2025/07/05 by Zhu Liu, Zhen Hu, Liu, Zhu +5
Computer Science · Social Sciences · #Computation and Language (cs.CL) #Data Management and Algorithms #FOS: Computer and information sciences #Geographic Information Systems Studies #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2507.04070
openalex publication_date 2025/07/05 · openalex created_date 2025/10/20 · openalex updated_date 2026/07/28
Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology. However, existing construction methods either depend on labor-intensive expert reasoning or on fully automated systems lacking expert involvement, creating a tension between scalability and interpretability. We introduce XISM, an interactive system that combines data-driven inference with expert knowledge. XISM generates candidate maps via a top-down procedure and allows users to iteratively refine edges in a visual interface, with real-time metric feedback. Experiments in three semantic domains and expert interviews show that XISM improves linguistic decision transparency and controllability in semantic-map construction while maintaining computational efficiency. XISM provides a collaborative approach for scalable and interpretable semantic-map building. The system\footnotehttps://app.xism2025.xin/ , source code\footnotehttps://github.com/hank317/XISM , and demonstration video\footnotehttps://youtu.be/m5laLhGn6Ys are publicly available.