Semantic Sensing Performance Analysis : Assessing Keyword Coverage in Text Data

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Scopus Citations
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Author(s)

  • Yaoqi Yang
  • Bangning Zhang
  • Daoxing Guo
  • Renhui Xu
  • Zehui Xiong
  • Dusit Niyato

Related Research Unit(s)

Detail(s)

Original languageEnglish
Journal / PublicationIEEE Transactions on Vehicular Technology
Publication statusOnline published - 5 Jun 2023

Abstract

In this paper, to provide the theoretical reference for semantic sensing performance, the stochastic geometry tool is used to make the quantitative coverage performance analysis of the semantic keywords. This method can help understand semantic network performance from the macroscopic view rather than the connection-oriented perspective. Firstly, through the semantic mapping operation, the context space is transformed into the semantic space in the semantic sensing process. Secondly, given the text data with different statement styles, e.g., literary and scientific texts, two typical semantic reflection models are established with the help of stochastic geometry theory, where the keyword coverage expressions are derived. Thirdly, under various parameter settings, the correctness and effectiveness of the proposed models have been evaluated through the simulation results and analysis.

© 2023 IEEE.

Research Area(s)

  • Semantic sensing, stochastic geometry, keyword coverage performance, semantic communication

Citation Format(s)

Semantic Sensing Performance Analysis: Assessing Keyword Coverage in Text Data. / Yang, Yaoqi; Zhang, Bangning; Guo, Daoxing et al.
In: IEEE Transactions on Vehicular Technology, 05.06.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review