On the efficient execution of bounded Jaro-Winkler distances

Issue title: Ontology and linked data matching

Guest editors: Michelle Cheatham, Isabel F. Cruz, Jérôme Euzenat and Catia Pesquita

Article type: Research Article

Authors: Dreßler, Kevin^{; *} | Ngonga Ngomo, Axel-Cyrille

Affiliations: AKSW Research Group, University of Leipzig, Germany

Correspondence: [*] Corresponding author. E-mail: kvndrsslr@gmail.com.

Abstract: Over the last years, time-efficient approaches for the discovery of links between knowledge bases have been regarded as a key requirement towards implementing the idea of a Data Web. Thus, efficient and effective measures for comparing the labels of resources are central to facilitate the discovery of links between datasets on the Web of Data as well as their integration and fusion. We present a novel time-efficient implementation of filters that allow for the efficient execution of bounded Jaro-Winkler measures. We evaluate our approach on several datasets derived from DBpedia 3.9 and LinkedGeoData and containing up to 106 strings and show that it scales linearly with the size of the data for large thresholds. Moreover, we also show that our approach can be easily implemented in parallel. We also evaluate our approach against SILK and show that we outperform it even on small datasets.

Keywords: Link discovery, string similarity measure

DOI: 10.3233/SW-150209

Journal: Semantic Web, vol. 8, no. 2, pp. 185-196, 2017