Top-k relevant semantic place retrieval on spatial RDF data

Jieming Shi, Dingming Wu, Nikos Mamoulis

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

15 Citations (Scopus)

Abstract

RDF data are traditionally accessed using structured query languages, such as SPARQL. However, this requires users to understand the language as well as the RDF schema. Keyword search on RDF data aims at relieving the user from these requirements; the user only inputs a set of keywords and the goal is to find small RDF subgraphs which contain all keywords. At the same time, popular RDF knowledge bases also include spatial semantics, which opens the road to location-based search operations. In this work, we propose and study a novel location-based keyword search query on RDF data. The objective of top-κ relevant semantic places (κSP) retrieval is to find RDF subgraphs which contain the query keywords and are rooted at spatial entities close to the query location. The novelty of κSP queries is that they are location-aware and that they do not rely on the use of structured query languages. We design a basic method for the processing of κSP queries. To further accelerate κSP retrieval, two pruning approaches and a data preprocessing technique are proposed. Extensive empirical studies on two real datasets demonstrate the superior and robust performance of our proposals compared to the basic method.

Original languageEnglish
Title of host publicationSIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1977-1990
Number of pages14
ISBN (Electronic)9781450335317
DOIs
Publication statusPublished - 26 Jun 2016
Externally publishedYes
Event2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
Volume26-June-2016
ISSN (Print)0730-8078

Conference

Conference2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
Country/TerritoryUnited States
CitySan Francisco
Period26/06/161/07/16

ASJC Scopus subject areas

  • Software
  • Information Systems

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