数字农科院2.0

RDFAdaptor: Efficient ETL Plugins for RDF Data Process

文献类型： 外文期刊

作者： Jiao Li;Guojian Xian;Ruixue Zhao;Yongwen Huang;Yuantao Kou;Tingting Luo;Tan Sun

作者机构：

关键词： Linked data;Portable plugins;RDF data processing;RDF ETL solution

期刊名称： Journal of Data and Information Science

ISSN： 2096-157X

年卷期： 2021 年 6.0 卷 3.0 期

页码：

收录情况： CSCD（2020-2021年度）

摘要： The interdisciplinary nature and rapid development of the Semantic Web led to the mass publication of RDF data in a large number of widely accepted serialization formats, thus developing out the necessity for RDF data processing with specific purposes. The paper reports on an assessment of chief RDF data endpoint challenges and introduces the RDF Adaptor, a set of plugins for RDF data processing which covers the whole life-cycle with high efficiency. The RDFAdaptor is designed based on the prominent ETL tool - Pentaho Data Integration - which provides a user-friendly and intuitive interface and allows connect to various data sources and formats, and reuses the Java framework RDF4J as middleware that realizes access to data repositories, SPARQL endpoints and all leading RDF database solutions with SPARQL 1.1 support. It can support effortless services with various configuration templates in multi-scenario applications, and help extend data process tasks in other services or tools to complement missing functions. The proposed comprehensive RDF ETL solution - RDFAdaptor - provides an easy-to-use and intuitive interface, supports data integration and federation over multi-source heterogeneous repositories or endpoints, as well as manage linked data in hybrid storage mode. The plugin set can support several application scenarios of RDF data process, but error detection/check and interaction with other graph repositories remain to be improved. The plugin set can provide user interface and configuration templates which enable its usability in various applications of RDF data generation, multi-format data conversion, remote RDF data migration, and RDF graph update in semantic query process. This is the first attempt to develop components instead of systems that can include extract, consolidate, and store RDF data on the basis of an ecologically mature data warehousing environment.

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