Nie, Q.; Sheng, X.; Zhang, N.; Wang, J.-J.; Shang, Y.-G., and Hu, F.-H., 2019. Construction of a nautical knowledge graph based on multiple data sources. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 223–226. Coconut Creek (Florida), ISSN 0749-0208.

To automatically recognize the nautical chart symbol is a difficult task; the first recognize step works well by the image recognition methods based on deep neural networks, however, these symbols usually have their own domain meanings which are different from their common meanings; a feasible method to improve the task is using domain knowledge to describe the domain meanings of these symbols. This paper proposes an automatic nautical knowledge graph construction method based on multiple data sources. The data sources adopted include structured data like data in relational databases, semi-structured data like data from web pages, and unstructured data like text. The author uses different methods to extract knowledge from these three types of data sources, including the rule-based method, wrapper method and machine learning method, etc. Then the author tests the proposed method by using it to establish a knowledge graph for the nautical industry. The established knowledge graph contains about 1,500 concepts and over 250 million hyponymy relations, about 80 million entities and about 30 million relations among the entities; and the average precision of these knowledge is above 0.9. The experimental result proves that the proposed method is precise and efficient.