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Prediction of Datasets sameAs Interlinking on Web of Data

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DOI: 10.23977/jwsa.2017.11005 | Downloads: 28 | Views: 5838


Jintao Tang 1, Ting Wang 1, Haichi Liu 1


1 College of Computer, National University of Defense Technology, Changsha, Hunan Province, China

Corresponding Author

Haichi Liu


In order to be considered as Linked Data, the datasets on the web must be linked to other datasets. We focus on predicting the possible links between datasets with the most important RDF link type, owl:sameAs using link prediction and classification techniques. Since the goal is to discriminate between linked dataset pairs against not-linked ones, we formulate the link prediction problem as a classification problem. We adopt Random Forest as the basic classifier to incorporate features of the scores output by unsupervised predictors, and apply the bagging technique to combine multiple forests to reduce variance and improve the accuracy. Experiments show we can improve the prediction performance by about 10% in AUROC compared with the best unsupervised predictor.


Linked data, Dataset, sameAs interlinking, Link Prediction.


Haichi, L. , Ting, W. , Jintao, T. (2017) Prediction of Datasets sameAs Interlinking on Web of Data. Journal of Web Systems and Applications (2017) 1: 25-29.


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