# Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data
## 3 Proposed Model
### Knowledge Graph for Flooding Disaster  
This section presents the proposed flooding disaster knowledge graph. First of all, our knowledge graph was constructed to consider reusability and scalability. The flooding disaster knowledge graph defines a hierarchical skeleton structure between the flooding concept and the existing knowledge graph and schema (Figure 2). We linked the concept by limiting only Wikidata, DBpedia, and schema.org among various knowledge graphs on the basis of the Flooding reference concepts in KBpedia [32]. The flooding concept has the \"same as\" relationship with the Flood of Wikidata and has the same meaning. In addition, Wikidata's Natural Disaster and High Tide exist as super classes of Flooding. In other words, all concepts in the flooding disaster knowledge graph are subordinate to these classes. According to Flooding reference concept of KBpedia, we can infer that Wikidata's Hazard, DBpedia's Activity and Event, and schema.org's Action classes are closely related to the Flooding concept. We thus define their relationship as a new relationship called isCloselyRelated. Therefore, the flooding disaster knowledge graph improves the interoperability with existing knowledge bases, search accessibility, and expandability by linking and reusing the general schema with the terms of the existing knowledge graph. We now explore the principal relationships with Flooding based on the collected open data (Figure 3). The three relationships first applied are \"has cause\", \"has effect\", and \"subclass of\". Using these relationships, we can expand the information from flooding to damage to human lives and property as well as other kinds of natural disasters, such as earthquakes and typhoons. Although the knowledge graphs developed in this study may appear similar to those released for other reference sites, we incorporated many details based on real open data provided by the Korean government. Moreover, the use of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Title** & \multicolumn{2}{c}{**Data Columns—Descriptions**} \\ \hline Precipitation data for the river basin & \(\bullet\) & Precipitation amount—customized precipitation forecast \\  & & information for each watershed \\ \hline \multirow{4}{*}{Nakdong river flood forecasting information} & \(\bullet\) & Water level prediction—forecasting information (unit: m) on the \\  & & flooding conditions in major rivers around the southeastern region \\  & & in Korea \\  & & Flood issuance—indication of the flooding occurrence \\ \hline \multirow{2}{*}{Natural disaster situation information inquiry service} & \(\bullet\) & Topography height—height (unit: m) of topography \\  & \(\bullet\) & Land use code–code number indicating land use categories \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: Hierarchical skeleton structure between the Flooding concept and the existing knowledge graph and schema. similar or the same relationships allows us to easily link our knowledge graphs with other references, which is a good characteristic of our knowledge graphs in terms of compatibility. We first analyzed what causes flooding while considering the causes of domestic and outside water immersion. We could then provide the natural disaster information and status of essential facilities near rivers and residential areas in addition to the list of flooding cases and definitions. We utilized the external data built in a knowledge graph format as much as possible. The left side of Figure 3 shows how we linked the external knowledge graph released by Wikidata. Moreover, we linked the information of possible damage due to flooding to the flooding data. For more detailed information, we included local and national open data; hence, our knowledge graphs are expected to be useful for residents and policy makers at the local region of interest. Accordingly, one can obtain more fruitful flooding information from those linked data with meaningful relationships within our knowledge graph.