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Introduction: We present GFO-light, a novel lightweight top-level ontology (TLO), which has been designed for ease of use by non-ontologists. TLOs are helpful tools for establishing semantic interoperability between domain ontologies, which, in turn, have become an essential tool in biomedical informatics. While most TLOs come with rich, complex axiomatizations and terminologies that include a lot of philosophical jargon, lightweight TLOs aim to strike a balance between expressivity and accessibility by domain experts without excessive expertise in formal ontology. GFO-light is derived from the General Formal Ontology (GFO) [1], which has unique features, such as the integration of objects and processes and coincidence in continuous spacetime.

Methods: The ontological and representational choices that guided the construction of GFO-light were, on the one hand, driven by different use cases in a bottom-up approach and, on the other hand, by the design choice to accommodate the largest possible number of parts from the original GFO ontology. Furthermore, a set of common modeling patterns were accommodated, such as ready-to-use relations between time entities and the distinction between aggregation and composition. The use cases guiding the development process were the ANthropological Notation Ontology (ANNO), which allows for the classification of bone finds, the description of skeletal pieces, and the definition of functions for the derivation of different phenotypes of humans in forensic and historical anthropology [2], the Risk Identification Ontology (RIO), which models critical situations arising in the context of medical procedures [3], and the Core Ontology of Phenotyping, which defines core entities relevant for the modeling of phenotypic knowledge and the development of model-driven phenotyping software [4].

Results: The resulting GFO-light TLO centers around the ontological categories of concrete individuals (continuants, attributives, situations, and processes) and time entities. Each of these categories stems from one of the use cases: ANNO guided the introduction of the category "Continuant" for modeling anthropological entities, and "Object", "ObjectAggregate" and "ObjectPart" as well as corresponding relations for describing their compositions. Properties of and relations between entities require the introduction of the categories "Attributive" and "Relator". RIO guided the introduction of the categories "Situation", "Process", and time-related entities, such as "TimeBoundary" and "Chronoid" (time interval) and relations for modeling temporal and causal relations. COP required the introducton of "InformationObject" and justified the inclusion of the category "Category".

Discussion: A comparison with existing lightweight TLOs reveals that GFO-light is unique in providing ready-to-use patterns for representing temporal relationships. It is also the only TLO to include the category of Categories, which allows the distinction between instantiable and non-instantiable entities and is particularly useful for distinguishing between abstact information objects' content and their conrete represenations. In the endeavor to strike a balance between expressivity and lightweight design, some compromies had to be made, such as the omissions of presentials.

Conclusion: The novel GFO-light TLO attempts to strike a balance between ease of use, expressiveness, and inclusion of widely used features lacking from other lightweight TLOs. We demonstrate the applicability of GFO-light to three real-life use cases.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.

Literatur

[1] Loebe F, Burek P, Herre H. GFO: The General Formal Ontology. Applied Ontology. 2022;17:71–106. DOI: 10.3233/AO-220264
[2] Heuschkel M, Höffner K, Schmiedel F, Labudde D, Uciteli A. The ANthropological Notation Ontology (ANNO): A Core Ontology for Annotating Human Bones and Deriving Phenotypes. Semantic Web. 2025;16. DOI: 10.1177/22104968251344452
[3] Uciteli A, Neumann J, Tahar K, Saleh K, Stucke S, Faulbrück-Röhr S, Kaeding A, Specht M, Schmidt T, Neumuth T, et al. Ontology-based specification, identification and analysis of perioperative risks. Journal of Biomedical Semantics. 2017;8:36. DOI: 10.1186/s13326-017-0147-8
[4] Uciteli A, Beger C, Kirsten T, Meineke FA, Herre H. Ontological Representation, Classification and Data-Driven Computing of Phenotypes. Journal of Biomedical Semantics. 2020;11:15. DOI: 10.1186/s13326-020-00230-0
[5] Onto-Med Research Group Leipzig University. General Formal Ontology (light version) [Internet]. San Francisco, USA: GitHub; 2025 Feb 07 [updated 2025 Apr 10; cited 2025 Jun 26]. Available from: https://github.com/Onto-Med/gfo-light