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

TL;DR

Rel2Graph is an automated system that converts relational databases into a unified property knowledge graph, supporting query translation and evaluated on standard benchmarks with high accuracy.

Contribution

This paper introduces Rel2Graph, a novel automated approach for converting relational databases into property knowledge graphs with query mapping capabilities, evaluated on multiple benchmarks.

Findings

Achieved high execution accuracy on Spider and KaggleDBQA datasets.

Supports mapping of conjunctive SQL queries to NoSQL patterns.

Provides publicly available code and data for reproducibility.

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 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 that aligns with the results of SQL queries performed on relational databases. Consequently, the counterpart property knowledge graph of benchmarks with high accuracy and integrity can be ensured. The code and data will be publicly available. The code and data are available at github\footnote{https://github.com/nlp-tlp/Rel2Graph}.