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

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 RDF data is one of the most important tasks in dealing with the incompleteness of Linked Data. In this paper, we propose an approach ba...

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Vydáno v:2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS) Ročník 30; s. 733 - 739
Hlavní autoři: Hamel, Oussama, Fareh, Messaouda
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: Polish Information Processing Society 01.01.2022
Témata:
Biological system modeling
Feature extraction
Linked data
Neural networks
Resource description framework
Semantics
Training
ISSN:2300-5963
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Shrnutí: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 RDF data 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.
ISSN:2300-5963
DOI:10.15439/2022F209