# An Approach to Constructing a Knowledge Graph Based on Korean Open-Government Data
## 3 Metadata-based Open-Government Data Platform  
### Background and Conceptual Model  
Big data technologies have revolutionized many IR areas in recent years, and the make especially decision-making more reliable. Previously, as several decision-making have been significantly influenced by decision makers' intuition, such as past experience or observation, there are no needs to systematically collect and analyze large amount of data. Since the accuracy of data-driven decision-making has been improved and gained a lot of interests, some governments have started to adopt the data-driven policymaking or review its adoption. The central government of Korea has found that making a good decision is started from using good data, but it does not know the exact status of OGDs (such as the number, type and quality of OGDs) held by each government agency. This is because only OGDs, which government agencies intend to disclose to other agencies as well as citizens, have been registered in the Korean OGD portal just like other countries.  
To rebuild the data governance from production to disposal, the Korean government has built a metadata-based open-data platform that allows the central government to accurately identify the number, type and location of data. Collecting metadata instead of raw data from government agencies offers several benefits. First, it is possible to reduce a data repository and collect raw data as needed. Second it is possible to increase the collecting interval for keeping the collected data up-to date. Lengthening the collecting interval can significantly reduce the load of network. In this paper, metadata refers to auxiliary data on systems, databases, tables and columns to infer the characteristics(i.e., structure, owner, location, data types, etc.) of OGDs. To ensure government agencies comply with guidelines of the central government, an instruction has been established by the central government of Korea to standardize database construction and its management. In the instruction, 43 metadata have been defined as national standards, and some of metadata are as follows: agency name, system name, database name, database description, table name, table description, column name, column description, data type, and so on. Due to the size constraints of the paper, the rest of metadata and their detailed descriptions are omitted.  
Figure 1 shows the conceptual model of an open-data platform and the data flow of the platform applied to the Korean government. The proposed open-data platform has four key features. First, whenever the metadata of OGDs in a government agency have created or modified, the changed metadata are automatically collected to the agency's metadata system and transmitted to the central metadata system in real time (or periodically according to system settings). Second, by referencing the standard terminology dictionary, the terms, types and formats of the collected metadata are converted into the standardized metadata to share metadata with others and to analyze the relationships of OGDs in the graph. Unless the standardization of terminology is performed, data nodes (i.e., vertices) with synonyms cannot be inter-linked in an OGD graph. Third, by automatically analyzing the relationship of data nodes, the visualized form of the graph can be provided to citizens who want to get useful information. For example, by using the OGD graph, it is possible to find a set of OGDs with a single query by exploring the estimated relationships of OGDs. In addition, it is possible to know the types of OGDs and the number of OGDs that each government agency has. As a result, it may be easier to request OGDs that are not yet available to citizens on their OGD portal. Finally, by collecting metadata and requesting raw data as needed, it is possible to integrate and analyze data models and raw data from heterogeneous domains. The analyzed results can be used for government officials to enhance the accuracy of policy execution, which is typically referred to as the data-driven policymaking.