<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Linking Datasets Using Semantic Textual Similarity

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Linking Datasets Using Semantic Textual Similarity"}

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...

Linking Datasets Using Semantic Textual Similarity

Online veröffentlicht: 30. März 2018

Seitenbereich: 109 - 123

Eingereicht: 29. Sept. 2017

Akzeptiert: 30. Nov. 2017

DOI: https://doi.org/10.2478/cait-2018-0010

Schlüsselwörter
Linked data, link discovery, ontology alignment, semantic textual similarity, structural similarity, NLP architectures

© 2018 John P. McCrae et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Linking Datasets Using Semantic Textual Similarity

Online veröffentlicht: 30. März 2018

Seitenbereich: 109 - 123

Eingereicht: 29. Sept. 2017

Akzeptiert: 30. Nov. 2017

DOI: https://doi.org/10.2478/cait-2018-0010

SchlüsselwörterLinked data, link discovery, ontology alignment, semantic textual similarity, structural similarity, NLP architectures

© 2018 John P. McCrae et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Linked data, link discovery, ontology alignment, semantic textual similarity, structural similarity, NLP architectures