An Unsupervised Approach for Determining Link Specifications

Khayra Bencherif, Mimoun Malki and Djamel Amar Bensaber
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Khayra Bencherif: University of Sidi Bel Abbes, Sidi Bel Abbes, Algeria
Mimoun Malki: Ecole Supérieure en Informatique de Sidi Bel-Abbes (ESI-SBA), LabRI-SBA Laboratoy, Algeria
Djamel Amar Bensaber: Ecole Supérieure en Informatique de Sidi Bel-Abbes, Algeria, Algeria

International Journal of Information Technology and Web Engineering (IJITWE), 2018, vol. 13, issue 4, 104-123

Abstract: This article describes how the Linked Open Data Cloud project allows data providers to publish structured data on the web according to the Linked Data principles. In this context, several link discovery frameworks have been developed for connecting entities contained in knowledge bases. In order to achieve a high effectiveness for the link discovery task, a suitable link configuration is required to specify the similarity conditions. Unfortunately, such configurations are specified manually; which makes the link discovery task tedious and more difficult for the users. In this article, the authors address this drawback by proposing a novel approach for the automatic determination of link specifications. The proposed approach is based on a neural network model to combine a set of existing metrics into a compound one. The authors evaluate the effectiveness of the proposed approach in three experiments using real data sets from the LOD Cloud. In addition, the proposed approach is compared against link specifications approaches to show that it outperforms them in most experiments.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jitwe0:v:13:y:2018:i:4:p:104-123

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