EAGLE: Efficient Active Learning of Link Specifications Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/esws/NgomoL12,
  author =       "Axel-Cyrille {Ngonga Ngomo} and Klaus Lyko",
  title =        "{EAGLE}: Efficient Active Learning of Link
                 Specifications Using Genetic Programming",
  booktitle =    "Proceedings of the 9th Extended Semantic Web
                 Conference (ESWC 2012)",
  year =         "2012",
  editor =       "Elena Simperl and Philipp Cimiano and 
                 Axel Polleres and Oscar Corcho and Valentina Presutti",
  volume =       "7295",
  series =       "Lecture Notes in Computer Science",
  pages =        "149--163",
  address =      "Heraklion, Crete, Greece",
  month =        may # " 27-31",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-30283-1",
  DOI =          "doi:10.1007/978-3-642-30284-8_17",
  size =         "15 pages",
  abstract =     "With the growth of the Linked Data Web, time-efficient
                 approaches for computing links between data sources
                 have become indispensable. Most Link Discovery
                 frameworks implement approaches that require two main
                 computational steps. First, a link specification has to
                 be explicated by the user. Then, this specification
                 must be executed. While several approaches for the
                 time-efficient execution of link specifications have
                 been developed over the last few years, the discovery
                 of accurate link specifications remains a tedious
                 problem. In this paper, we present EAGLE, an active
                 learning approach based on genetic programming. EAGLE
                 generates highly accurate link specifications while
                 reducing the annotation burden for the user. We
                 evaluate EAGLE against batch learning on three
                 different data sets and show that our algorithm can
                 detect specifications with an F-measure superior to
                 90percent while requiring a small number of
                 questions.",
  notes =        "The Semantic Web: Research and Applications",
  affiliation =  "Department of Computer Science, University of Leipzig,
                 Johannisgasse 26, 04103 Leipzig, Germany",
  bibdate =      "2012-05-24",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/esws/eswc2012.html#NgomoL12",
}

Genetic Programming entries for Axel-Cyrille Ngonga Ngomo Klaus Lyko

Citations