Bridging the Technology Gap Between Industry and Semantic Web: Generating Databases and Server Code From RDF

TL;DR

This paper presents an automatic method to convert RDF-based Semantic Web datasets into relational databases with REST APIs, facilitating industry adoption of Semantic Web data access.

Contribution

It introduces a fully automatic approach for generating relational databases and REST APIs from RDF datasets, bridging the gap between Semantic Web and industry practices.

Findings

Generated databases accurately reflect RDF datasets

APIs can reproduce simple SPARQL queries

Potential improvements include reducing data redundancies

Abstract

Despite great advances in the area of Semantic Web, industry rather seldom adopts Semantic Web technologies and their storage and query concepts. Instead, relational databases (RDB) are often deployed to store business-critical data, which are accessed via REST interfaces. Yet, some enterprises would greatly benefit from Semantic Web related datasets which are usually represented with the Resource Description Framework (RDF). To bridge this technology gap, we propose a fully automatic approach that generates suitable RDB models with REST APIs to access them. In our evaluation, generated databases from different RDF datasets are examined and compared. Our findings show that the databases sufficiently reflect their counterparts while the API is able to reproduce rather simple SPARQL queries. Potentials for improvements are identified, for example, the reduction of data redundancies in generated databases.