Copyright © 2018 Ju Ri Kim. T et. alhis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Engineering & Technology, 7 (2.33) (2018) 84-88 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET Research paper Sparql query processing in relational databases Ju Ri Kim 1 *, Zhanfang Zhao 2 , Sung-Kook Han 3 1 College of Liberal Arts, WonKwang University, City Iksan, JeonBuk, 54538, Korea 2 College of Information Engineering, Hebei GEO University, Hebei, 050031, China 3 Department of Computer Engineering, WonKwang University , City Iksan, JeonBuk,54538, Korea *Corresponding author E-mail: cyanic@wku.ac.kr Abstract Background/Objectives: The mapping RDB to RDF has become important to populate Linked Data more efficiently. This paper shows how to implement SPARQL endpoint in RDB using a conceptual level mapping approach. Methods/Statistical analysis: Many diverse approaches and related languages for mapping RDB to RDF have been proposed. The prominent achievements of mapping RDB to RDF are two standard draft Direct Mapping and R2RML proposed by W3C RDB2RDF Working Group. This paper analyzes these conventional mapping approaches and proposes a new approach based on schema mapping. The paper also presents SPARQL query processing in RDB. Findings: There are distinct differences between instance level mapping and conceptual level mapping for RDB2RDF. Data redundancy of instance level mapping causes many inevitable problems during mapping procedure. The conceptual level mapping can provide straightforward and efficient way. The ER model in RDB and RDF model in Linked Data have obvious similarity. The ER model de- scribes entities and relationships, which is the conceptual schema of RDB. RDF model consists of three parts: subject, predicate and ob- ject, which is the standard model for data interchange on the Web. The entities in ER model and subjects in RDF model are all the things that can be anything in the real world. Both the relationships in ER model and predicates in RDF model describe the relations between things. Since RDB and RDF share the similar modeling approach at the schema level, it is reasonable that mapping approach should be based on RDB schema. This kind of conceptual level mapping also can provide efficient SPARQL query processing in RDB. Improvements/Applications: The paper realizes SPARQL query processing in RDB, which is based on conceptual level mapping. The query experiments show that it is a concise and efficient way to populate Linked Data. Keywords: SPARQL, RDF; Triple; Linked Open Data; Schema Mapping. 1. Introduction The Linked Open Data (LOD) has emerged as a powerful enabler to realize Web of open, shared, interlinked data and, ultimately, the vision of Semantic Web [1-2]. To realize Web of Data based on LOD, LOD data sets from the diverse domains play the key role in expanding the global data space. Since the vast amount of useful data are still stored in relational databases (RDB), one of the most efficient ways to populate LOD sets is to map data in relational databases into RDF (Resource Description Framework) of Linked Data. Due to the importance of mapping RDB to RDF (RDB2RDF), the various mapping approaches have been proposed [5-7]. Most of mapping approaches provide the SPARQL query to process the RDB data. However, the conventional mapping RDB data to RDF has not yielded the significant achievement as was predicted, and has revealed some limitations and problems3. This paper proposes a new modeling method based on schema transla- tion for RDB to RDF. Since the conceptual schema of RDB is similar to ontological modeling of a certain domain, RDB schema mapping into RDF is more effective than the conventional in- stance-based direct mapping approaches, which can overcome some shortcomings of instance-based mapping approaches. In the following section, we discuss the conventional mapping methods and their features, and review related technique and map- ping language. In section [3], we introduce the triple modeling of relational databases and propose schema-based mapping model. The mapping language is provided. Section 4 discusses the SPARQL query procedure of schema-based mapping approach and introduces the processing of SPARQL query by examples. Section 5 concludes with the features of schema-based mapping method. 2. Materials and methods Many diverse approaches [8-9], techniques, languages [4], [15], [16], [17] and corresponding tools [13-14] for mapping RDB to RDF have been proposed over the last decade. The W3C RDB2RDF Working Group has proposed two standards to support LOD data publication: Direct Mapping and R2RML (Relational Database to RDF Mapping Language) [10-11]. Direct Mapping is the recommended approach to directly translate RDB data and its schema to RDF representation. R2RML is a generic language for describing a set of customized mappings that transform RDB data into RDF datasets. However, the proposed mapping has revealed the difficulties in handling the complex relationships of RDB ta- bles, foreign key constraints and SQL query generation [12-13]. Direct Mapping is instance-based approaches, which means that it would generate large number of RDF graphs. Figure 1 shows an example of Direct Mapping method.