# KOGNAC: Efficient Encoding of Large Knowledge Graphs

arXiv: https://arxiv.org/abs/1604.04795 · HTML: https://mlsanity.com/paper/1604.04795/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases](https://mlsanity.com/paper/2108.08597/) (2021)
- [Neural Graph Databases](https://mlsanity.com/paper/2209.09732/) (2022)
- [Higher-Order Graph Databases](https://mlsanity.com/paper/2506.19661/) (2025)

## Abstract (arXiv)

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.