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Semantic Region Retrieval from Spatial RDF Data

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Database Systems for Advanced Applications (DASFAA 2020)

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Abstract

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.

This work is supported in part by grant No. 2019A1515011721 from Natural Science Foundation of Guangdong, China and the DiCyPS project, funded by Innovation Fund Denmark.

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Notes

  1. 1.

    Vertices with degree less than 12 on Yago and less than 20 on DBpedia.

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Author information

Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Dingming Wu, Can Hou & Erjia Xiao

  2. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Christian S. Jensen

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  1. Dingming Wu
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  2. Can Hou
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  3. Erjia Xiao
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  4. Christian S. Jensen
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Corresponding author

Correspondence to Dingming Wu .

Editor information

Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Peking University, Haidian, China

    Bin Cui

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Sang-Won Lee

  4. Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong

    Jeffrey Xu Yu

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Steven Euijong Whang

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Wu, D., Hou, C., Xiao, E., Jensen, C.S. (2020). Semantic Region Retrieval from Spatial RDF Data. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12113. Springer, Cham. https://doi.org/10.1007/978-3-030-59416-9_25

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  • DOI: https://doi.org/10.1007/978-3-030-59416-9_25

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