QuaQue: Design and SQL Implementation of Condensed Algebra for Concurrent Versioning of Knowledge Graphs

2026-03-19

AI Summary

This work addresses the challenge of inefficient cross-version querying in multi-version knowledge graphs under concurrent environments. The authors propose QuaQue, a system that enables efficient querying by translating SPARQL queries into SQL and leveraging a novel condensed algebra combined with a bitstring-encoded version storage mechanism within standard relational databases. This approach constitutes the first SPARQL-to-SQL translation framework that natively supports version semantics, significantly enhancing query performance through relational modeling of knowledge graphs. Experimental results demonstrate that QuaQue outperforms native RDF triplestore systems on standard benchmarks, establishing a new baseline for scalable and efficient versioned knowledge graph querying.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

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 query analysis, representation and understanding

Abstract

The management of versioned knowledge graphs presents significant challenges, particularly in querying data across multiple versions efficiently. This paper introduces QuaQue, a key component of the ConVer-G system, which addresses this challenge by translating SPARQL (SPARQL Protocol and RDF Query Language) queries into SQL (Structured Query Language). QuaQue leverages a novel condensed algebra to operate on a relational model where versioning information is compactly stored using bitstrings. This approach allows for efficient querying of concurrent versions of knowledge graphs within a standard relational database system. We present the key concepts of our condensed algebra, detail the translation process from SPARQL algebra to SQL, and provide a comparative benchmark against a native RDF (Resource Description Framework) triple store, demonstrating the viability and performance benefits of our approach.

Problem

• versioned knowledge graphs
• concurrent versioning
• efficient querying
• SPARQL
• SQL

Innovation

• condensed algebra
• versioned knowledge graphs
• SPARQL-to-SQL translation
• bitstring encoding
• concurrent versioning