Type inference through the analysis of wikipedia links
|Type inference through the analysis of wikipedia links|
|Author(s)||Nuzzolese A.G., Gangemi A., Presutti V., Ciancarini P.|
|Published in||CEUR Workshop Proceedings|
|Keyword(s)||Unknown (Extra: Dbpedia, Link structure, Machine learning techniques, Type inferences, Wikipedia, Learning systems, Data handling)|
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Type inference through the analysis of wikipedia links is a 2012 conference paper written in English by Nuzzolese A.G., Gangemi A., Presutti V., Ciancarini P. and published in CEUR Workshop Proceedings.
DBpedia contains millions of untyped entities, either if we consider the native DBpedia ontology, or Yago plus Word- Net. Is it possible to automatically classify those entities? Based on previous work on wikilink invariances, we wondered if wikilinks convey a knowledge rich enough for their classification. In this paper we give three contributions. Concerning the DBpedia link structure, we describe some measurements and notice both problems (e.g. the bias that could be induced by the incomplete ontological coverage of the DBpedia ontology), and potentials existing in current type coverage. Concerning classification, we present two techniques that exploit wikilinks, one based on induction from machine learning techniques, and the other on abducfition. Finally, we discuss the limited results of classification, which confrmed our fears expressed in the description of general figures from the measurement. We also suggest some new possible directions to entity classification that could be taken.
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