KOGNAC: Efficient Encoding of Large Knowledge Graphs
This addresses query efficiency for web applications using large Knowledge Graphs, representing an incremental improvement through a hybrid encoding method.
The paper tackles the problem of efficiently querying large Knowledge Graphs by proposing KOGNAC, a dictionary-encoding algorithm that combines statistical and semantic techniques, resulting in significant improvements in SPARQL querying on graphs with up to 1 billion edges.
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.