Machine Learning to Improve Retrieval by Category in Big Volunteered Geodata
Nowadays, Volunteered Geographic Information (VGI) is commonly used in research and practical applications. However, the quality assurance of such a geographic data remains a problem. In this study we use machine learning and natural language processing to improve record retrieval by category (e.g. restaurant, museum, etc.) from Wikimapia Points of Interest data. We use textual information contained in VGI records to evaluate its ability to determine the category label. The performance of the trained classifier is evaluated on the complete dataset and then is compared with its performance on regional subsets. Preliminary analysis shows significant difference in the classifier performance across the regions. Such geographic differences will have a significant effect on data enrichment efforts such as labeling entities with missing categories.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1490594
- Resource Relation:
- Conference: ACM SIGSPATIAL 2018: 12th Workshop on Geographic Information Retrieval - Seattle, Washington, United States of America - 11/6/2018 3:00:00 PM-11/6/2018 3:00:00 PM
- Country of Publication:
- United States
- Language:
- English
Ambiguity and plausibility
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conference | November 2014 |
Assuring the quality of volunteered geographic information
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journal | May 2012 |
A review of volunteered geographic information quality assessment methods
|
journal | May 2016 |
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