User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU

• URL: http://arxiv.org/abs/2406.04490v1
• Date: Thu, 6 Jun 2024 20:28:05 GMT
• Title: User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU
• Authors: Sakshi Mahendru,
• Abstract summary: This work analyzed the informational, navigational, and transactional-based intents in queries for enhanced QP. For efficient QP, the data is structured using Epanechnikov Kernel-Ordering Points To Identify the Clustering Structure (EK-OPTICS) The extracted features, detected intents and structured data are inputted to the Multi-head Gated Recurrent Learnable Attention Unit (MGR-LAU)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Query Processing (QP) is optimized by a Cloud-based cache by storing the frequently accessed data closer to users. Nevertheless, the lack of focus on user intention type in queries affected the efficiency of QP in prevailing works. Thus, by using a Contextual Fuzzy Linguistic Inference System (CFLIS), this work analyzed the informational, navigational, and transactional-based intents in queries for enhanced QP. Primarily, the user query is parsed using tokenization, normalization, stop word removal, stemming, and POS tagging and then expanded using the WordNet technique. After expanding the queries, to enhance query understanding and to facilitate more accurate analysis and retrieval in query processing, the named entity is recognized using Bidirectional Encoder UnispecNorm Representations from Transformers (BEUNRT). Next, for efficient QP and retrieval of query information from the semantic cache database, the data is structured using Epanechnikov Kernel-Ordering Points To Identify the Clustering Structure (EK-OPTICS). The features are extracted from the structured data. Now, sentence type is identified and intent keywords are extracted from the parsed query. Next, the extracted features, detected intents and structured data are inputted to the Multi-head Gated Recurrent Learnable Attention Unit (MGR-LAU), which processes the query based on a semantic cache database (stores previously interpreted queries to expedite effective future searches). Moreover, the query is processed with a minimum latency of 12856ms. Lastly, the Semantic Similarity (SS) is analyzed between the retrieved query and the inputted user query, which continues until the similarity reaches 0.9 and above. Thus, the proposed work surpassed the previous methodologies.

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