@InProceedings{hernandezgonzalez-hruschkajr-mitchell:2017:TextGraphs-11,
  author    = {Hern\'{a}ndez-Gonz\'{a}lez, Jer\'{o}nimo  and  Hruschka Jr., Estevam R.  and  Mitchell, Tom M.},
  title     = {Merging knowledge bases in different languages},
  booktitle = {Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {21--29},
  abstract  = {Recently, different systems which learn to populate and extend a knowledge base
	(KB) from the web in different languages have been presented. Although a large
	set of concepts should be learnt independently from the language used to read,
	there are facts which are expected to be more easily gathered in local language
	(e.g., culture or geography). A system that merges KBs learnt in different
	languages will benefit from the complementary information as long as common
	beliefs are identified, as well as from redundancy present in web pages written
	in different languages. In this paper, we deal with the problem of identifying
	equivalent beliefs (or concepts) across language specific KBs, assuming that
	they share the same ontology of categories and relations. In a case study with
	two KBs independently learnt from different inputs, namely web pages written in
	English and web pages written in Portuguese respectively, we report on the
	results of two methodologies: an approach based on personalized PageRank and an
	inference technique to find out common relevant paths through the KBs. The
	proposed inference technique efficiently identifies relevant paths,
	outperforming the baseline (a dictionary-based classifier) in the vast majority
	of tested categories.},
  url       = {http://www.aclweb.org/anthology/W17-2403}
}