Abstract
Linked Data are data of directed graph with labels to describe based on Resource Description Framework (RDF), and can create a knowledge base by linking each resource on the Web. However, a large amount of Linked Data does not have enough links since resources defined at Internationalized Resource Identifier (IRI) data type are scanty. Therefore, this paper presents that Resource Propagation Algorithm predicts links between resources in Linked Data based on semantic distance, and reinforces the knowledge base. The algorithm was confirmed that it was able to generate semantic links between the resources considering predicate dictionary.
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Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 17J09765 for Resource Fellowship for Young Scientists.
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Maki, T., Takahashi, K., Wakahara, T., Kodate, A., Sonehara, N. (2018). Resource Propagation Algorithm to Reinforce Knowledge Base in Linked Data. In: Barolli, L., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-65521-5_41
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DOI: https://doi.org/10.1007/978-3-319-65521-5_41
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