Challenges in Realism-Based Ontology Design: a Case Study on Creating an Ontology for Motivational Learning Theories.

Abstract

Objective: to identify on the basis of a use case major problem types novices in realism-based ontology design face when attempting to construct an ontology intended to explain differences and commonalities between competing scientific theories.

Methodology: an ontology student was tasked (1) to extract manually from a paper about five distinct motivational learning theories the scientific terms used to explain the theories, (2) to map these terms where possible to type-terms from existing realism-based ontologies or create new ones otherwise, (3) to indicate for new type-terms their immediate subsumer, and (4) to document at every step issues that were encountered.

Results: where term extraction and type-term assignment were handled satisfactorily, correct classification in function of the BFO was a major challenge. Root causes identified included ambiguous and underspecified term use in the theories, the ontological status of psychological constructs, lack of high quality ontologies for the behavioral sciences and insufficient 'deep' understanding of some BFO entities, in part because of insufficient documentation thereof suitable for learners. The issues the student encountered were often insufficiently described for the instructor to identify the problem without analyzing the source paper itself.

Conclusion: whereas behavioral scientists need to do efforts to make their theories comparable, realism-based ontologies can help them therein only when ontology developers and educators put more effort in making them more accessible without violating the principles.