research-article

Experience: Enhancing Address Matching with Geocoding and Similarity Measure Selection

Authors:

Ioannis Koumarelas,

Clifford Mosley,

Felix NaumannAuthors Info & Claims

Journal of Data and Information Quality (JDIQ), Volume 10, Issue 2

Article No.: 8, Pages 1 - 16

https://doi.org/10.1145/3232852

Published: 07 September 2018 Publication History

Abstract

Given a query record, record matching is the problem of finding database records that represent the same real-world object. In the easiest scenario, a database record is completely identical to the query. However, in most cases, problems do arise, for instance, as a result of data errors or data integrated from multiple sources or received from restrictive form fields. These problems are usually difficult, because they require a variety of actions, including field segmentation, decoding of values, and similarity comparisons, each requiring some domain knowledge.

In this article, we study the problem of matching records that contain address information, including attributes such as Street-address and City. To facilitate this matching process, we propose a domain-specific procedure to, first, enrich each record with a more complete representation of the address information through geocoding and reverse-geocoding and, second, to select the best similarity measure per each address attribute that will finally help the classifier to achieve the best f-measure. We report on our experience in selecting geocoding services and discovering similarity measures for a concrete but common industry use-case.

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Cited By

Qiu QZheng STian MLi JMa KTao LXie Z(2024)A deep neural network model for Chinese toponym matching with geographic pre-training modelInternational Journal of Digital Earth10.1080/17538947.2024.235311117:1Online publication date: 13-May-2024
https://doi.org/10.1080/17538947.2024.2353111
De Paoli FCiavotta MAvogadro RHristov EBorukova MPetrova-Antonova DKrasteva I(2024)An interactive approach to semantic enrichment with geospatial dataData & Knowledge Engineering10.1016/j.datak.2024.102341153(102341)Online publication date: Sep-2024
https://doi.org/10.1016/j.datak.2024.102341
Kilic BBayrak OGülgen FGurturk MAbay P(2024)Unveiling the impact of machine learning algorithms on the quality of online geocoding services: a case study using COVID-19 dataJournal of Geographical Systems10.1007/s10109-023-00435-8Online publication date: 25-Jan-2024
https://doi.org/10.1007/s10109-023-00435-8
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Index Terms

Experience: Enhancing Address Matching with Geocoding and Similarity Measure Selection
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
    2. Retrieval tasks and goals
      1. Clustering and classification
      2. Near-duplicate and plagiarism detection
  2. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image Journal of Data and Information Quality

Journal of Data and Information Quality Volume 10, Issue 2

Challenge Papers and Experience Paper

June 2018

31 pages

ISSN:1936-1955

EISSN:1936-1963

DOI:10.1145/3276749

Editor:
Tiziana Catarci
Sapienza University of Rome, Rome, Italy

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2018

Accepted: 01 June 2018

Revised: 01 April 2018

Received: 01 October 2017

Published in JDIQ Volume 10, Issue 2

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Cited By

Qiu QZheng STian MLi JMa KTao LXie Z(2024)A deep neural network model for Chinese toponym matching with geographic pre-training modelInternational Journal of Digital Earth10.1080/17538947.2024.235311117:1Online publication date: 13-May-2024
https://doi.org/10.1080/17538947.2024.2353111
De Paoli FCiavotta MAvogadro RHristov EBorukova MPetrova-Antonova DKrasteva I(2024)An interactive approach to semantic enrichment with geospatial dataData & Knowledge Engineering10.1016/j.datak.2024.102341153(102341)Online publication date: Sep-2024
https://doi.org/10.1016/j.datak.2024.102341
Kilic BBayrak OGülgen FGurturk MAbay P(2024)Unveiling the impact of machine learning algorithms on the quality of online geocoding services: a case study using COVID-19 dataJournal of Geographical Systems10.1007/s10109-023-00435-8Online publication date: 25-Jan-2024
https://doi.org/10.1007/s10109-023-00435-8
Sierra Requena RMartínez-Llario JLorenzo-Sáez EColl-Aliaga E(2023)Development of an Algorithm to Evaluate the Quality of Geolocated Addresses in Urban AreasISPRS International Journal of Geo-Information10.3390/ijgi1210040712:10(407)Online publication date: 4-Oct-2023
https://doi.org/10.3390/ijgi12100407
Krasteva IPetrova-Antonova DPaoli FHristov EBorukova MCiavotta MAvogadro R(2023)Geospatial Enrichment of Urban Data for Advanced City Planning: a Pilot Study2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386822(3139-3143)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386822
Lei JWu CWang Y(2023)CLGLIAM: contrastive learning model based on global and local semantic interaction for address matchingApplied Intelligence10.1007/s10489-023-05089-z53:23(29267-29281)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-05089-z
Soeng SBae JLee KCho W(2022)Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District DataApplied Sciences10.3390/app12211112912:21(11129)Online publication date: 2-Nov-2022
https://doi.org/10.3390/app122111129
Xu LMao RZhang CWang YZheng XXue XXia F(2022)Deep Transfer Learning Model for Semantic Address MatchingApplied Sciences10.3390/app12191011012:19(10110)Online publication date: 8-Oct-2022
https://doi.org/10.3390/app121910110
Su YChang S(2022)Cross-dataset calibration of industrial electricity consumptionEnergy Efficiency10.1007/s12053-022-10020-515:3Online publication date: 17-Feb-2022
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Li FLu YMao XDuan JLiu X(2022)Multi-task deep learning model based on hierarchical relations of address elements for semantic address matchingNeural Computing and Applications10.1007/s00521-022-06914-134:11(8919-8931)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00521-022-06914-1
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