Intelligence Information Retrieval System Modeling for Afaan Oromo

Due to today’s information overload, finding and retrieving desired data or information has become difficult. Due to this new phenomenon, users will have a harder time locating and retrieving relevant information. It is now the norm rather than the exception to have data and information in multiple languages. It is critical in Ethiopia, where huge amounts of Afaan Oromo data are produced daily. With massive document collections come archival and search issues, putting these data and information characteristics to the test. Afaan Oromo users could easily search for and retrieve data that was relevant to their needs and interests using a hybrid information retrieval system developed for Afaan Oromo. Performance of information retrieval systems (IRS) has been under-researched in the Afaan Oromo linguistic domain. The efficiency of these systems has been found to be significantly lacking. For these issues, the AOIR system’s performance was improved by integrating various IR system approaches. The prototype includes the current indexing and searching subsystems. On-line news articles (Oromia Broadcasting Network, VOA Afaan Oromo), websites, books, and the Afaan Oromo Bible were used to gather 1000 Afaan Oromo text documents for the experiment. To identify terms and vocabulary that contained content, the text was subjected to text operations such as tokenization, normalization, stop-word removal and stemming and calculated using tf-idf term weighting scheme. After the experimental analysis in Python 3.7, precision, recall, and F-measure were all greater than 96.6 percent. The polysemy issue continued to affect the system’s overall performance. The paper also advocates for more work on system performance optimization to advance the AOIR.

I. Introduction

The flood of data, information, and knowledge that has engulfed the Internet in the modern era has presented Internet users with a plethora of opportunities, risks, and challenges, which they must navigate. Social media and the proliferation of multilingual content on the internet have posed new challenges for the development of tools and techniques that are linguistically specific. IR systems are widely perceived as problem-solving agents, which is a common perception of information retrieval systems (IR systems). These are used to identify relevant documents in large document pools [1] [2]. According to research studies, when dealing with large document collections, the ability of information retrieval systems to locate and retrieve relevant documents has become a significant challenge [2] [3] [4]. Using a hybrid method of analysis, this article proposed an architecture for an infrared system, as well as a functional prototype for testing. To demonstrate the system’s functional validity, a functional prototype of the Afaan Oromo information retrieval (IR) system is currently being developed. Documents in the Afaan Oromo language were gathered from a variety of sources in order to create the Corpus, and a functional prototype was designed and implemented using a variety of algorithmic techniques. On the basis of the obtained results, a hybrid approach for ordering the relevant documents in response to user queries has been suggested. All of this was completed by indexing the organized corpus, which was then used to evaluate the proposed prototype by utilizing the file structure of the language. The precision, recall, and F-measure values for the IR system were 96.8 percent, 90.0 percent, and 93.3 percent, respectively, in the evaluation of the IR system, which was carried out over the proposed system prototype.