@techreport{TR-IC-PFG-20-03,
   number = {IC-PFG-20-03},
   author = {Victor Eiti Yamamoto and Julio Cesar dos Reis},
   title  =  {{Alignment of Knowledge Graphs based on Learning to Rank
                   techniques}},
   month = {August},
   year = {2020},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 20 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Knowledge  graphs  (KGs)  define  facts  expressed  as  triples 
       considering subject, predicate and object in the representation
       of  knowledge.  Usually, several knowledge graphs are published
       in a given domain. It is relevant to create alignments both for
       classes  that  model  concepts  and  between instances of those
       classes defined in different knowledge graphs. In this work, we
       study  techniques  for  aligning entities expressed in KGs. Our
       solution  explores supervised ranking aggregation method in the
       alignment  based  on similarity values. Our experiments rely on
       the  dataset  from  the  \textit{Ontology  Alignment Evaluation
       Initiative}  to  evaluate  the  proposed method in experimental
       analyzes.
  }
}