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Semantic approaches for query expansion: taxonomy, challenges, and future research directions
Azzah Allahim1,2, Asma Cherif1,3, Abdessamad Imine4
1IT Department, King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia.
Semantic query expansion enhances information retrieval by providing more relevant results. This review covers recent advances (2015-2024), challenges, and future directions, favoring linguistic and ontology-based approaches for improved search.
Area of Science:
- Information Retrieval
- Natural Language Processing
- Artificial Intelligence
Background:
- Information retrieval systems struggle with the vast amount of online data.
- Query expansion techniques significantly improve search result relevance.
- Semantic query expansion offers more pertinent and practical results.
Purpose of the Study:
- To provide a comprehensive review of query expansion methods, focusing on semantic approaches.
- To overview recent semantic query expansion frameworks (2015-2024) and their limitations.
- To discuss practical challenges and identify future research directions in semantic query expansion.
Main Methods:
- Review of recent literature on query expansion, emphasizing semantic techniques.
- Analysis of frameworks developed between 2015 and 2024.
- Discussion of linguistic, ontology-based, and artificial intelligence (AI) approaches.
Main Results:
- Semantic query expansion significantly improves information retrieval.
- Linguistic approaches offer flexibility, while ontology approaches suit domain-specific applications.
- Recent advancements focus on AI and query context utilization for better results.
Conclusions:
- The linguistic approach is highly effective and flexible for semantic query expansion.
- Ontology development holds potential for domain-specific search applications.
- Future research should leverage AI and query context to optimize expanded queries.
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