Semantic approaches for query expansion: taxonomy, challenges, and future research directions
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Summary
This summary is machine-generated.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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