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
- 2. Dingming Wu
- 3. Nikos Mamoulis
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,482 | Proportionality in Spatial Keyword Search | 2021 | SIGMOD | 4.1905499e-05 |
| 11,660 | Top-k Queries over Digital Traces | 2019 | SIGMOD | 4.1905499e-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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Semantically Similar Papers
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| 11,175 | Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries | 2023 | SIGMOD | 4.1905499e-05 |
| 4,446 | Retrieving Top-k Prestige-Based Relevant Spatial Web Objects | 2010 | VLDB | 6.174662e-05 |
| 7,271 | Exact Top-k Nearest Keyword Search in Large Networks | 2015 | SIGMOD | 4.7764555e-05 |
| 2,020 | Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects | 2009 | VLDB | 9.7766925e-05 |
| 9,057 | Semantic SPARQL Similarity Search Over RDF Knowledge Graphs | 2016 | VLDB | 4.3997447e-05 |