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Verification of POI and Location Pairs via Weakly Labeled Web Data

Published: 18 May 2015 Publication History

Abstract

With the increased popularity of mobile devices and smart phones, location-based services (LBS) have become a common need in our daily life. Therefore, maintaining the correctness of POI (Points of Interest) data has become an important issue for many location-based services such as Google Maps and Garmin navigation systems. The simplest form of POI contains a location (e.g., represented by an address) and an identifier (e.g., an organization name) that describes the location. As time goes by, the POI relationship of a location and organization pair may change due to the opening, moving, or closing of a business. Thus, effectively identifying outdated or emerging POI relations is an important issue for improving the quality of POI data. In this paper, we examine the possibility of using location-related pages on the Web to verify existing POI relations via weakly labeled data, e.g., the co-occurrence of an organization and an address in Web pages, the published date of such pages, and the pairing diversity of an address or an organization, etc. The preliminary result shows a promising direction for discovering emerging POI and mandates more research for outdated POI.

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  1. Verification of POI and Location Pairs via Weakly Labeled Web Data

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    WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
    May 2015
    1602 pages
    ISBN:9781450334730
    DOI:10.1145/2740908

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    Published: 18 May 2015

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    Author Tags

    1. crowdsourcing
    2. geographic information retrieval
    3. location-based service
    4. semi-supervised learning
    5. weakly labeled data

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    • (2022)DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu MapsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557097(3063-3071)Online publication date: 17-Oct-2022
    • (2021)A Latent Customer Flow Model for Interpretable Predictions of Check-In Counts2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671946(529-539)Online publication date: 15-Dec-2021
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    • (2018)POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas StationISPRS International Journal of Geo-Information10.3390/ijgi70501787:5(178)Online publication date: 8-May-2018
    • (2018)Detecting outdated POI relations via web‐derived featuresTransactions in GIS10.1111/tgis.1246122:5(1238-1256)Online publication date: 17-Sep-2018
    • (2016)Report on the Fifth International Workshop on Location and the Web (LocWeb 2015)ACM SIGIR Forum10.1145/2888422.288844249:2(123-128)Online publication date: 29-Jan-2016
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