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

  • Trong Nhan PhanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Pairwise similarity Indexing Semantics Increment Multi-feature 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringHCMC University of Technology, VNU-HCMHo Chi Minh CityVietnam

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