How to evaluate NoSQL Database Paradigms for Knowledge Graph Processing

TL;DR

This paper introduces a specialized benchmarking framework for evaluating NoSQL databases in knowledge graph processing, providing systematic, metric-driven guidance for selecting optimal storage paradigms based on graph characteristics.

Contribution

It presents a novel KG-specific benchmarking framework with a graph-centric metric, enabling systematic comparison of NoSQL paradigms for knowledge graph workloads.

Findings

Performance crossover points identified between paradigms

Guidelines for matching NoSQL storage to KG features

Empirical evaluation on FAERS KG at multiple scales

Abstract

Knowledge Graph (KG) processing faces critical infrastructure challenges in selecting optimal NoSQL database paradigms, as traditional performance evaluations rely on static benchmarks that fail to capture the complexity of real-world KG workloads. Although the big data field offers numerous comparative studies, in the KG context DBMS selection remains predominantly ad-hoc, leaving practitioners without systematic guidance for matching storage technologies to specific KG characteristics and query requirements. This paper presents a KG-specific benchmarking framework that employs connectivity density, scale, and introduces a graph-centric metric, namely Semantic Richness (SR), within a four-tier query methodology to reveal performance crossover points across Document-Oriented, Graph, and Multi-Model DBMSs. We conduct an empirical evaluation on the FAERS adverse event KG at three scales, comparing paradigms from simple filtering to deep traversal, and provide metric-driven, evidence-based guidelines for aligning NoSQL paradigm selection with graph size, connectivity, and semantic richness.