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Retrieving Landmark Salience Based on Wikipedia: An Integrated Ranking Model

Binski, N; Natapov, A; Dalyot, S; (2019) Retrieving Landmark Salience Based on Wikipedia: An Integrated Ranking Model. ISPRS International Journal of Geo-Information , 8 (12) , Article 529. 10.3390/ijgi8120529. Green open access

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Abstract

Landmarks are important for assisting in wayfinding and navigation and for enriching user experience. Although many user-generated geotagged sources exist, landmark entities are still mostly retrieved from authoritative geographic sources. Wikipedia, the world’s largest free encyclopedia, stores geotagged information on many geospatial entities, including a very large and well-founded volume of landmark information. However, not all Wikipedia geotagged landmark entities can be considered valuable and instructive. This research introduces an integrated ranking model for mining landmarks from Wikipedia predicated on estimating and weighting their salience. Other than location, the model is based on the entries’ category and attributed data. Preliminary ranking is formulated on the basis of three spatial descriptors associated with landmark salience, namely permanence, visibility, and uniqueness. This ranking is integrated with a score derived from a set of numerical attributes that are associated with public interest in the Wikipedia page―including the number of redirects and the date of the latest edit. The methodology is comparatively evaluated for various areas in different cities. Results show that the developed integrated ranking model is robust in identifying landmark salience, paving the way for incorporation of Wikipedia’s content into navigation systems.

Type: Article
Title: Retrieving Landmark Salience Based on Wikipedia: An Integrated Ranking Model
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/ijgi8120529
Publisher version: https://doi.org/10.3390/ijgi8120529
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Wikipedia; landmark salience; spatial cognition; data mining; user-generated content
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10088645
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