Verification of POI and location pairs via weakly labeled web data

Hsiu Min Chuang, Chia Hui Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

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 Thus, effectively identifying outdated or emerging POI relations is an important issue for improving the quality of POI data.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 The preliminary result shows a promising direction for discovering emerging POI and mandates more research for outdated POI.

Original languageEnglish
Title of host publicationWWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages743-748
Number of pages6
ISBN (Electronic)9781450334730
DOIs
StatePublished - 18 May 2015
Event24th International Conference on World Wide Web, WWW 2015 - Florence, Italy
Duration: 18 May 201522 May 2015

Publication series

NameWWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web

Conference

Conference24th International Conference on World Wide Web, WWW 2015
Country/TerritoryItaly
CityFlorence
Period18/05/1522/05/15

Keywords

  • Crowdsourcing
  • Geographic information retrieval
  • Location-based service
  • Semi-supervised learning
  • Weakly labeled data

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