Place2Vec: Visualizing and Reasoning About Place Type Similarity and Relatedness by Learning Context Embeddings

Open access
Date
2018-01-15Type
- Conference Poster
ETH Bibliography
no
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Abstract
Understanding, representing, and reasoning about points of interest (POI) types is a key aspect of geographic information retrieval, location-based services, and knowledge graphs. POI-type similarity and relatedness are important for query expansion if there is no direct-matched candidate for a query. Current place-hierarchy representation is mainly derived from a top-down expert design perspective which may not capture holistic geospatial semantics from real-world POI datasets. In this demo, we illustrate how to learn the POI-type embedding representations from spatial contexts and a data-driven perspective, and to visualize the corresponding POI-type similarity and relatedness from such embeddings. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000225625Publication status
publishedBook title
Adjunct Proceedings of the 14th International Conference on Location Based ServicesPages / Article No.
Publisher
ETH ZurichEvent
Subject
Points of Interest; Similarity; Geospatial SemanticsRelated publications and datasets
Is part of: https://doi.org/10.3929/ethz-b-000224043
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ETH Bibliography
no
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