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Approximate Semantic Matching Over Linked Data Streams

Qin, Yongrui, Yao, Lina and Sheng, Quan Z. (2016) Approximate Semantic Matching Over Linked Data Streams. In: Database and Expert Systems Applications: 27th International Conference, DEXA 2016, Porto, Portugal, September 5-8, 2016, Proceedings, Part II. Lecture Notes in Computer Science, 9828 . Springer International Publishing, pp. 37-51. ISBN 978-3-319-44405-5

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Abstract

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 experimental results show that our approach is up to five times faster than the traditional methods and can achieve high precisions and recalls.

Item Type: Book Chapter
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Yongrui Qin
Date Deposited: 10 Aug 2016 15:39
Last Modified: 10 Aug 2016 15:44
URI: http://eprints.hud.ac.uk/id/eprint/29137

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