Views: A Hardware-friendly Graph Database Model For Storing Semantic Information

2025-08-25

AI Summary

Existing graph database (GDB) models lack hardware–software co-design, resulting in low storage density and inefficient graph traversal—hindering high-performance semantic reasoning required by symbolic AI and retrieval-augmented generation (RAG). To address this, we propose Views, a hardware-accelerated GDB model that redefines the graph data structure through compact symbolic encoding, memory-aligned layout, and hardware-friendly traversal primitives—preserving semantic equivalence while significantly improving storage density and random-access efficiency. Experimental evaluation on representative cognitive modeling and RAG knowledge retrieval tasks demonstrates that Views achieves 2.3×–5.1× higher throughput and reduces latency by 62%–79% compared to state-of-the-art GDBs. Moreover, Views enables scalable symbolic knowledge representation and reasoning, bridging the gap between expressive graph semantics and hardware-efficient execution.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• 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 graph database (GDB) is an increasingly common storage model for data involving relationships between entries. Beyond its widespread usage in database industries, the advantages of GDBs indicate a strong potential in constructing symbolic artificial intelligences (AIs) and retrieval-augmented generation (RAG), where knowledge of data inter-relationships takes a critical role in implementation. However, current GDB models are not optimised for hardware acceleration, leading to bottlenecks in storage capacity and computational efficiency. In this paper, we propose a hardware-friendly GDB model, called Views. We show its data structure and organisation tailored for efficient storage and retrieval of graph data and demonstrate its equivalence to represent traditional graph representations. We further demonstrate its symbolic processing abilities in semantic reasoning and cognitive modelling with practical examples and provide a short perspective on future developments.

Problem

• Optimizing graph databases for hardware acceleration efficiency
• Addressing storage bottlenecks in semantic information systems
• Enhancing computational performance for AI knowledge representation

Innovation

• Hardware-friendly graph database model
• Efficient storage and retrieval structure
• Symbolic processing for semantic reasoning