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  • Manusc_Combining_Multiple_String_Similarity_Metrics_for_Effective_Toponym_Matching

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Digital Earth on 06/09/2017, available online: http://www.tandfonline.com/doi/full/10.1080/17538947.2017.1371253

    Accepted author manuscript, 490 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Learning to combine multiple string similarity metrics for effective toponym matching

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>6/09/2017
<mark>Journal</mark>International Journal of Digital Earth
Number of pages26
Publication StatusE-pub ahead of print
Early online date6/09/17
<mark>Original language</mark>English

Abstract

Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching, that is, the problem of matching place names that share a common referent. In this article, we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task. We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics, which has the natural advantage of avoiding the manual tuning of similarity thresholds. Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small, and that carefully tuning the similarity threshold is important for achieving good results. The methods based on supervised machine learning, particularly when considering ensembles of decision trees, can achieve good results on this task, significantly outperforming the individual similarity metrics.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Digital Earth on 06/09/2017, available online: http://www.tandfonline.com/doi/full/10.1080/17538947.2017.1371253