Toponym matching through deep neural networks

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that c...

Full description

Saved in:
Bibliographic Details
Published in:International journal of geographical information science : IJGIS 2018-02, Vol.32 (2), p.324-348
Main Authors: Santos, Rui, Murrieta-Flores, Patricia, Calado, Pável, Martins, Bruno
Format: Article
Language:English
Subjects:
approximate string matching
Artificial neural networks
deep neural networks
duplicate detection
geographic information retrieval
Learning algorithms
Machine learning
Neural networks
Place names
recurrent neural networks
Representations
Similarity
String matching
Strings
Toponym matching
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.
ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2017.1390119