Semantic Scholar

Introduction

Semantic Scholar is a free academic search engine developed by the Allen Institute for AI. It uses AI to help researchers discover relevant literature. Ai4Scholar proxies and organizes commonly used Semantic Scholar endpoints for search, paper details, and citation networks. See the interactive API documentation for the complete list of available endpoints, parameters, and limits.

Data coverage

• Disciplines: Computer science, biomedicine, physics, mathematics, and more
• Update frequency: Determined by Semantic Scholar's current index
• Metadata: Titles, abstracts, authors, citations, references, and semantic tags

API capabilities

Through Ai4Scholar, you can access the following Semantic Scholar APIs:

Academic Graph API

• paper/search — Search for papers by keyword
• paper/{paper_id} — Get paper details
• paper/{paper_id}/citations — Get papers that cite a paper
• paper/{paper_id}/references — Get a paper's references
• author/search — Search for authors
• author/{author_id} — Get author details

Recommendations API

• Recommend related papers based on a single paper
• Generate batch recommendations from a list of papers

Datasets API

• List available dataset releases
• Download academic data in bulk

Search examples

# Search for papers on machine learning
curl "https://ai4scholar.net/graph/v1/paper/search?query=machine+learning&limit=10" \
  -H "Authorization: Bearer YOUR_API_KEY"

import requests
 
response = requests.get(
    "https://ai4scholar.net/graph/v1/paper/search",
    params={"query": "deep learning", "limit": 5},
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)
papers = response.json()["data"]