Literature Review → Knowledge Graph → Research Gaps
Automatically analyze research literature to identify knowledge gaps and generate new research directions.
Workflow Steps
Semantic Scholar API
Gather relevant papers
Use the API to search for papers related to your research domain, filtering by publication date, citation count, and relevance. Export metadata including abstracts, citations, and author information
OpenAI API or Claude
Extract key concepts and relationships
Process each paper's abstract and key sections to identify: main concepts, methodologies used, limitations mentioned, and future work suggestions. Structure output as JSON with relationships between concepts
Obsidian
Build knowledge graph
Import the structured data to create interconnected notes for each concept, with links showing relationships. Use tags for methodologies, limitations, and research gaps
Python with NetworkX
Analyze network structure
Export the knowledge graph and use NetworkX to identify clusters of related concepts, isolated nodes (potential gaps), and central concepts that bridge multiple areas
ChatGPT or Claude
Generate research questions
Input the gap analysis results and prompt the AI to generate specific, testable research questions that could address the identified gaps, along with suggested methodological approaches
Workflow Flow
Step 1
Semantic Scholar API
Gather relevant papers
Step 2
OpenAI API or Claude
Extract key concepts and relationships
Step 3
Obsidian
Build knowledge graph
Step 4
Python with NetworkX
Analyze network structure
Step 5
ChatGPT or Claude
Generate research questions
Why This Works
Leverages AI's pattern recognition with network analysis to systematically identify research opportunities that might be missed in manual reviews
Best For
Researchers and graduate students conducting systematic literature reviews and identifying novel research directions
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