Modular Query Refinement for Search Enhancement

A sophisticated web-based application that revolutionizes search experiences by using Natural Language Processing (NLP) to refine user queries intelligently. By analyzing intent, context, and semantics, this system transforms vague or poorly structured queries into precise search terms that deliver more relevant results.

Overview

Traditional keyword-based search engines often fail to capture the nuanced intent behind user queries. This Modular Query Refinement system addresses these limitations by:

  • Analyzing the semantic meaning and context of user queries
  • Identifying user intent beyond literal keywords
  • Refining queries based on personalized context
  • Generating more precise and relevant search results
  • Adapting to various domains from e-commerce to academic research

The project demonstrates how NLP techniques can bridge the gap between how users naturally express their needs and how search engines process queries.

Key Features

1. NLP-Powered Query Analysis

Uses advanced natural language processing to understand query semantics:

  • Intent Detection: Identifies whether the user wants information, comparison, purchase options, or troubleshooting
  • Entity Recognition: Extracts key entities, concepts, and relationships from queries
  • Contextual Understanding: Considers previous queries and user behavior
  • Synonym Expansion: Suggests related terms to broaden or narrow search scope

2. Modular Architecture

Flexible, component-based design that allows:

  • Easy integration with existing search systems
  • Customization for specific domains or industries
  • Independent scaling of processing modules
  • Simple addition of new refinement techniques
  • Reusable components across different applications

3. Personalized Adjustments

Tailors query refinements based on:

  • User search history and preferences
  • Domain-specific context and terminology
  • Geographic and cultural relevance
  • Time-sensitive information needs
  • User expertise level

4. Cross-Domain Utility

Applicable across various sectors:

  • E-commerce: Product discovery and comparison
  • Academic Research: Scientific literature search
  • Legal: Case law and document retrieval
  • Healthcare: Medical information lookup
  • News & Media: Content discovery

Technical Architecture

System Components

Frontend Layer (Next.js + Vue.js)

  • Modern, responsive user interface
  • Real-time query refinement suggestions
  • Interactive feedback mechanism
  • Search result visualization

Backend Layer (Django)

  • RESTful API architecture
  • Query processing pipeline
  • NLP model integration
  • User session management

Database Layer (PostgreSQL)

  • Query logs and analytics
  • User preferences and history
  • Domain-specific knowledge bases
  • Refinement pattern storage

Deployment Infrastructure

  • Docker: Containerized microservices
  • Nginx: Reverse proxy and load balancing
  • Production-ready: Scalable deployment configuration

How It Works

Query Refinement Pipeline

  1. Input Reception

    • User submits search query through interface
    • System captures query text and context

  2. Preprocessing

    • Tokenization and normalization
    • Stopword removal
    • Spell checking and correction

  3. Intent Analysis

    • Query classification (informational, transactional, navigational)
    • User goal identification
    • Context extraction

  4. Semantic Processing

    • Word embedding generation
    • Concept extraction
    • Relationship mapping

  5. Refinement Generation

    • Query expansion or narrowing suggestions
    • Alternative phrasings
    • Related concept suggestions

  6. Result Enhancement

    • Refined query passed to search engine
    • Results ranked by relevance
    • Personalized result filtering

Technology Stack

Frontend

  • Next.js: React-based framework for server-side rendering
  • Vue.js: Progressive JavaScript framework
  • TypeScript: Type-safe JavaScript development
  • HTML/CSS: Semantic markup and responsive styling

Backend

  • Django: High-level Python web framework
  • Django REST Framework: API development
  • Python: Core processing logic

NLP & ML

  • NLTK: Natural Language Toolkit
  • spaCy: Industrial-strength NLP
  • Transformers: Pretrained language models
  • Word2Vec/GloVe: Word embeddings

Infrastructure

  • Docker: Container orchestration
  • Nginx: Web server and reverse proxy
  • PostgreSQL: Relational database
  • Redis: Caching layer (optional)

Use Cases

Problem: User searches “comfortable running shoes under 100”

