TSSE: A Two-step Semantic Extraction Framework for Knowledge Graph Construction

Knowledge graphs (KGs) are utilized to represent factual knowledge, constructing complete and accurate KGs is vital for precise knowledge representation. In mathematics education scenarios, well-defined KGs can significantly enhance both targeted instruction for teachers and comprehensive knowledge acquisition for learners. However, existing “text-to-graph” approaches face two main challenges: (1) generated KGs often suffer from either redundant or insufficient entities and relationships, resulting in structural imbalance and reduced pedagogical relevance, and (2) a lack of specialization for the unique requirements of mathematics education. To address these limitations, we propose a novel two-step semantic extraction framework implemented via an agent-based approach, specifically designed for mathematics education domain. Experimental results demonstrate that our method effectively enriches contextual information by aligning extractions with pedagogical dimensions, thereby facilitating more effective teaching and learning.