# Compressed Indexes for Fast Search of Semantic Data

## TL;DR

This paper introduces a trie-based compressed index for RDF data that significantly reduces space and accelerates query processing, enabling efficient handling of large-scale semantic datasets.

## Contribution

It presents a novel trie-based index layout with two techniques for space reduction, outperforming existing solutions in RDF triple indexing.

## Key findings

- Achieves 30-60% less space usage
- Speeds up query execution by 2-81x
- Outperforms state-of-the-art solutions

## Abstract

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60% less space and speeding up query execution by a factor of 2-81x.