demonstration

Spatial Entity Matching with GeoAlign (demo paper)

Authors:

Fabien Duchateau,

Franck Favetta,

Ludovic MonclaAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 580 - 583

https://doi.org/10.1145/3347146.3359345

Published: 05 November 2019 Publication History

Abstract

Points of interest (POI) are central in many applications such as tourism, itinerary search, crisis management. Cartographic providers usually represent these POI with a spatial entity. However, the description of these entities may significantly vary from one provider to another (e.g., missing properties, outdated information, conflicting values). Spatial entity matching (or record linkage) aims at detecting correspondences between entities referring to the same POI. Most existing approaches have a fixed function for combining similarity measures, thus limiting customization. Besides, evaluating the matching quality is a difficult task since a ground truth dataset cannot be built for all entities and providers. In this paper, we describe GeoAlign, an application that allows fine-grained tuning for spatial entity matching. A merging step is also provided using different strategies. Finally, we propose to estimate the quality of correspondences based on the differences between combination functions and to visualize this estimation in GeoAlign.

References

[1]

Manel Achichi, Michelle Cheatham, Zlatan Dragisic, Jérôme Euzenat, Daniel Faria, Alfio Ferrara, Giorgos Flouris, Irini Fundulaki, Ian Harrow, Valentina Ivanova, et al. 2017. Results of the ontology alignment evaluation initiative 2017. In Workshop on Ontology Matching co-located with ISWC, Vol. 2032. CEUR-WS, 61--113.

[2]

Spiros Athanasiou, Giorgos Giannopoulos, Damien Graux, Nikos Karagiannakis, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Kostas Patroumpas, Mohamed Ahmed Sherif, and Dimitrios Skoutas. 2019. Big POI data integration with Linked Data technologies. In EDBT. 477--488.

[3]

Jens Bleiholder and Felix Naumann. 2009. Data fusion. ACM Computing Surveys (CSUR) 41, 1 (2009), 1.

Digital Library

[4]

Léonor Ferrer Catala, Franck Favetta, Claire Cunty, Bilal Berjawi, Fabien Duchateau, Maryvonne Miquel, and Robert Laurini. 2016. Visualizing Integration Uncertainty Enhances User's Choice in Multi-Providers Integrated Maps. In Advanced Visual Interfaces (AVI '16). ACM, 298--299. https://doi.org/10.1145/2909132.2926075

Digital Library

[5]

Błażej Ciepluch, Ricky Jacob, Peter Mooney, and Adam C Winstanley. 2010. Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps. In Symposium on Spatial Accuracy Assessment in Natural Resources and Enviromental Sciences. University of Leicester, 337.

[6]

Thomas Devogele, Christine Parent, and Stefano Spaccapietra. 1998. On spatial database integration. International Journal of Geographical Information Science 12, 4 (1998), 335--352.

[7]

Cidália Costa Fonte, Vyron Antoniou, Lucy Bastin, Jacinto Estima, Jamal Jokar Ar-sanjani, Juan-Carlos Laso Bayas, Linda See, and Rumiana Vatseva. 2017. Assessing VGI data quality. Mapping and the citizen sensor (2017), 137--163.

[8]

Hyunmo Kang, Vivek Sehgal, and Lise Getoor. 2007. GeoDDupe: A Novel Interface for Interactive Entity Resolution in Geospatial Data. In International Conference on Information Visualisation. 489--496.

[9]

Hanna Köpcke and Erhard Rahm. 2010. Frameworks for entity matching: A comparison. Data & Knowledge Engineering 69, 2 (2010), 197--210.

Digital Library

[10]

George Lamprianidis, Dimitrios Skoutas, George Papatheodorou, and Dieter Pfoser. 2014. Extraction, Integration and Analysis of Crowdsourced Points of Interest from Multiple Web Sources. In ACM SIGSPATIAL (GeoCrowd '14). ACM, 16--23. https://doi.org/10.1145/2676440.2676445

Digital Library

[11]

Grant McKenzie, Krzysztof Janowicz, and Benjamin Adams. 2013. Weighted Multi-attribute Matching of User-generated Points of Interest. In ACM SIGSPATIAL. ACM, 440--443. https://doi.org/10.1145/2525314.2525455

[12]

Anthony Morana, Thomas Morel, Bilal Berjawi, and Fabien Duchateau. 2014. GeoBench: a Geospatial Integration Tool for Building a Spatial Entity Matching Benchmark. In ACM SIGSPATIAL. ACM, 533--536.

[13]

AM Olteanu. 2007. A multi-criteria fusion approach for geographical data matching. International Symposium in Spatial Data Quality (2007).

[14]

Philip Resnik. 1999. Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research 11 (1999), 95--130.

Digital Library

[15]

Alieh Saeedi, Eric Peukert, and Erhard Rahm. 2017. Comparative evaluation of distributed clustering schemes for multi-source entity resolution. In European Conference on Advances in Databases and Information Systems. Springer, 278--293.

[16]

Vivek Sehgal, Lise Getoor, and Peter Viechnicki. 2006. Entity resolution in geospatial data integration. In GIS, Rolf A. de By and Silvia Nittel (Eds.). ACM, 83--90. http://dblp.uni-trier.de/db/conf/gis/gis2006.html#SehgalGV06

[17]

Pavel Shvaiko and Jérôme Euzenat. 2005. A survey of schema-based matching approaches. In Journal on data semantics IV. Springer, 146--171.

Cited By

Pyo JChiang Y(2024)Leveraging Large Language Models for Generating Labeled Mineral Site Record Linkage DataProceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3687123.3698298(86-98)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3687123.3698298
Duchateau FFavetta F(2023)Knowledge Management at Multiple Decision Levels: A Use Case About COVID-19 PandemicKnowledge Management for Regional Policymaking10.1007/978-3-031-15648-9_4(61-88)Online publication date: 2-Jan-2023
https://doi.org/10.1007/978-3-031-15648-9_4
Shah SMeduri VSarwat M(2021)GEMProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483973(346-349)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3474717.3483973
Show More Cited By

Index Terms

Spatial Entity Matching with GeoAlign (demo paper)
1. General and reference
  1. Cross-computing tools and techniques
    1. Evaluation
2. Information systems
  1. Data management systems
    1. Information integration
  2. Information systems applications
    1. Spatial-temporal systems

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Information

Published In

cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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Author Tags

Qualifiers

Demonstration
Research
Refereed limited

Funding Sources

Labex IMU (Intelligences des Mondes Urbains)

Conference

SIGSPATIAL '19

Sponsor:

SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

Acceptance Rates

SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Pyo JChiang Y(2024)Leveraging Large Language Models for Generating Labeled Mineral Site Record Linkage DataProceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3687123.3698298(86-98)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3687123.3698298
Duchateau FFavetta F(2023)Knowledge Management at Multiple Decision Levels: A Use Case About COVID-19 PandemicKnowledge Management for Regional Policymaking10.1007/978-3-031-15648-9_4(61-88)Online publication date: 2-Jan-2023
https://doi.org/10.1007/978-3-031-15648-9_4
Shah SMeduri VSarwat M(2021)GEMProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483973(346-349)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3474717.3483973
Barret NDuchateau FFavetta FGentil ABonneval L(2021)An Environmental Study of French NeighbourhoodsData Management Technologies and Applications10.1007/978-3-030-83014-4_13(267-292)Online publication date: 23-Jul-2021
https://doi.org/10.1007/978-3-030-83014-4_13

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