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

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Area of Science:

  • Artificial Intelligence
  • Information Science
  • Knowledge Management

Background:

  • Current document management systems lack semantics and metadata, limiting their effectiveness.
  • E-Government initiatives require advanced methods for managing large, bilingual datasets.
  • Existing techniques struggle with retrieving and organizing Arabic and English documents effectively.

Purpose of the Study:

  • To develop an ontology-based artificial intelligence (AI) framework for enhanced document management.
  • To address the limitations in semantic understanding and metadata utilization in current systems.
  • To facilitate the management and retrieval of bilingual (Arabic and English) documents.

Main Methods:

  • Data extraction from 77 bilingual documents.
  • Development of a bilingual dictionary for information retrieval.
  • Implementation of a Naïve Bayes classifier for document relation identification.
  • Application of a link analysis-based ranking approach for query results.

Main Results:

  • The proposed ontology-based AI framework demonstrated superior performance.
  • The system effectively managed and retrieved bilingual documents.
  • The Naïve Bayes classifier accurately identified document relationships.
  • Link analysis improved the relevance of ranked search results.

Conclusions:

  • The developed ontology-based AI framework offers a significant improvement over existing document management solutions.
  • The framework provides a robust approach for handling bilingual data in E-Government contexts.
  • The study highlights the potential of AI and ontologies in advancing information retrieval and knowledge organization.