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Automated feature generation from structured knowledge
2011
Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11
The prediction accuracy of any learning algorithm highly depends on the quality of the selected features; but often, the task of feature construction and selection is tedious and nonscalable. In recent years, however, there have been numerous projects with the goal of constructing general-purpose or domain-specific knowledge bases with entity-relationshipentity triples extracted from various Web sources or collected from user communities, e.g., YAGO, DBpedia, Freebase, UMLS, etc. This paper
doi:10.1145/2063576.2063779
dblp:conf/cikm/ChengKGSH11
fatcat:skherxpctfd4bgfipgqbpin4b4