## 4 Discussion
Therefore, codes from multiple taxonomies must be added to the properties of a phenotype class to make sure that resulting queries are executable in all centres. The values of numeric phenotypes used in formulas of unrestricted derived phenotype classes may be missing, resulting in an incalculable phenotype. In this case, additional expressions (e.g., conditional if-else expressions) can be inserted into the formula to provide, for example, a default value or an alternative phenotype to be used as a substitute. Ultimately, only some aspects of poor data quality can be addressed at the level of phenotypic abstraction. [PERSON] et al. proposed a low-burden, multicentric model for cohort assessments based on computable phenotype algorithms [61]. Their premise is that all participating centres are autonomous, diverse and operate individual solutions for count estimations (e.g., i2b2 [62], TriNetX [63], and direct database access). Phenotype algorithms are developed and discussed by informaticians and data scientists and shared between the centres so that they can run queries in their respective solutions, and the resulting count estimations are returned. We are confident that our solution can also be used in the setting described. For phenotypic queries resulting from PheSOs to be executable in all centres, appropriate transformations need to be implemented. After this initial overhead, phenotypic query execution is narrowed down to the exchange of PheSOs between centres. In addition, PheSOs can be developed by scientists without a background in informatics. An ontological approach for building computable eligibility criteria commonly used in Hepatitis C virus clinical trials was proposed by [PERSON] et al. [3]. Their approach was based on the OBDA framework Ontop, which enables querying relational databases as virtual RDF graphs with SPARQL queries. They constructed an ontology for computable eligibility criteria in the Hepatitis C virus (OCED-HCV) based on the Basic Formal Ontology as the upper-level ontology and additional ontologies such as the Human Disease Ontology and the Ontology for Biomedical Investigations. Via semantic mapping axioms, entities in the OCED-HCV were linked to data constructs in the trial data management system. Researchers can use a web frontend to construct eligibility criteria and visualise cohort results. The frontend transmits the criteria to a backend, where they are translated into SPARQL queries and executed on the trial data. We believe that our approach of modelling phenotype classes and automatically constructing phenotypic queries is more generic than the one proposed by [PERSON] et al., because we did not prepare a disease-specific ontology but encouraged researchers in building phenotype classes and enriching them with annotations on their own. The resulting standardised ontological phenotype models (PheSOs) can be shared, are usable independent of the trial data management system (with the drawback that mappings need to be implemented), and are not restricted to a limited set of diseases (in this case, entities in OCED-HCV). The main limitation of our proposed ontology-based phenotypic model is that we cannot take advantage of the full expressive power of specific query languages (such as SQL or SPARQL). However, our approach is intended for domain experts, so we focused on the most relevant query types (phenotypic queries) that they use in their work. By providing user-friendly tools, domain experts can be efficiently supported in specifying phenotypic knowledge and building respective queries. Another limitation is the need to implement adapters for the corresponding data sources. We will explore solutions to simplifying adapter implementation.