# Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data
## 3 Proposed Model
### Data Management Model for Flooding Disasters  
We propose herein a data management model for flooding disasters to build a knowledge graph using open datasets. We tried to consider the reusability, scalability, and interoperability of the knowledge graph in the other disaster domains. To this end, first of all, open data needs to be identified and refined for their data fields that are used differently for each organization. We analyzed the data fields of Korean open data, extracted common concepts of different data sets, and analyzed the relationship between them [29]. Standardization of data fields is based on the extracted common concepts. For extensibility, our model reuses the vocabulary of the existing knowledge graph and schema using URI [30]. Figure 1 summarizes the proposed model and defined elements.  
As shown in Figure 1, this model consists of the following eight elements:  
* Open dataset: Data providers provide the open data of various formats and domain and disaster data;
* Collect data: We define the purpose or objective of solving the disaster domain preferentially. The heterogeneous open data related to flooding disasters are collected from several data providers;
* Extract nodes and relations: On the basis of the collected data, the relationship between the data and the main data columns are analyzed to extract the concept, attribute, synonym, and semantic relationship in the flood disaster knowledge graph;
* Domain expert: When extracting nodes and relationships based on data, domain experts provide criteria and scope;
* Integrate schema: A general schema and an existing knowledge graph are used (e.g., schema.org, Wikidata, DBpedia, and owl vocabulary) to increase the reusability and the extensibility of the established knowledge graph;
* Data scientists: The nodes, edges, and URIs for the knowledge graph are defined on the basis of the concepts and relationships extracted by domain experts;
* Knowledge graph: Triple structure as an S-P-O type;
* Graph database: The constructed knowledge graph is stored in the database, such as Neo4j, in graph form.  
On the basis of this model, we collected 24 datasets related to real flooding disasters in Korea from an open data provider [31]. Table 1 describes the principal datasets selected from the open dataset related to flooding disasters. As shown in Table 1, we extracted the data columns from each dataset required for constructing the knowledge graph through an analysis of domain experts. We then integrated and used the existing knowledge graph concept and schema that are universally used terms. Finally, the knowledge graph was built and stored in a database.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Title** & \multicolumn{3}{c}{**Data Columns—Descriptions**} \\ \hline Flooding trace information & \(\bullet\) & Flood level—elevation (unit: m) of the water level due to flooding \\ Flooding trace information & \(\bullet\) & Flood area—total surface area (unit: m\({}^{2}\)) that has ever been flooded \\ Flooding trace information & \(\bullet\) & Flood time—flooding duration \\ Flooding & \(\bullet\) & Cause of flooding— brief disaster name causing the flooding \\ Flooding & \(\bullet\) & Damage amount—financial loss (unit: Korean won) due to flooding \\ \hline \multirow{3}{*}{Drainage pumping station} & \(\bullet\) & Pump station name—drainage pump station name \\ \cline{1-1}  & \(\bullet\) & Drainage area—land area (unit: m\({}^{2}\)), where the precipitation falls off \\ \cline{1-1}  & \(\bullet\) & into creeks, streams, rivers, lakes, and reservoirs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of the principal datasets selected from the open dataset related to flooding disasters.  
Figure 1: Proposed model.