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Author Carlos Boned Riera; Oriol Ramos Terrades
Title Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph Type Conference Article
Year (down) 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
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Area Expedition Conference ICPR
Notes DAG; 600.121; 600.162 Approved no
Call Number Admin @ si @ BoR2022 Serial 3741
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