Abstract
In the process of ontology construction, we often need to find relations between entities described by the Resource Description Framework (RDF). Predicting relations between RDF entities is important for developing large-scale ontologies. The goal of our research is to predict a relation (predicate) of two given entities (subject and object). TransE and TransR have been proposed as the methods for such a prediction. We propose a method for predicting a predicate from a subject and an object by using a Deep Neural Network (DNN), and developed RDFDNN. Experimental results showed that predictions by RDFDNN are more accurate than those by TransE and TransR.
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Acknowledgement
This work was supported by Tokyo Tech - Fuji Xerox Cooperative Research (Project Code KY260195), JSPS Grant-in-Aid for Scientific Research (B) (Grant Number 17H01785) and JST CREST (Grant Number JPMJCR1687).
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Murata, T. et al. (2017). Predicting Relations Between RDF Entities by Deep Neural Network. In: Blomqvist, E., Hose, K., Paulheim, H., Ławrynowicz, A., Ciravegna, F., Hartig, O. (eds) The Semantic Web: ESWC 2017 Satellite Events. ESWC 2017. Lecture Notes in Computer Science(), vol 10577. Springer, Cham. https://doi.org/10.1007/978-3-319-70407-4_43
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DOI: https://doi.org/10.1007/978-3-319-70407-4_43
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