Abstract
In the recent years, there is a significant interest in a link prediction - an important task for graph-based data structures. Although there exist many approaches based on the graph theory and factorizations, there is still lack of methods that can work with multiple types of links and temporal information. The creation time of a link is an important aspect: it reflects age and credibility of the information. In this paper, we introduce a method that predicts missing links in RDF datasets. We model multiple relations of RDF as a tensor that incorporates the creation time of links as a key component too. We evaluate the proposed approach on real world datasets: an RDF representation of the ProgrammableWeb directory and a subset of the DBpedia focused on movies. The results show that the proposed method outperforms other link prediction approaches.
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Acknowledgements
This work was supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS14/104/OHK3/1T/18. We also thank to ProgrammableWeb.com for supporting this research.
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Kuchař, J., Dojchinovski, M., Vitvar, T. (2016). Exploiting Temporal Dimension in Tensor-Based Link Prediction. In: Monfort, V., Krempels, KH., Majchrzak, T.A., Turk, Ž. (eds) Web Information Systems and Technologies. WEBIST 2015. Lecture Notes in Business Information Processing, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-30996-5_11
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DOI: https://doi.org/10.1007/978-3-319-30996-5_11
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