Semantic Region Retrieval from Spatial RDF Data

Dingming Wu, Can Hou, Erjia Xiao, Christian S. Jensen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


The top-k most relevant Semantic Place retrieval (kSP) query on spatial RDF data combines keyword-based and location-based retrieval. The query returns semantic places that are subgraphs rooted at a place entity with an associated location. The relevance to the query keywords of a semantic place is measured by a looseness score that aggregates the graph distances between the place (root) and the occurrences of the keywords in the nodes of the tree. We observe that kSP queries may retrieve semantic places that are spatially close to the query location, but with very low keyword relevance. When any single nearby place has low relevance, returning instead multiple relevant places maybe helpful. Hence, we propose a generalization of semantic place retrieval, namely semantic region (SR) retrieval. An SR query aims to return multiple places that are spatially close to the query location such that each place is relevant to one or more query keywords. An algorithm and optimization techniques are proposed for the efficient processing of SR queries. Extensive empirical studies with two real datasets offer insight into the performance of the proposals.

Original languageEnglish
Title of host publicationDASFAA 2020 : Database Systems for Advanced Applications
EditorsYunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang
Number of pages17
Publication date2020
ISBN (Print)978-3-030-59415-2
ISBN (Electronic)978-3-030-59416-9
Publication statusPublished - 2020
EventInternational Conference on Database Systems for Advanced Applications - Jeju, Korea, Republic of
Duration: 24 Sep 202027 Sep 2020


ConferenceInternational Conference on Database Systems for Advanced Applications
CountryKorea, Republic of
SeriesLecture Notes in Computer Science


  • Query processing
  • Semantic region
  • Spatial RDF data

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