Linking Datasets Using Semantic Textual Similarity

Open access

Abstract

Linked data has been widely recognized as an important paradigm for representing data and one of the most important aspects of supporting its use is discovery of links between datasets. For many datasets, there is a significant amount of textual information in the form of labels, descriptions and documentation about the elements of the dataset and the fundament of a precise linking is in the application of semantic textual similarity to link these datasets. However, most linking tools so far rely on only simple string similarity metrics such as Jaccard scores. We present an evaluation of some metrics that have performed well in recent semantic textual similarity evaluations and apply these to linking existing datasets.

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CiteScore 2018: 0.84

SCImago Journal Rank (SJR) 2018: 0.215
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