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Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semi-supervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.

SAIM – One Step Closer to Zero-Configuration Link Discovery