Constructing the Self Data Atlas ontology: a crisp-fuzzy ontology for quality of life representation in children with neurodevelopmental and learning disabilities

The lived experiences of children with neurodevelopmental and learning disabilities are documented through diverse forms of personal data, including identity, medical, mental health, accomplishments, social relationships, financial resources, and personal ecology. However, these records rarely integrate quality of life (QoL) information represented through validated multidimensional frameworks. Building on the gap identified in a companion narrative review, this paper presents a reproducible ontology engineering methodology for representing QoL in children with neurodevelopmental and learning disabilities. The paper presents a design-science methodology for constructing an ontology from the Self Research Institute's Self Data Atlas (SDA), a seven-category taxonomy for organizing longitudinal personal data. Derived from Wieringa's design-science framework, the methodology follows a seven-step protocol comprising: (1) competency-question-based requirements specification; (2) ontology reuse and re-engineering; (3) conceptualization; (4) crisp formalization in OWL 2 DL; (5) fuzzy formalization in Fuzzy OWL 2; (6) alignment with the Common Core Ontology (CCO) as a Basic Formal Ontology (BFO)-conformant mid-level scaffold; and (7) iterative evaluation and refinement. The methodology incorporates two complementary design features. First, it separates factual and classificatory information, including demographics, SNOMED CT diagnostic codes, and clinical observations, represented in classical description logic, from graded and context-dependent QoL concepts, including social inclusion, severity, intensity, and wellbeing, represented using fuzzy datatypes, fuzzy linguistic variables, and graded class membership. Second, it embeds tritemporal reasoning by assigning three temporal qualifiers to each SDA event, state, or information artifact: (1) absolute time, representing when the fact existed in reality; (2) ingestion time, representing when the record entered the system; and (3) assignment time, representing when an interpretative classification was applied. These temporal qualifiers are represented as ontology entities rather than opaque metadata. This tritemporal structure supports longitudinal queries, retrospective recording, and event-history replay. Each stage is illustrated using examples from the seven SDA categories. The paper illustrates how CCO classes provide stable upper-level abstractions for SDA components and interoperability with ICF, SNOMED CT, HL7 FHIR, and the Schalock–Verdugo QoL model. This paper makes a methodological rather than an empirical contribution. It provides a reproducible ontology engineering methodology for future SDA ontology development and related personal-data ontologies.