Semantic Scholar – Free Scientific Literature Search and Discovery
With millions of research papers published every year, there is a huge information overload in scientific literature search. Semantic Scholar leverages our AI expertise to help researchers find the most relevant information efficiently. We utilize methods from data mining, natural-language processing, and computer vision to create powerful new search and discovery experiences. Starting with Computer Science in 2015, we plan to scale the service to additional scientific areas over the next few years in support of AI2’s mission of “AI for the Common Good”. Semantic Scholar features: a) Ability to provide an overview or quickly find the most relevant survey papers for a topic; b) Filtering search results using automatically generated facets like authors and venues; c) Identifying “key” citations to overcome citation overload; d) Extracting and making figures and captions more easily accessible; and e) Identifying and presenting useful concepts and their relationships. How Semantic Scholar works: 1) PDF Extraction – State of the art PDF extraction mechanisms specifically targeted to scholarly articles; 2) Targeted Search Index – For serving relevant results for queries specific to the academic domain; and 3) Customized GUI – A user interface tailored to academic search with features supporting the academic community. This will be added to Academic and Scholar Search Engines and Sources white paper. This will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Web Data Extractors white paper. This will be added to Internet Expert Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™ Information Blog.