{"type":"ScholarlyArticle","title":"KOGNAC: Efficient Encoding of Large Knowledge Graphs","authors":[{"name":"Jacopo Urbani","slug":"jacopo-urbani","given":"Jacopo","family":"Urbani","affiliation":""},{"name":"Sourav Dutta","slug":"sourav-dutta","given":"Sourav","family":"Dutta","affiliation":""},{"name":"Sairam Gurajada","slug":"sairam-gurajada","given":"Sairam","family":"Gurajada","affiliation":""},{"name":"Gerhard Weikum","slug":"gerhard-weikum","given":"Gerhard","family":"Weikum","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2016/548","pdf":"https://ijcai.org/proceedings/2016/0548.pdf","markdown":"https://ijcai.org/proceedings/2016/548.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/548","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":548,"pages":{"first":3896,"last":3901},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}