用知识图谱优化几何题集，精准匹配教学目标

Euclidean geometry has historically played a central role in cultivating logical reasoning and abstract thinking within mathematics education, but has experienced waning emphasis in recent curricula. The resurgence of interest, driven by advances in artificial intelligence and educational technology, has highlighted geometry's potential to develop essential cognitive skills and inspired new approaches to automated problem solving and proof verification. This article presents an ontology-based framework for annotating and optimizing geometry problem sets, originally developed in the 1990s. The ontology systematically classifies geometric problems, solutions, and associated skills into interlinked facts, objects, and methods, supporting granular tracking of student abilities and facilitating curriculum design. The core concept of 'solution graphs': directed acyclic graphs encoding multiple solution pathways and skill dependencies enables alignment of problem selection with instructional objectives. The framework has been tested in practice through the annotation of thousands of problems over three decades. We contend that our approach addresses longstanding challenges in representing dynamic, procedurally complex mathematical knowledge. We conclude by articulating a research agenda: the open problems of automated problem annotation and solution validation, whose resolution would reduce the time teachers spend validating student work and enable interactive feedback for self-learners.