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Comparison of Semantic Similarity Models on Constrained Scenarios

Author

Listed:
  • Rafael Teixeira

    (Univerisdade de Aveiro)

  • Mário Antunes

    (Univerisdade de Aveiro
    Universidade de Aveiro)

  • Diogo Gomes

    (Univerisdade de Aveiro
    Universidade de Aveiro)

  • Rui L. Aguiar

    (Univerisdade de Aveiro
    Universidade de Aveiro)

Abstract

The technological world has grown by incorporating billions of small sensing devices, collecting and sharing large amounts of diversified data over the new generation of wireless and mobile networks. We can use semantic similarity models to help organize and optimize these devices. Even so, many of the proposed semantic similarity models do not consider the constrained and dynamic environments where these devices are present (IoT, edge computing, 5g, and next-generation networks). In this paper, we review the commonly used models, discuss the limitations of our previous model, and explore latent space methods (through matrix factorization) to reduce noise and correct the model profiles with no additional data. The new proposal is evaluated with corpus-based state-of-the-art approaches achieving competitive results while having four times faster training time than the next fastest model and occupying 36 times less disk space than the next smallest model.

Suggested Citation

  • Rafael Teixeira & Mário Antunes & Diogo Gomes & Rui L. Aguiar, 2024. "Comparison of Semantic Similarity Models on Constrained Scenarios," Information Systems Frontiers, Springer, vol. 26(4), pages 1307-1330, August.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:4:d:10.1007_s10796-022-10350-w
    DOI: 10.1007/s10796-022-10350-w
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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    Cited by:

    1. Muhammad Younas & Irfan Awan, 2024. "Cloud, IoT and Data Science," Information Systems Frontiers, Springer, vol. 26(4), pages 1219-1222, August.

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