Rel2Graph: Automated Mapping From Relational Databases to a Unified Property Knowledge Graph

Abstract Although a few approaches are proposed to convert relational databases to graphs, there is a genuine lack of systematic evaluation across a wider spectrum of databases. Recognising the important issue of query mapping, this paper proposes an approach called Rel2Graph, an automatic knowledge graph construction (KGC) approach from an arbitrary number of relational databases. Our approach also supports the mapping of conjunctive SQL queries into pattern-based NoSQL queries. We evaluate our proposed approach on two widely used relational database-oriented datasets: Spider and KaggleDBQA benchmarks for semantic parsing. We employ the execution accuracy (EA) metric to quantify the proportion of results by executing the NoSQL queries on the property knowledge graph we construct, which aligns with the results of SQL queries performed on relational databases. Consequently, the counterpart property knowledge graph of benchmarks can be ensured to have high accuracy and integrity. The code and data will be publicly available. The code and data are available at github∗. ∗https://github.com/nlp-tlp/Rel2Graph