Computation,2003

4. Blaisure, J.C. & Ceusters, W.M. (2018). Improving the ‘fitness for purpose’ of common data models through realism based ontology. In AMIA 2017 Annual Symposium Proceedings (pp. 440–447). American Medical Informatics Association.

5. The role of axiomatically rich ontologies in transforming medical data to knowledge;Brochhausen;Studies in Health Technology and Informatics,2018

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