How to evaluate NoSQL Database Paradigms for Knowledge Graph Processing

2025-12-01

BDCAT

AI Summary

This work addresses the lack of systematic evaluation of knowledge graph (KG) storage solutions under NoSQL database paradigms, which often leads to technology selection based on heuristics rather than workload characteristics. The study proposes the first NoSQL benchmarking framework tailored to KG-specific properties, introducing Semantic Richness (SR)—a graph-centric metric that integrates connectivity density and scale. It defines four query patterns spanning simple filtering to deep traversal and conducts multi-scale experiments using the FAERS adverse drug event KG. Evaluations across three data scales systematically compare document-oriented, graph-native, and multi-model NoSQL databases, identifying performance crossover points and revealing how graph structural features influence system behavior. The findings offer a data-driven guideline for selecting appropriate NoSQL backends for KG storage.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• NoSQL Database
• Knowledge Graph
• Performance Evaluation
• Database Selection
• Benchmarking

Innovation

• Semantic Richness
• Knowledge Graph Benchmarking
• NoSQL Database Selection
• Graph-Centric Metrics
• Multi-Model DBMS