Abstract
Due to the outstanding role of owl:sameAs as the most widely used linking predicate, the problem of identifying potential Linked Data datasets for sameAs interlinking was studied in this paper. The problem was regarded as a Recommender systems problem, so serveral classical collaborative filtering techniques were employed. The user-item matrix was constructed with rating values defined depending on the number of owl:sameAs RDF links between datasets from Linked Open Data Cloud 2014 dump. The similarity measure is a key for memory-based collaborative filtering methods, a novel dataset semantic similarity measure was proposed based on the vocabulary information extracted from datasets. We conducted experiments to evaluate the accuracy of both the predicted ratings and recommended datasets lists of these recommenders. The experiments demonstrated that our customized recommenders out-performed the original ones with a great deal, and achieved much better metrics in both evaluations.
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This material is based on work supported by the National Natural Science Foundation of China (61200337, 61202118, 61472436)
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Liu, H. et al. (2016). Identifying Linked Data Datasets for sameAs Interlinking Using Recommendation Techniques. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_23
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DOI: https://doi.org/10.1007/978-3-319-39937-9_23
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