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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Encoder-Decoder Neural Network with Attention Mechanism for Types Detection in Linked Data

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DOI: http://dx.doi.org/10.15439/2022F209

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 733–739 ()

Full text

Abstract. With the emergence of use of Linked Data in different application domains, several problems have arisen, such as data incompleteness. Type detection for entities in RDFdata is one of the most important tasks in dealing with the incompleteness of Linked Data. In this paper, we propose an approach based on Deep Learning techniques, using an encoder-decoder model with attention mechanism, embedding layer to extract the features of each subject from the RDF triples and the GRU cells to address the problem of vanishing. We use the DBpedia dataset for the training and test phases. Initial test results showed the effectiveness of our model.

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