building-surmise-relations

The QUERY algorithm elicits direct prerequisites from experts using a formalized questioning protocol, producing a quasi-order of knowledge items to establish principled prerequisite relationships within a knowledge space.

Validating expert-derived surmise relations is best done using IITA and FCA cross-validation, which compares expert-elicited prerequisites with data-driven and lattice-based insights to ensure the knowledge-space structure is robust and coherent.

To enforce transitive closure and acyclicity when building a knowledge graph, use a structured workflow that checks and updates surmise_relations and competence_relations during graph I/O, ensuring the persisted JSON graph remains valid and cycle-free.

Yes, you can use this prerequisite discovery workflow to support adaptive assessment and personalized learning paths by deploying it within course materials pipelines to generate a coherent prerequisite structure across knowledge items.

Yes, the surmise relations workflow requires reading and writing graphs in JSON files conforming to the knowledge-graph.schema.json schema, ensuring updated surmise_relations and competence_relations maintain provenance data for audit and reproducibility.

You should avoid expert querying for knowledge space construction when you lack domain experts available for the formalized questioning protocol, as the QUERY algorithm relies on expert input to accurately identify and codify direct prerequisites.