Semantic Scholar Paper Search Tool

A Python toolkit for searching and analyzing academic papers using the Semantic Scholar API with advanced semantic search capabilities. This tool goes beyond simple keyword matching to understand the meaning and context of your research queries.

Key Features

Semantic Search Powered

• Natural Language Queries: Use full sentences and research questions instead of just keywords
• Conceptual Understanding: Finds papers about "inflammatory bowel disease" when you search "IBD"
• Synonym Recognition: Automatically matches related terms and concepts
• Context-Aware: Understands research methodology, populations, and domains

Dual Analysis Modes

• Top Cited Papers: Find the most influential papers in your field within a specific timeframe
• Recent Papers: Discover the latest research across multiple keywords

Smart Filtering

• Exclude unwanted topics or methodologies
• Customizable time ranges
• Configurable result limits
• Field-specific data retrieval

Prerequisites

• Python 3.7+
• Semantic Scholar API key (Get one here)
• Required packages: requests

Setup

1. Install dependencies:

   pip install requests

2. Set your API key:

   # Windows
   set SEMANTIC_SCHOLAR_API_KEY=your_api_key_here
   
   # Mac/Linux
   export SEMANTIC_SCHOLAR_API_KEY=your_api_key_here

Usage Examples

Basic Usage

# Default search (IBD research)
python main.py

# Simple keyword search
python main.py --query "machine learning"

Semantic Search Examples

Complex Semantic Phrases:

python main.py --query "personalized medicine approaches for treatment-resistant ulcerative colitis"

python main.py --query "gut-brain axis mechanisms in inflammatory bowel disease pathogenesis"

python main.py --query "novel therapeutic targets for preventing IBD complications"

Multi-Keyword Semantic Search:

python main.py --keywords "How do genetic variants influence IBD susceptibility" "What environmental factors trigger Crohn's disease" "Why do some patients not respond to anti-TNF therapy"

Advanced Filtering

Exclude Specific Topics:

python main.py --query "IBD treatment" --exclude-terms "surgery" "pediatric" "animal model"

Custom Time Ranges:

# Recent papers from last 30 days
python main.py --days-back 30

# Top cited papers from last 6 months
python main.py --months-back 6

Result Customization:

# Get top 10 papers, show 5 per keyword
python main.py --top-n 10 --display-limit 5

# Fetch more comprehensive results
python main.py --max-results-per-keyword 300 --max-fetch-top-cited 2000

Output Format

Each paper includes:

• Title and Authors (formatted for readability)
• Journal/Venue and Publication Date
• Citation Count (for impact assessment)
• DOI/URL (direct access links)
• TLDR (AI-generated summary when available)

Command Line Options

Option	Description	Default
--query	Primary search query (supports natural language)	"IBD"
--keywords	Multiple keywords for recent search	["IBD genetics", "Crohn's disease", "ulcerative colitis"]
--exclude-terms	Terms to filter out from results	["microbiome", "prebiotics", "probiotics"]
--days-back	Days back for recent papers search	7
--months-back	Months back for top cited papers	12
--top-n	Number of top cited papers to retrieve	5
--max-results-per-keyword	Max results per keyword	150
--display-limit	Papers to display per keyword	3
--fields	API fields to retrieve	All standard fields

This project is open source. Please ensure you comply with Semantic Scholar's API terms of service.