Knowledge Graph Representation and Application for Cartographic Generalization of Coastal Geomorphology in Nautical Charts
Year: 2026
Type: article
Abstract: Coastal geomorphology is a key component of nearshore representation on nautical charts. However, its cartographic generalization remains constrained by incomplete knowledge-system modeling, limited task-oriented guidance, and insufficient quantitative descriptions of generalization quality. To address these limitations, this study proposes a knowledge-graph-based approach for cartographic generalization of coastal geomorphology in nautical charts. A three-layer graph framework, consisting of a concept layer, an entity layer, and a rule layer, was constructed around shorelines, coastline categories, and generalization rules. Textual knowledge extraction and spatial-feature extraction were then conducted using cartographic specifications, historical chart samples, and expert-experience documents to support the unified organization and population of multi-source knowledge. A reasoning mechanism integrating spatial constraints and semantic associations was further developed to generate quantitative reference indicators and evaluate shoreline generalization quality. The results show that the proposed knowledge graph can organize domain knowledge in a structured and traceable form, identify rule-compliance differences among different shoreline generalization results, and provide descriptive reference benchmarks for quality evaluation. The method offers a knowledge-driven basis for improving the objectivity and explainability of coastal geomorphology generalization in nautical-chart production.
DOI: 10.3390/jmse14171568
Authors: Haoshi Song, Shuaidong Jia, Xianpeng Liu, Zikang Song, Qiqian Sun
Institution: Dalian Naval Academy
Language: English
Subfield: Geography, Planning and Development
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7204186076