# An Approach to Constructing a Knowledge Graph Based on Korean Open-Government Data
## 2 Related Work  
The OGD platform has been considered throughout the nationwide with high expectations for benefits to society. Recent studies in this research field has covered many facets of topics including sociology, humanities, cognitive sciences, education, as well as information science. Recent work [5] have addressed the topic of OGD by identifying the problems in developing an ecosystem model of stakeholders, policies, practices, relationships, and influences. The ecosystem has been applied to two case studies of New York a,d St. Petersburg. The study [6] have investigated the evaluation methods to assess the quality of OGD. It identified common problems in OGD as lack of metadata, incompleteness, and loss of updates. Qualitative analysis has focused mainly on the policy guidelines for data quality control. Several other works [6; 7; 8] have introduced the portals as their flagships initiatives of OGD. However, they usually consider the strategic perspectives distributing OGD to society rather than its computational meanings resided in the large-scale data sets. This paper is different from the above in that we have developed a practical data search system, whose technical structures are adopted from information science, to support further data analysis for the strategic social applications. Until now, a popular information retrieval (IR) paradigm is the exact matching of index terms (or keywords) derived from documents and users' needs (such as queries) because of its simplicity and efficiency [9; 10; 11]. The most successful keyword-based IR systems used in users' daily lives are keyword search systems such as Google, Bing, and others. Typically, the keyword search system estimates the degree of relationship between the query and the target documents (i.e., search results), and the search results are ordered and displayed in a list of snippets consisting of titles, URLs and descriptions of the web pages. Even if a user wants to know the relationship between search results, he/she will not be able to apprehend the relationship explicitly in existing keyword search systems. Because of the growing number of users interested in getting a set of search results with a single query, some studiestry to take advantage of explicit relationships in documents. This paper classified these studies into two categories; logic-based search, link-based search. Logic-based search systems [12; 13; 14] use domain knowledge to define concepts and their relationships in the given domain. Therefore, a user, who wants to use the logic-based search system, must have background knowledge (such as a query language) to exploit the domain knowledge (or knowledge graph) well. For example, as knowledge graphs such as YAGO [15], and DBpedia [16] typically represent the billions of facts in the form of RDF triples, ontology-based query languages such as SPARQL and OWL-QL are widely used in logic-based search systems. Furthermore, since logic-based search systems provide inferred results via inference engines, users are generally satisfied with the quality of search results. However, learning a specific query language to exploit a logic-based search system is regarded as a barrier for ordinary users. In addition, there are no inference engines available to manage a large knowledgebase with a reasonable period of time [17]. Link-based search systems [18; 19] find relevant documents to the given query through the hyperlinks between the web documents. Most link-based search systems consist of two steps. Finding initial documents is based on the exact matching of keywords between the query and documents. In addition, the next step is to find relevant documents that are frequently referenced by the initial documents. As a result, it is possible to expand the search results to find the desired document although it does not contain any index terms derived from the query. However, link-based search systems have two limitations.