# Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data
###### Abstract  
Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.  
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Footnote †: journal: _Future Internet_  
As regards solving the abovementioned problems, previous studies have already suggested many methods to address disaster data silos and improve interoperability in terms of data management that defines and uses relationships between data [17; 18; 19; 20]. The knowledge graph is one of the representative methods representing the relationship between data along with ontology. It is expressed as a triple consisting of a subject, a predicate, and an object. The subject and the object are represented by nodes as entities. The predicate represents the relationship between entities and is represented by edges. The knowledge graph is excellent at expressing the relationship between heterogeneous data in various domains. However, the previously proposed flooding disaster knowledge graphs are constructed using different standards and terms based on case studies for specific areas; hence, some issues are difficult to interoperate and have limitations in reusability and scalability.  
In this study, we propose a knowledge graph for disaster data management related to flooding disasters using open data. Among several kinds of disaster, we focus herein on flood disaster data. As mentioned above, one of the main challenges is sharing and searching for suitable data for domain experts or researchers. Our proposed model improves interoperability, reusability, and scalability using universally existing concepts and schema in the knowledge graph. This study builds a richer concept and relations using actual open data in Korea.  
The remainder of this paper is organized as follows: Section 2 presents the background of the study and the related works for flooding disaster knowledge graphs; Section 3 describes our proposed model and the knowledge graph for flooding disasters and presents the principal datasets selected from an open dataset related to flooding disasters; Section 4 discusses the performed experiment; and finally, Section 5 concludes this study.