Hardware Acceleration for Knowledge Graph Processing: Challenges & Recent Developments

TL;DR

This paper systematically reviews how heterogeneous hardware accelerates knowledge graph processing, highlighting recent developments, classifications, and future research directions to enhance efficiency in KG applications.

Contribution

It provides a comprehensive classification and analysis of hardware acceleration techniques for knowledge graphs, identifying research gaps and future directions.

Findings

Classification of hardware acceleration methods for KGs

Analysis of recent hardware-based KG schemes

Identification of research gaps and future trends

Abstract

Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in the area of the Semantic Web as well as gaining popularity in other application domains such as data mining and search engines. Simultaneously, there has been enormous progress in the development of different types of heterogeneous hardware, impacting the way KGs are processed. The aim of this paper is to provide a systematic literature review of knowledge graph hardware acceleration. For this, we present a classification of the primary areas in knowledge graph technology that harnesses different hardware units for accelerating certain knowledge graph functionalities. We then extensively describe respective works, focusing on how KG related schemes harness modern hardware accelerators. Based on our review, we identify various research gaps and future exploratory directions that are anticipated to be of significant value both for academics and industry practitioners.