Connected Papers vs Semantic Scholar
Similarity23%
Connected Papers
Visual literature maps that reveal related work around a seed paper, helping researchers explore fields, spot clusters, and find influential prior art quickly.
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Semantic Scholar
Semantic Scholar is a free AI powered scholarly search engine from AI2 that helps you find papers authors and citation links, and it also provides a public REST API and Academic Graph data access for building research tools and analyses.
Visit website →At a glance
| Connected Papers | Semantic Scholar | |
|---|---|---|
| Price | Free / $9 per month | Free |
| Difficulty | Beginner | Beginner |
| Type | Web App | Web App |
| Status | Active | Active |
Connected Papers — Key features
- Graph of related papers via co-citation analysis
- Cluster views to identify schools of thought and methods
- Filters for date influence and distance from seed
- Snapshots and exports for sharing reading lists
- Links out to publisher pages and repositories
- Free tier plus Academic and Business plans
Semantic Scholar — Key features
- Free scholarly search: Provides a free search experience for papers authors venues and citation relationships
- REST API access: Offers a REST API to explore publication data about papers authors citations and venues
- API license terms: Publishes an API license agreement that defines acceptable use and legal obligations
- Graph based discovery: Supports citation network exploration to trace influential works and related research paths
- Metadata retrieval: Enables programmatic metadata retrieval for building research dashboards and tools
- Citation linkage: Helps follow citations and references quickly to map a field without manual browsing
Connected Papers — Best for
- Map a field around a seminal work in minutes
- Assemble a syllabus or lab reading plan by cluster
- Validate novelty and check for near-duplicate ideas
- Find bridges between subfields for new directions
- Identify review papers to onboard collaborators
Semantic Scholar — Best for
- Literature discovery: Find key papers and authors in a topic and expand via citation links to build a reading list
- Author profiles: Track an authors output and coauthor network to understand a research area faster
- Dataset building: Use API data to build a local dataset of papers and citations for analysis and visualization
- Trend analysis: Analyze venues and citation patterns over time to spot emerging topics and influential work
- Tool prototyping: Build a research assistant app that fetches paper metadata and shows related work automatically
