TY - GEN
T1 - Top-k relevant semantic place retrieval on spatial RDF data
AU - Shi, Jieming
AU - Wu, Dingming
AU - Mamoulis, Nikos
N1 - Publisher Copyright:
© 2016 ACM.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/6/26
Y1 - 2016/6/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84979681317&partnerID=8YFLogxK
U2 - 10.1145/2882903.2882941
DO - 10.1145/2882903.2882941
M3 - Conference article published in proceeding or book
AN - SCOPUS:84979681317
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 1977
EP - 1990
BT - SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PB - Association for Computing Machinery
T2 - 2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
Y2 - 26 June 2016 through 1 July 2016
ER -