AI Based Search for Research

Making search better with semantics ...

New AI-Based Search Engines Are a 'Game Changer' for Science Research
Scientific American (11/12/16) Nicola Jones 

Artificial intelligence (AI)-based academic search engines such as Semantic Scholar and Microsoft Academic could transform scientific research and inquiry, according to proponents. Semantic Scholar from the Allen Institute for Artificial Intelligence (AI2) is designed to sort and rank academic papers with more refined content and contextual understanding than keyword-reliant search engines. Stanford University neurobiologist Andrew Huberman calls Semantic Scholar a "game changer," noting "it leads you through what is otherwise a pretty dense jungle of information." The AI2 search engine's creators say they are growing its database to encompass about 10 million research articles, mostly on computer science and neuroscience. Meanwhile, Microsoft Academic was released in May as a replacement for Microsoft Academic Search, and Microsoft Research's Kuansan Wang contrasts the tool with Semantic Scholar in several respects. He notes Semantic Scholar is more deeply invested in natural-language processing to drive searches, while Microsoft Academic, powered by Bing's semantic search capabilities, covers far more publications--160 million. Wang says the tool's recursive algorithm evaluates the most influential scientists in each sub-discipline according to whether their papers are cited by other important papers. Microsoft Research says the development of a personalizable version of Microsoft Academic also is underway ... "