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README.md
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@@ -32,13 +32,16 @@ The Research Paper Metadata Database represents **prior work** that demonstrates
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### 🎯 Position Within CopernicusAI Knowledge Engine
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The Research Paper Metadata Database serves as a **core data infrastructure component** of the CopernicusAI Knowledge Engine, providing:
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- **Foundation for Knowledge Graph Construction:** Structured metadata enables relationship mapping
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- **Integration with AI Podcast Generation:** Links research papers to generated podcast content
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- **Support for GLMP:** Provides source paper references for biological process visualizations
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- **Science Video Database Integration:** Potential linking between papers and related video content
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- **Programming Framework Support:** Supplies structured data for process analysis applications
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This work establishes a proof-of-concept for AI-assisted research metadata management, demonstrating how structured data can enable systematic analysis and visualization of scientific research patterns.
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## 🎯 Project Goals
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### 🎯 Position Within CopernicusAI Knowledge Engine
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The Research Paper Metadata Database serves as a **core data infrastructure component** of the CopernicusAI Knowledge Engine, providing:
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- **Foundation for Knowledge Graph Construction:** Structured metadata enables relationship mapping - **✅ Now Fully Operational** (December 2025) with 12,000+ mathematics papers indexed, interactive knowledge graph visualization, and relationship extraction (citations, semantic similarity, categories)
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- **Knowledge Engine Dashboard** (✅ Implemented December 2025) - Fully operational web interface providing unified access to research papers through knowledge graph visualization, vector search, RAG queries, and content browsing. Live at: https://copernicus-frontend-phzp4ie2sq-uc.a.run.app/knowledge-engine
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- **Vector Search:** Semantic search using Vertex AI embeddings across papers, podcasts, and processes
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- **RAG System:** Retrieval-augmented generation with citation support and multi-modal content integration
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- **Integration with AI Podcast Generation:** Links research papers to generated podcast content
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- **Support for GLMP:** Provides source paper references for biological process visualizations
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- **Science Video Database Integration:** Potential linking between papers and related video content
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- **Programming Framework Support:** Supplies structured data for process analysis applications
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This work establishes a proof-of-concept for AI-assisted research metadata management, demonstrating how structured data can enable systematic analysis and visualization of scientific research patterns. The Knowledge Engine now provides a fully operational system for exploring research papers through multiple interfaces.
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## 🎯 Project Goals
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