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You spend too much time reading PDFs. AI helps you find research gaps faster.
Architect Your Search Strings Create keyword blocks. Build synonym rings in a spreadsheet. Use Boolean logic for precise queries.
Automated Snowballing Find references in old papers. Find new papers citing them. Repeat until you find all key works.
Embedding Generation Use FAISS for dense vector similarity. This finds papers by meaning instead of keywords.
Define Relevance Prototypes Label a few papers as relevant. Score new papers against these examples.
Academic Knowledge Graphs Use OpenAlex. Check venue quality and citation counts. See how authors connect.
The Initial Harvest Use Semantic Scholar API for TLDR summaries. Start with small batches to test.
Build a Classification Layer Train a simple model. Use it to filter results based on your prototypes.
Corpus Diagnostics Check author networks. Ensure your sources match your field.
Execute Automated Triage Remove duplicates using DOI. Keep a clean list for your review.
Actionable Takeaways
- Start small.
- Validate each step.
- Use data over raw keyword counts.
Checklist
- Synonym rings done.
- Snowballing finished.
- Vectors stored.
- Metadata added.
- Triage run.
Source: https://dev.to/ken_deng_ai/title-328d Optional learning community: https://t.me/GyaanSetuAi