Inspiration
As a developer interested in research tools, I was frustrated by how hard it is to find connections between research papers. Inspired by knowledge graph concepts and modern AI capabilities, I wanted to build a system that could automatically discover and visualize relationships between scientific papers.
What it does
ResearchReasoner searches academic databases and builds interactive knowledge graphs:
- Searches arXiv and Semantic Scholar APIs for research papers
- Downloads PDFs and extracts content when available
- Creates connections based on shared authors, citations, and content similarity
- Stores everything in a Neo4j graph database
- Provides a chat interface to ask questions about the papers
- Visualizes the research network with an interactive graph
How we built it
Frontend: React with TypeScript, using react-force-graph-2d for visualizations Backend: Node.js/Express server with Neo4j database AI Integration: Groq API for chat responses, custom embeddings for search APIs: Semantic Scholar and arXiv for paper discovery
The system searches for papers, analyzes relationships (shared authors, similar content, citations), stores everything in Neo4j, and provides both visual exploration and chat-based querying.
Challenges we ran into
- API Rate Limits: Had to implement proper delays and fallbacks when paper APIs failed
- PDF Processing: Many papers aren't freely available, so created text fallbacks
- Graph Performance: Rendering thousands of nodes required optimization and sampling
- Search Quality: Tuning the relationship detection algorithms to find meaningful connections
Accomplishments that we're proud of
- Successfully integrated multiple academic APIs (arXiv, Semantic Scholar)
- Built a working graph database that stores papers and relationships
- Created an interactive visualization that can handle hundreds of papers
- Implemented a functional chat interface that can answer questions about the research
- Got the full pipeline working: search → download → analyze → store → visualize → query→new research
What we learned
- Academic APIs have significant limitations and require robust error handling
- Graph databases like Neo4j are great for storing research relationships
- Building embeddings locally can work better than API calls for specific use cases
- Interactive graph visualizations need careful performance optimization
- Real research tools need to handle messy, incomplete data gracefully
What's next for ResearchReasoner
- Improve PDF access through better source discovery
- Add more relationship types (temporal patterns, methodology similarities)
- Enhance the chat interface with better context understanding
- Add export features for research notes and bibliographies
- Optimize graph rendering for larger datasets
Built With
- arxiv-api
- axios
- css
- express.js
- groq
- html
- javascript
- lucide-react
- neo4j
- node.js
- react
- react-force-graph-2d
- semantic-scholar-api
- tailwind-css
- typescript
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