Refinement Process:

  • Extracts: product type (shoes), activity (running), price constraint (<$100), quality attribute (comfortable)
  • Expands: adds related terms like “athletic footwear”, “jogging shoes”
  • Filters: price range, customer ratings, comfort features

Result: More targeted product listings matching all criteria

Academic Research

Problem: Student searches “AI ethics problems”

Refinement Process:

  • Disambiguates: “AI” = Artificial Intelligence
  • Expands context: moral implications, bias, privacy concerns
  • Suggests: related concepts like algorithmic fairness, transparency
  • Adds temporal filter: recent publications

Result: Relevant research papers and articles on AI ethics

Medical Information

Problem: Patient searches “headache behind eyes”

Refinement Process:

  • Medical entity recognition: symptom = headache, location = retro-orbital
  • Context: potential conditions (migraine, sinus, eye strain)
  • Safety filter: emphasizes professional medical advice
  • Removes jargon: simplifies medical terminology

Result: Informative health articles with appropriate disclaimers

Installation & Setup

Prerequisites

  • Docker and Docker Compose
  • Node.js 16+ (for frontend development)
  • Python 3.8+ (for backend development)
  • PostgreSQL 13+

Quick Start with Docker

# Clone the repository
git clone https://github.com/rishav-dahal/Modular-Query-Refinement.git
cd Modular-Query-Refinement

# Start development environment
docker-compose -f docker-compose.dev.yml up --build

# Access the application
# Frontend: http://localhost:3000
# Backend API: http://localhost:8000

Manual Setup

# Backend setup
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver

# Frontend setup
cd MQR-frontend
npm install
npm run dev

Project Contributors

This project was developed as a collaborative effort:

  • Rishav Dahal - Backend architecture, NLP implementation
  • Sandhya Gautam - Frontend development, UI/UX design
  • Dhiraj Poudel - Database design, API development
  • Binit Bikram KC - DevOps, deployment, testing

Challenges & Solutions

Challenge 1: Query Ambiguity

Problem: Same query can have multiple interpretations.

Solution:

  • Implemented context-aware disambiguation
  • Used user history to infer likely intent
  • Provided multiple refinement options for user selection

Challenge 2: Real-Time Performance

Problem: NLP processing can be computationally expensive.

Solution:

  • Cached common query patterns
  • Used lightweight models for initial processing
  • Implemented async processing for complex refinements
  • Optimized database queries with indexing

Challenge 3: Domain Adaptation

Problem: Different domains require different refinement strategies.

Solution:

  • Modular plugin architecture for domain-specific rules
  • Configurable refinement pipelines
  • Domain-specific knowledge base integration
  • Transfer learning from general to specific domains

Future Enhancements

  • Multilingual Support: Query refinement in multiple languages
  • Voice Interface: Voice-to-text query input
  • Advanced Personalization: Deep learning-based user modeling
  • Explainable AI: Show why specific refinements were suggested
  • A/B Testing Framework: Evaluate refinement effectiveness
  • Mobile Applications: Native iOS and Android apps
  • Browser Extension: In-browser query enhancement

Technical Learnings

This project provided hands-on experience with:

  • Natural language processing and understanding
  • Full-stack web development (Next.js, Django)
  • Microservices architecture with Docker
  • Database optimization for text search
  • RESTful API design patterns
  • Frontend state management
  • Production deployment with Nginx
  • Collaborative software development

Conclusion

Modular Query Refinement demonstrates how NLP can transform search experiences by understanding user intent beyond literal keywords. The modular architecture makes it adaptable to various domains, while the sophisticated processing pipeline ensures accurate and relevant results.

This system represents a meaningful step toward more intelligent, context-aware search experiences that better serve user needs across e-commerce, research, healthcare, and other domains requiring precise information retrieval.

GitHub Repository: Modular-Query-Refinement

Live Demo: Modular-Query-Refinement

Contact: For inquiries, reach out at [email protected]

License: Proprietary - Contributor License Agreement (CLA)