2016/04/16 by Jacopo Urbani, Sourav Dutta, Urbani, Jacopo +5 · 1 citation
Computer Science · Biochemistry, Genetics and Molecular Biology · #Semantic Web and Ontologies #Genomics and Phylogenetic Studies #Advanced Graph Neural Networks
paper · pdf · doi:10.48550/arxiv.1604.04795
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.