The use of foundational ontologies in biomedical research

Abstract

Background: The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages.

Results: From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers).

Conclusion: Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.

Keywords: Biomedical ontologies; FAIR; Foundational ontologies; Systematic literature mapping.

Fig. 1

de Almeida Falbo et al.’s view of ontological levels as a continuum. Ontologies that define more general concepts of the world (e.g., foundational ontologies) are placed more to the left, while domain specific ones are placed more to the right. In the examples, BFO is defined in the leftmost side, while Biotop is depicted as more specific than BFO, but still more general than domain ones, such as ORDO. The positioning of SIO, Biotop, GO, ORDO and the EJP RD CDE Semantic Model are examples suggested by the authors of this paper

Fig. 2

Steps of the SLM method. The first step - planning - consists of defining the research questions, sources, queries and selection criteria. During conduction, the queries are applied to research sources, and the extracted papers are deduplicated and selected accordingly to different excerpts of information. In the last step, the selected papers are used to answer the research questions defined in the first step