Business Intelligence Management Studies

Designing a system for matching the name and field of activity of companies based on artificial intelligence

Document Type : Research Paper

Author

mohammad rabiei

Associate Professor, Department of Electrical and Computer Engineering, Faculty of Engineering, Eyvanekey University, Eyvanekey, Semnan, Iran.Corresponding Author: Mohammad.Rabiei@eyc.ac. ir

https://doi.org/10.22054/ims.2026.83957.2573
Abstract
Semantic similarity is used in applications such as information retrieval, text summarization and sentiment analysis. In this article, a new method based on deep learning has been presented in order to check the matching percentage of the proposed name of the company registration applicants with the time of the company's activity. The key innovation lies in the use of a combined Aria BERT model for word embedding to convert registered company names into vectors. Additionally, the company's field of activity is converted into numerical vectors using the FastText model, which are then processed through deep learning algorithms, specifically bidirectional long short-term memory (Bi-LSTM) networks with an additional attention layer. The results were evaluated using cosine similarity and ROUGE criteria. Following the approval of the company name and activity field, the DBSCAN clustering method is employed to categorize the company names based on their activities.
The results demonstrate that the ROUGE-1, ROUGE-2, and ROUGE-L scores for company activity vectorization are 0/7623, 0/7413, and 0/7982, respectively. The overall model accuracy and recall were 0/8512 and 0/8317, respectively. Moreover, the correlation coefficient between the cosine similarity of the proposed names and the company's activity time, as calculated by the model, was 93%, confirming the model's effectiveness.
This method effectively preventing the registration of names that do not meaningfully relate to the company's operations. By clustering company names, the method facilitates the suggestion of related names based on the company's field of activity.

Keywords

Company registration
Cosine similarity
Deep learning
Semantic relation
Text mining
Subjects
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‌ ‌استناد به این مقاله: ربیعی، محمد. (1405). طراحی سیستم سنجش تطابق نام و حوزه فعالیت شرکت‌ها بر اساس هوش مصنوعی، مطالعات مدیریت کسب وکار هوشمند، 15(55)، 299-328. DOI: 10.22054/ims.2026.83957.2573

 Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..

Business Intelligence Management Studies
Volume 15, Issue 55
Spring 2026
Pages 307-336

  • Receive Date 25 January 2025
  • Revise Date 19 November 2025
  • Accept Date 03 January 2026

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