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Author Carlos Boned Riera; Oriol Ramos Terrades edit  doi
  Title Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph Type Conference Article
  Year 2022 Publication 26th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 2186-2191  
  Keywords Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition  
  Abstract Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks.  
  Address Montreal; Quebec; Canada; August 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title (up) Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG; 600.121; 600.162 Approved no  
  Call Number Admin @ si @ BoR2022 Serial 3741  
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