Semantic Scholar: re-inventing Academic Search

We launched Semantic Scholar this morning.

Semantic Scholar Utilizes Artificial Intelligence Methods to Transform Scientific Search for Computer Scientists

The Allen Institute for Artificial Intelligence (AI2) today officially launched its free Semantic Scholar service, which will allow scientific researchers to quickly cull through the millions of scientific papers published each year to find those most relevant to their work. Leveraging AI2’s expertise in data mining, natural-language processing and computer vision, Semantic Scholar provides an AI-enhanced way to quickly search and discover information. At launch, the system searches over three million computer science papers, and will add scientific categories on an ongoing basis.

“No one can keep up with the explosive growth of scientific literature,” said Dr. Oren Etzioni, CEO at AI2. “Which papers are most relevant? Which are considered the highest quality? Is anyone else working on this specific or related problem? Now, researchers can begin to answer these questions in seconds, speeding research and solving big problems faster.”

With Semantic Scholar (semanticscholar.org), computer scientists can:

• Home in quickly on what they are looking for, with advanced selection filtering tools. Researchers can filter search results by author, publication, topic, and date published. This gets the researcher to the most relevant result in the fastest way possible, and reduces information overload.

• Instantly access a paper’s figures and findings. Unique among scholarly search engines, this feature pulls out the graphic results, which is often what a researcher is really looking for.
• Jump to cited papers and references and see how many researchers have cited each paper, a good way to determine citation influence and usefulness.
• Be prompted with key phrases within each paper to winnow the search further.

How Semantic Scholar works

Using machine reading and vision methods, Semantic Scholar crawls the web, finding all PDFs of publically available scientific papers on computer science topics, extracting both text and diagrams/captions, and indexing it all for future contextual retrieval. Using natural language processing, the system identifies the top papers, extracts filtering information and topics, and sorts by what type of paper and how influential its citations are.  It provides the scientist with a simple user interface (optimized for mobile) that maps to academic researchers’ expectations. Filters such as topic, date of publication, author and where published are built in. It includes smart, contextual recommendations for further keyword filtering as well. Together, these search and discovery tools provide researchers with a quick way to separate wheat from chaff, and to find relevant papers in areas and topics that previously might not have occurred to them.

Semantic Scholar builds from the foundation of other research-paper search applications such as Google Scholar, adding AI methods to overcome information overload.

Caption: Semantic Scholar’s modern interface provides computer science researchers to find relevant research papers in seconds.

Said Etzioni, “Semantic Scholar is a first step toward AI-based discovery engines that will be able to connect the dots between disparate studies to identify novel hypotheses and suggest experiments that would otherwise be missed. Our goal is to enable researchers to find answers to some of science’s thorniest problems.”

About AI2

AI2 was founded in 2014 with the singular focus of conducting high-impact research and engineering in the field of artificial intelligence, all for the common good. AI2 is the creation of Paul Allen, Microsoft cofounder, and is led by Dr. Oren Etzioni, a renowned researcher in the fields of AI and search. AI2 employs more than 35 top-notch researchers and engineers, attracting individuals of varied interests and backgrounds from across the globe. AI2 prides itself on the diversity and collaboration of this team, and takes a results-oriented approach to complex challenges in AI.

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