---
title: "KOGNAC: Efficient Encoding of Large Knowledge Graphs"
authors:
  - "Jacopo Urbani"
  - "Sourav Dutta"
  - "Sairam Gurajada"
  - "Gerhard Weikum"
doi: ""
url: "https://ijcai.org/proceedings/2016/548"
pdf: "https://ijcai.org/proceedings/2016/0548.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3896-3901"
track: ""
keywords:
---

# KOGNAC: Efficient Encoding of Large Knowledge Graphs

Jacopo Urbani, Sourav Dutta, Sairam Gurajada, Gerhard Weikum

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3896-3901.

## 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.