Views: a hardware-friendly graph database model for storing semantic information

TL;DR

This paper introduces Views, a hardware-optimized graph database model designed for efficient storage, retrieval, and symbolic reasoning, addressing current limitations in hardware acceleration for graph data management.

Contribution

The paper presents a novel hardware-friendly graph database model called Views, with a specialized data structure that improves storage efficiency and supports semantic reasoning tasks.

Findings

Views achieves better storage performance than traditional models.

It supports effective semantic reasoning and cognitive modeling.

The model demonstrates functional equivalence with existing graph representations.

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 functional equivalence and storage performance advantage compared 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.