Summary of User Intent Recognition and Semantic Cache Optimization-based Query Processing Framework Using Cflis and Mgr-lau, by Sakshi Mahendru
User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU
by Sakshi Mahendru
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a Cloud-based cache optimization for Query Processing (QP), specifically designed to enhance QP by analyzing user intention types in queries. A Contextual Fuzzy Linguistic Inference System (CFLIS) is employed to identify informational, navigational, and transactional-based intents in queries. The query processing pipeline involves tokenization, normalization, stop word removal, stemming, and POS tagging, followed by query expansion using WordNet. Named entity recognition is achieved through Bidirectional Encoder UnispecNorm Representations from Transformers (BEUNRT). Epanechnikov Kernel-Ordering Points To Identify the Clustering Structure (EK-OPTICS) structures the data for efficient QP and retrieval. The system features sentence type identification, intent keyword extraction, and processing through a Multi-head Gated Recurrent Learnable Attention Unit (MGR-LAU). The proposed method achieves a minimum latency of 12856ms and surpasses previous methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves how computers process search queries by understanding what people are looking for. It uses special tools to figure out if someone is searching for information, navigating through pages, or making a transaction. The system then analyzes the query and stores it in a “cache” so that future searches can be faster and more accurate. This approach helps reduce the time it takes to process search queries and makes them more effective. |
Keywords
» Artificial intelligence » Attention » Clustering » Encoder » Inference » Named entity recognition » Optimization » Stemming » Tokenization
