The Future of Academic Search: From Keywords to Semantic Understanding

Short summary

Semantic search uses embeddings to understand research concepts rather than exact keyword matches, improving academic paper discovery. Testing Paper List (semantic) vs Google Scholar shows 80% vs 40% relevance with better handling of terminology variation and cross-disciplinary work. This shift to concept-based discovery enables personalized feeds and represents a change as significant as Google Scholar's original launch.

• •Semantic search encodes papers and queries as embeddings for concept-based matching instead of keyword matching
• •Production systems combine keyword (BM25) + dense retrieval with learned fusion for optimal results
• •Enables interdisciplinary discovery and levels the playing field for papers with creative titles

Generated with AI, which can make mistakes.