  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.
