Approximate semantic matching over linked data streams

Yongrui Qin, Lina Yao, Quan Z. Sheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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

LanguageEnglish
Title of host publicationDatabase and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings
PublisherSpringer Verlag
Pages37-51
Number of pages15
Volume9828
ISBN (Print)9783319444055
DOIs
Publication statusPublished - 2016
Event27th International Conference on Database and Expert Systems Applications - Porto, Portugal
Duration: 5 Sep 20168 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9828
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Database and Expert Systems Applications
Abbreviated titleDEXA 2016
CountryPortugal
CityPorto
Period5/09/168/09/16

Fingerprint

Linked Data
Data Streams
Semantics
Internet of Things
Semantic Web
Hashing
Query
Processing
Internet of things
Locality
Thing
Nearest Neighbor
Speedup
Transform
Resources

Cite this

Qin, Y., Yao, L., & Sheng, Q. Z. (2016). Approximate semantic matching over linked data streams. In Database and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings (Vol. 9828, pp. 37-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9828). Springer Verlag. https://doi.org/10.1007/978-3-319-44406-2_5
Qin, Yongrui ; Yao, Lina ; Sheng, Quan Z. / Approximate semantic matching over linked data streams. Database and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings. Vol. 9828 Springer Verlag, 2016. pp. 37-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Qin, Y, Yao, L & Sheng, QZ 2016, Approximate semantic matching over linked data streams. in Database and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings. vol. 9828, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9828, Springer Verlag, pp. 37-51, 27th International Conference on Database and Expert Systems Applications, Porto, Portugal, 5/09/16. https://doi.org/10.1007/978-3-319-44406-2_5

Approximate semantic matching over linked data streams. / Qin, Yongrui; Yao, Lina; Sheng, Quan Z.

Database and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings. Vol. 9828 Springer Verlag, 2016. p. 37-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9828).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Yao, Lina

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AB - 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.

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Qin Y, Yao L, Sheng QZ. Approximate semantic matching over linked data streams. In Database and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings. Vol. 9828. Springer Verlag. 2016. p. 37-51. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-44406-2_5