Search Query Refinement Using Context, Knowledge and Long-term Memorization
Sudhanshu Gupta, Krishna Kumar Tiwari
Asian Journal of Research in Computer Science · pp. 27–36 · Published 16 Jan 2024
10.9734/ajrcos/2024/v17i2417Abstract
In the era of information overload, search tools are crucial, yet users often struggle to articulate their precise dataneeds, resulting in sub-optimal search outcomes. This is often due to users associating products with influencers or celebrities rather than knowing the specific brand or product name. This research aims to enable users to find products based on real-life associ- ations, emphasizing the importance of upgrading search query refinement for accuracy and relevance. A significant challenge faced by existing web tools is refining queries involving unrelated entities. This research addresses this gap by proposing a compre- hensive approach that integrates context, knowledge represen- tation, and long-term memorization. The framework combines contextual information with advanced knowledge representation techniques, enhancing the system’s understanding of user intent and domain-specific concepts. Long-term memorization reduces the time complexity of query refinement by leveraging past search experiences. This study underscores the potential of incorporating context, knowledge representation, and long-term memorization in refining search queries, offering more accurate results. As the digital landscape evolves, our approach has the potential to upgrade the search engine experience, providing personalized and contextually relevant results globally.
References (8)
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- 2 Knowledge Graph Embedding Based Question Answering [DOI]
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- 7 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining [DOI]
- 8 Deep Search Query Intent Understanding [DOI]
Cited by 4
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Bahadır Yalın, Akasya Akyuz, F. Abut · Cukurova University Journal of Natural and Applied Sciences · 2024
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