Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph

Carlos Boned Riera, Oriol Ramos Terrades*

*Corresponding author for this work

Research output: Book/ReportProceedingResearchpeer-review


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.

Original languageEnglish
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665490627
Publication statusPublished - 2022

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


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