View source for SERIMI – Resource Description Similarity, RDF Instance Matching and Interlinking

We have loaded all these datasets into an open-source instance of Virtuoso Universal server 10 , where around 2GB of data were loaded. An exception was the DBPedia dataset, which we accessed online via its Sparql endpoint. The Virtuoso server was installed in a Mac OS X – version 10.5.8, with 2.4 GHz Intel Core 2 Duo processor and with 4 GB 1067 MHz DDR3 of memory. We ran the script that implements the SERIMI approach directly over the local SPARQL endpoints and DBPedia online endpoint.

The interlinking of datasets published in the Linked Data Cloud is a challenging problem and a key factor for the success of the Semantic Web. Manual rule-based methods are the most effective solution for the problem, but they require skilled human data publishers going through a laborious, error prone and time-consuming process for manually describing rules mapping instances between two datasets. Thus, an automatic approach for solving this problem is more than welcome. In this paper, we propose a novel interlinking method, SERIMI, for solving this problem automatically. SERIMI matches instances between a source and a target datasets, without prior knowledge of the data, domain or schema of these datasets. Experiments conducted with benchmark collections demonstrate that our approach considerably outperforms state-of-the-art automatic approaches for solving the interlinking problem on the Linked Data Cloud.

As future work, we intend to investigate how our model can be adjusted to consider partial string matching in the similarity function that we proposed, and to accommodate different score distribution metrics as the threshold for the parameter Also, we intend to evaluate this approach in different collections that may provide a more accurate reference alignment than the ones that we used in this work.