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International Conference on Future Data and Security Engineering

FDSE 2019: Future Data and Security Engineering pp 226–238Cite as

Efficiently Semantic-Aware Pairwise Similarity: an Applicable Use-Case

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11814))

Abstract

Pairwise similarity is an essential operation in multidisciplinary fields of study. However, its operation is expensive due to its complexity as well as the evolution of big data. Besides, similarity scores without semantics leads to neither accurate nor practical results. In this paper, we study the problem of pairwise similarity in terms of semantics, efficiency, and increment. More concretely, we build our own synonym database and organize it in a reflexive synonym index to support us in semantic-aware pairwise similarity. In addition, we employ cache to perform incremental similarity computing in the process of pair-by-pair similarity search. Moreover, we design our feature-based hierarchy to eliminate irrelevant candidate objects before doing pairwise similarity. Furthermore, we analyze a practical case and apply our method into such social network-based applications in order to demonstrate its feasibility and efficiency in reality.

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Notes

  1. 1.

    http://www.friendster.com.

  2. 2.

    https://getfriendfinder.com.

  3. 3.

    https://firebase.google.com/.

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Acknowledgment

This research is funded by Ho Chi Minh City University of Technology-VNU-HCM, under the grant number T-KHMT-2018-26. We also give our thanks to Vu Tuan Minh and Tran Ngoc Thai Minh, HCMC University of Technology, for their support in our work.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, HCMC University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Trong Nhan Phan

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  1. Trong Nhan Phan
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Correspondence to Trong Nhan Phan .

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Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler Universität Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Telecommunications University, Nha Trang City, Vietnam

    Son Ha Bui

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Phan, T.N. (2019). Efficiently Semantic-Aware Pairwise Similarity: an Applicable Use-Case. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_15

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  • DOI: https://doi.org/10.1007/978-3-030-35653-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35652-1

  • Online ISBN: 978-3-030-35653-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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