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Conference Paper: Top-k relevant semantic place retrieval on spatial RDF data

TitleTop-k relevant semantic place retrieval on spatial RDF data
Authors
Issue Date2016
PublisherACM Press.
Citation
The 2016 ACM SIGMOD International Conference on Management of Data (SIGMOD 2016), San Francisco, CA., 26 June-1 July 2016. In Conference Proceedings, 2016, p. 1977-1990 How to Cite?
AbstractRDF 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. © 2016 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/229721
ISBN

 

DC FieldValueLanguage
dc.contributor.authorShi, J-
dc.contributor.authorWu, D-
dc.contributor.authorMamoulis, N-
dc.date.accessioned2016-08-23T14:12:52Z-
dc.date.available2016-08-23T14:12:52Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 ACM SIGMOD International Conference on Management of Data (SIGMOD 2016), San Francisco, CA., 26 June-1 July 2016. In Conference Proceedings, 2016, p. 1977-1990-
dc.identifier.isbn978-1-4503-3531-7-
dc.identifier.urihttp://hdl.handle.net/10722/229721-
dc.description.abstractRDF 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. © 2016 ACM.-
dc.languageeng-
dc.publisherACM Press.-
dc.relation.ispartofProceedings of the 2016 International Conference on Management of Data, SIGMOD '16-
dc.titleTop-k relevant semantic place retrieval on spatial RDF data-
dc.typeConference_Paper-
dc.identifier.emailWu, D: dmwu@cs.hku.hk-
dc.identifier.emailMamoulis, N: nikos@cs.hku.hk-
dc.identifier.authorityMamoulis, N=rp00155-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1145/2882903.2882941-
dc.identifier.scopuseid_2-s2.0-84979681317-
dc.identifier.hkuros262975-
dc.identifier.spage1977-
dc.identifier.epage1990-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160919-

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