KOGNAC: Efficient Encoding of Large Knowledge Graphs

TL;DR

KOGNAC is a novel encoding algorithm that combines statistical and semantic techniques to efficiently compress and query large Knowledge Graphs, significantly enhancing SPARQL performance on billion-edge datasets.

Contribution

It introduces a hybrid encoding method that detects frequent terms and groups infrequent ones semantically, improving compression and query efficiency for large KGs.

Findings

Significant SPARQL query performance improvements on large KGs.

Effective compression of large-scale Knowledge Graphs.

Compatibility with state-of-the-art RDF engines.

Abstract

Many Web applications require efficient querying of large Knowledge Graphs (KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques. In KOGNAC, frequent terms are detected with a frequency approximation algorithm and encoded to maximise compression. Infrequent terms are semantically grouped into ontological classes and encoded to increase data locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines, and observed that it significantly improves SPARQL querying on KGs with up to 1B edges.