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Top-k relevant semantic place retrieval on spatiotemporal RDF data

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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 users from these requirements; users only input a set of keywords, and the goal is to find small RDF subgraphs that contain all keywords. At the same time, popular RDF knowledge bases also include spatial and temporal semantics, which opens the road to spatiotemporal-based search operations. In this work, we propose and study novel keyword-based search queries with spatial semantics on RDF data, namely kSP queries. The objective of the kSP query is to find RDF subgraphs which contain the query keywords and are rooted at spatial entities close to the query location. To add temporal semantics to the kSP query, we propose the kSPT query that uses two ways to incorporate temporal information. One way is considering the temporal differences between the keyword-matched vertices and the query timestamp. The other way is using a temporal range to filter keyword-matched vertices. The novelty of kSP and kSPT queries is that they are spatiotemporal-aware and that they do not rely on the use of structured query languages. We design an efficient approach containing two pruning techniques and a data preprocessing technique for the processing of kSP queries. The proposed approach is extended and improved with four optimizations to evaluate kSPT queries. Extensive empirical studies on two real datasets demonstrate the superior and robust performance of our proposals compared to baseline methods.

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Notes

  1. Disk-based graph representations for RDF data (e.g., [67]) can also be used for larger-scale data.

  2. If multiple trees rooted at p have the same minimum looseness, we can: (1) break ties arbitrarily and select one of them to be the TQSP for p or (2) keep all trees with the same minimum looseness in a set. If we use option (2), the result of a kSP query would the top-k qualified semantic place sets. The methods proposed in this paper are applicable for both options. For the ease of presentation, we adopt option (1) in the rest of the paper.

  3. The temporal difference could be measured in days, minutes, etc.

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Authors and Affiliations

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

    Dingming Wu & Hao Zhou

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Jieming Shi

  3. Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

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  1. Dingming Wu
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  2. Hao Zhou
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Correspondence to Jieming Shi.

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This work was supported in part by Grant No. 2019A1515011721 from Natural Science Foundation of Guangdong, China and by Grant No. 61502310 from National Natural Science Foundation of China and by Grant No. 17253616 from Hong Kong RGC.

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Wu, D., Zhou, H., Shi, J. et al. Top-k relevant semantic place retrieval on spatiotemporal RDF data. The VLDB Journal 29, 893–917 (2020). https://doi.org/10.1007/s00778-019-00591-8

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