Top-k Relevant Semantic Place Retrieval on Spatial RDF Data
Summary: Introduces top-k relevant semantic places kSP retrieval for spatial RDF: keyword search returns subgraphs anchored to nearby spatial entities. No SPARQL; adds pruning methods and preprocessing; experiments show robust, superior performance. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jieming Shi (University of Hong Kong)
- 2. Dingming Wu (Shenzhen University)
- 3. Nikos Mamoulis (University of Ioannina)
BibTeX Citation
@inproceedings{shi_sigmod16,
title = {{Top-k Relevant Semantic Place Retrieval on Spatial RDF Data}},
author = {Shi, Jieming and Wu, Dingming and Mamoulis, Nikos},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2882941},
url = {https://dl.acm.org/doi/10.1145/2882903.2882941},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,676 | Proportionality in Spatial Keyword Search | 2021 | SIGMOD | 5.093636e-05 |
| 11,850 | Top-k Queries over Digital Traces | 2019 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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