Open Access

ARTICLE

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

Ahmad Ababneh¹, Yousef Sanjalawe², Salam Fraihat^3,*, Salam Al-E’mari⁴, Hamzah Alqudah⁵

1 Department of Computer Science, Faculty of Information Technology, American University of Madaba, Amman, 11821, Jordan
2 Department of Cybersecurity, Faculty of Information Technology, American University of Madaba, Amman, 11821, Jordan
3 Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, P.O. Box 346, Ajman, 13306, United Arab Emirates
4 Information Security Department, Faculty of Information Technology, University of Petra, Amman, 11196, Jordan
5 Department of Data Science and Artificial Intelligence, Faculty of Information Technology, American University of Madaba, Amman, 11821, Jordan

* Corresponding Author: Salam Fraihat. Email:

Computers, Materials & Continua 2024, 80(1), 1347-1379. https://doi.org/10.32604/cmc.2024.050600

Received 11 February 2024; Accepted 05 June 2024; Issue published 18 July 2024

Abstract

This study introduces the Orbit Weighting Scheme (OWS), a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval (IR) models, which have traditionally relied on weighting schemes like tf-idf and BM25. These conventional methods often struggle with accurately capturing document relevance, leading to inefficiencies in both retrieval performance and index size management. OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space, emphasizing term relationships and distribution patterns overlooked by existing models. Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall, Precision, Interpolated Average Precision (IAP), and Mean Average Precision (MAP). Additionally, we assess OWS’s effectiveness in reducing the inverted index size, crucial for model efficiency. We compare OWS-based retrieval models against others using different schemes, including tf-idf variations and BM25Delta. Results reveal OWS’s superiority, achieving a 54% Recall and 81% MAP, and a notable 38% reduction in the inverted index size. This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

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Keywords

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