Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

International audienceWhile the data deluge accelerates, most of the data produced remains locked in deep Web databases. For the linked open data to benefit from the potential represented by this huge amount of data, it is crucial to come up with solutions to expose heterogeneous databases as linked data. The xR2RML mapping language is an endeavor towards this goal: it is designed to map various types of databases to RDF, by flexibly adapting to heterogeneous query languages and data models while remaining free from any specific language. It extends R2RML, the W3C recommendation for the mapping of relational databases to RDF, and relies on RML for the handling of various data formats. In this paper we present xR2RML, we analyse data models of several modern databases as well as the format in which query results are returned , and we show how xR2RML translates any result data element into RDF, relying on existing languages such as XPath and JSONPath when necessary. We illustrate some features of xR2RML such as the generation of RDF collections and containers, and the ability to deal with mixed data formats. We also describe a real-world use case in which we applied xR2RML to build a SKOS thesaurus aimed at supporting studies on History of Zoology, Archaeozoology and Conservation Biology

RDB to RDF 변환을 위한 의미 정보 보존 맵리듀스 처리

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 김형주.Today, most of the data on the web is stored in relational databases, which is called deep web. Semantic web is a movement to the next generation of the web, where all data are augmented with well-defined semantics and linked together in machine-readable format. RDB2RDF approaches have been proposed and standardized by W3C, which publishes relational data to semantic web by converting relational data into RDF formatted data. We propose a system that automatically transforms relational data into RDF data and creates OWL ontology based on the schema of database. Some approaches have been proposed, but most of them did not fully make use of schema information to extract rich semantics, nor did they experimented on large databases for performance. We utilize Hadoop framework in transformation process, which enables distributed system for scalability. We present mapping rules that implements augmented direct mapping to create local ontology with rich semantics. The results show that our system successfully transforms relational data into RDF data with OWL ontology, with satisfactory performance on large-sized databases.Abstract i Introduction 3 Related Work 7 2.1 Semantic ETL Systems 7 2.2 Hadoop MapReduce 8 2.3 Mapping Approaches 9 Mapping Rules 14 3.1 General Rule 1 19 3.2 General Rule 2 20 3.3 General Rule 3 20 3.4 General Rule 4 21 3.5 General Rule 5 21 3.6 Constraint Rule 1 22 3.7 Constraint Rule 2 22 3.8 Constraint Rule 3 23 3.9 Constraint Rule 4 24 3.10 Constraint Rule 5 24 3.11 Constraint Rule 6 25 3.12 Discussion 26 Our Approach 28 4.1 Preprocessing 28 4.1.1 Schema Caching Method 30 4.1.2 Relational Data 32 4.2 Hadoop Algorithm 33 Experiment 36 5.1 Ontology Extraction 37 5.2 Performance 38 5.3 Scalability 41 Conclusion 42 Reference 44 Appendix 46Maste