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

• Autorzy: Hamel Oussama , Fareh Messaouda

Identyfikatory

DOI

10.15439/2022F209

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

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.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 733--739
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Hamel Oussama

autor

Fareh Messaouda