Efficient Semantic Summary Graphs for Querying Large Knowledge Graphs

TL;DR

This paper introduces two algorithms for RDF graph summarization that significantly reduce graph size and improve query efficiency, enabling faster querying of large knowledge graphs without losing information.

Contribution

It proposes two novel RDF graph summarization algorithms, GBS and QBS, with QBS being an optimized lossless method that enhances query performance.

Findings

QBS reduces query execution time by up to 80%.

Summarized RDF graphs decrease in size by up to 99%.

Effective retrieval of complete data with fewer triples.

Abstract

Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the development of efficient tools for allowing end users to extract useful insights from these sources of knowledge. In such a context, reducing the size of a Resource Description Framework (RDF) graph while preserving all information can speed up query engines by limiting data shuffle, especially in a distributed setting. This paper presents two algorithms for RDF graph summarization: Grouping Based Summarization (GBS) and Query Based Summarization (QBS). The latter is an optimized and lossless approach for the former method. We empirically study the effectiveness of the proposed lossless RDF graph summarization to retrieve complete data, by rewriting an RDF Query Language called SPARQL query with fewer triple patterns using a semantic similarity. We conduct our experimental study in instances of four datasets with different sizes. Compared with the state-of-the-art query engine Sparklify executed over the original RDF graphs as a baseline, QBS query execution time is reduced by up to 80% and the summarized RDF graph is decreased by up to 99%.