Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Abstract

In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

A comprehensive survey of techniques for developing an Arabic question answering system

“…The purpose of a search engine is to find the most relevant content from a large pool of results. It employs a complex processing technique to sort valuable answers ( Alothman & Wahab Sait, 2022 ). The final step is locating content that may provide valuable insights.…”

“…Capitalization is not used in Arabic, which increases the difficulty of performing NER tasks. Lack of capitalization thus adds a lot of ambiguity for parsing queries and answer formation ( Breja & Jain, 2022 ; Alothman & Wahab Sait, 2022 ; Hao et al, 2022 ). Due to their unique characteristics, proper nouns may require specialized hardware for appropriate recognition.…”

“…The response of factoid QAS is often considered adequate. Questions of the “factoid” variety are typically part of a vast dataset ( Alothman & Wahab Sait, 2022 ; Brini et al, 2009b ; Fareed, Mousa & Elsisi, 2013 ). Answering inquiries of the “factoid” variety does not require sophisticated NLP systems.…”

“…Any replies to this query should be presented as a list ( Alothman & Wahab Sait, 2022 ; Brini et al, 2009b ; Fareed, Mousa & Elsisi, 2013 ). For instance, list the cities in Saudi Arabia, and the query can list the name of the cities in the country.…”

“…Answering confirmation queries with a yes or no requires understanding the inference process and the reasoning ability ( Alothman & Wahab Sait, 2022 ; Brini et al, 2009b ; Fareed, Mousa & Elsisi, 2013 ). Some experienced users require common reasoning and world knowledge for new understanding.…”