@InProceedings{faralli-EtAl:2017:EACLlong,
  author    = {Faralli, Stefano  and  Panchenko, Alexander  and  Biemann, Chris  and  Ponzetto, Simone Paolo},
  title     = {The ContrastMedium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {590--600},
  abstract  = {In this paper, we present ContrastMedium, an algorithm that transforms noisy
	semantic networks into full-fledged, clean taxonomies. ContrastMedium is able
	to identify the embedded taxonomy structure from a noisy knowledge graph
	without explicit human supervision such as, for instance, a set of manually
	selected input root and leaf concepts. This is achieved by leveraging
	structural information from a companion reference taxonomy, to which the input
	knowledge graph is linked (either automatically or manually). When used in
	conjunction with methods for hypernym acquisition and knowledge base linking,
	our methodology provides a complete solution for end-to-end taxonomy induction.
	We conduct experiments using automatically acquired knowledge graphs, as well
	as a SemEval benchmark, and show that our method is able to achieve high
	performance on the task of taxonomy induction.},
  url       = {http://www.aclweb.org/anthology/E17-1056}
}