Semantic Scholar is an AI-powered academic search and research platform that helps researchers, students, and academics discover and explore scientific literature. Developed by the Allen Institute for AI, it uses artificial intelligence and machine learning to make it easier to find relevant research papers, understand scholarly work, and navigate large collections of academic publications.

Users can search for research papers by topic, keyword, author, or field and explore related publications through citation and recommendation features. Semantic Scholar analyzes academic literature to identify important connections between papers, helping users discover relevant research beyond basic keyword matching.

The platform provides useful information such as paper summaries, citation counts, influential citations, references, authors, and related research. Its AI-powered recommendations can help researchers find papers relevant to their interests and stay informed about developments in their field.

Semantic Scholar is particularly useful for literature reviews, academic research, scientific discovery, and exploring unfamiliar research areas. It provides a searchable research environment that helps users move from one relevant paper to connected studies and broader research topics.

Overall, Semantic Scholar is an AI-powered academic search engine that helps users discover, understand, and navigate scientific research more efficiently.