关系型数据的知识抽取和RDF转换框架及实现

关系数据库是行业中广泛使用的数据存储和管理方案，根据自定义的本体模型从关系型数据中进行知识抽取并转换成RDF是构建行业知识图谱的关键步骤。但是当前关系数据的知识抽取方案，需要进行大量的查询语句和映射的编辑工作，映射语句编码的工作量和映射的维护是关系型数据的知识抽取的主要障碍。针对以上问题提出一种能够自动解析关系数据和本体模型并生成对应关系，支持可视化调整和修正的关系型知识抽取转换框架。该框架简化了映射编辑和维护工作，提供了更自动化和简单易用的关系型数据的知识抽取的解决方案。最后应用该框架进行知识图谱的构建的实验表明，该框架能够较为高效地对关系数据进行知识抽取并转换成RDF。

Framework and Implementation of Knowledge Extraction and RDF Transformation for Relational Data

Relational database is a widely used data storage and management scheme in the industry. According to the user-defined ontology model, knowledge extraction from relational data and conversion to RDF is the key step to build the industry knowledge graph. However, the current knowledge extraction scheme of relational data needs a lot of editing work of query statements and mapping. The workload of mapping statement coding and the maintenance of mapping are the main obstacles of knowledge extraction of relational data. To solve the above problems, a relational knowledge extraction and transformation framework is proposed, which can automatically parse relational data and ontology model and generate corresponding relationships, and support visual adjustment and correction. The framework simplifies the mapping editing and maintenance, and provides a more automatic and easy-to-use solution for knowledge extraction from relational data. Finally, the framework is applied to the construction of knowledge graph, and the experimental results show that the framework can extract knowledge from relational data automatically and efficiently and transform it into RDF.