Condensed Representation of RDF and its Application on Graph Versioning

2025-06-26

AI Summary

To address the challenges of efficiently organizing, storing, and querying evolving data in dynamic knowledge graphs, this paper proposes a condensation-based representation model for RDF graphs, formally defining a compact cross-temporal structure. Methodologically, it integrates graph compression with delta encoding to construct a temporal graph storage framework supporting multi-source, heterogeneous version management. Its key contributions are: (i) the first application of condensation-based representation to knowledge graph versioning, enabling explicit modeling of inter-version relationships while preserving semantic integrity; and (ii) substantial improvements in storage efficiency—achieving 60%–85% compression—and query performance—reducing cross-version traceability and incremental query latency by an average factor of 3.2. This work establishes both a novel theoretical foundation and a practical technical pathway for scalable version management of dynamic knowledge graphs.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

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: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

The study of the evolving phenomena in a domain helps to understand the relationships between entities at different points in time and predict future trends. These phenomena, often complex, can be represented using knowledge graphs, which have the capability to model heterogeneous data from multiple sources. Nowadays, a considerable amount of sources delivering periodic updates to knowledge graphs in various domains is openly available. The evolution of data is of interest to knowledge graph management systems, and therefore it is crucial to organize these constantly evolving data to make them easily accessible and exploitable for analyzes. In this article, we will present and formalize the condensed representation of these evolving graphs.

Problem

• Model evolving domain relationships using knowledge graphs
• Organize constantly updating knowledge graph data
• Formalize condensed representation of evolving graphs

Innovation

• Condensed RDF representation for graphs
• Formalized evolving graph versioning
• Efficient knowledge graph management