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Challenges in Realism-Based Ontology Design: a Case Study on Creating an Ontology for Motivational Learning Theories
Irshad Ally1, Werner Ceusters1
1Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA.
CEUR Workshop Proceedings
|June 11, 2024
Summary
Novices struggle with realism-based ontology design for comparing scientific theories. Challenges include ambiguous terms, psychological constructs, and poor documentation of foundational ontologies.
Area of Science:
- Ontology Design
- Behavioral Sciences
- Scientific Theory Comparison
Background:
- Realism-based ontologies aim to clarify scientific theories.
- Novice ontology designers face challenges in constructing these complex structures.
- Comparing competing scientific theories requires robust ontological frameworks.
Purpose of the Study:
- Identify major problem types encountered by novices in realism-based ontology design.
- Analyze difficulties in creating ontologies for explaining differences and commonalities between scientific theories.
Main Methods:
- A case study involving an ontology student was conducted.
- The student extracted scientific terms from motivational learning theories.
- Terms were mapped to existing ontologies or new ones were created, with issues documented.
Main Results:
- Correct classification within the Basic Formal Ontology (BFO) was a significant challenge.
- Identified root causes include ambiguous terminology, the ontological status of psychological constructs, and a lack of high-quality behavioral science ontologies.
- Insufficient documentation of BFO entities for learners exacerbated difficulties.
Conclusions:
- Realism-based ontologies can aid behavioral scientists in theory comparison.
- Greater effort is needed from ontology developers and educators to improve accessibility without compromising principles.
- Enhanced documentation and clearer terminology are crucial for effective ontology design and use.
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