The Tower of Babel Problem: Making Data Make Sense with Basic Formal Ontology

Abstract

Purpose – Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.

Design/methodology/approach – The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing a contextual review of the widely used Basic Formal Ontology (BFO) through digital methods and descriptions, and presents historical research alongside unstructured interview data with leading experts in BFO development.

Findings – The author found that ACOs are products of communal deliberation and decision-making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.

Originality/value – The paper presents a new approach for studying ACOs, the unintended social effects of ACO work, and describes methods that may be used to produce further applied ontology studies.

Keywords: Applied Computational Ontology, Social Ontology, Tower of Babel Problem, Data Ethics, Semantic Technology