Approximate semantic matching over linked data streams

Yongrui Qin*, Lina Yao, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)


In the Internet of Things (IoT), data can be generated by all kinds of smart things. In such context, enabling machines to process and understand such data is critical. Semantic Web technologies, such as Linked Data, provide an effective and machine-understandable way to represent IoT data for further processing. It is a challenging issue to match Linked Data streams semantically based on text similarity as text similarity computation is time consuming. In this paper, we present a hashing-based approximate approach to efficiently match Linked Data streams with users’ needs. We use the Resource Description Framework (RDF) to represent IoT data and adopt triple patterns as user queries to describe users’ data needs. We then apply locality-sensitive hashing techniques to transform semantic data into numerical values to support efficient matching between data and user queries. We design a modified k nearest neighbors (kNN) algorithm to speedup the matching process. The experimentalresults show that our approach is up to five times faster than the traditional methods and can achieve high precisions and recalls.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications
Subtitle of host publication27th International Conference, DEXA 2016, Porto, Portugal, September 5-8, 2016, Proceedings, Part II
EditorsS Hartmann, H Ma
Place of PublicationCham
PublisherSpringer, Springer Nature
Number of pages15
ISBN (Electronic)9783319444062
ISBN (Print)9783319444055
Publication statusPublished - 2016
Externally publishedYes
Event27th International Conference on Database and Expert Systems Applications, DEXA 2016 - Porto, Portugal
Duration: 5 Sept 20168 Sept 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other27th International Conference on Database and Expert Systems Applications, DEXA 2016


  • Internet of Things
  • kNN classification
  • Linked Data
  • Semantic matching


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