Name
Title | ||
|---|---|---|
Spatial Keyword Query of Region-Of-Interest Based on the Distributed Representation of Point-Of-Interest. |
Abstract | ||
|---|---|---|
The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people's daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user's requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/ijgi8060287 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION |
Keywords | Field | DocType |
ROI exploration,spatial keyword search,distributed representation,environment semantics,deep learning | Information retrieval,Computer science,Information technology,Artificial intelligence,Deep learning,Word2vec,Point of interest,Region of interest,Pound (mass),Distributed representation,Semantics | Journal |
Volume | Issue | Citations |
8 | 6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangdian Zhu | 1 | 0 | 0.34 |
| Ye Wu | 2 | 0 | 0.68 |
| Luo Chen | 3 | 44 | 16.16 |
| Ning Jing | 4 | 75 | 20.54 |