Semantic Scholara searchable database of ~228 million citations w/ content from PubMed.gov, arXiv & major publishers such as Springer Nature and Wiley.
Note:OpenAI is leading in the general AI space, but independent and big AI companies are developing new tools all the time, and experimenting with AI-powered academic semantic searching in support of research. How will these tools affect our traditional bibliographic databases?
Semantic Scholar is a free AI-powered academic search tool (search engine, perhaps, and database of scholarly papers) developed by the Allen Institute for Artificial Intelligence. SS uses natural language processing and machine learning to locate relevant research papers, extracting insights from papers, summaries, citations, and influential references from its corpus of 230 million citations. The database is created by crawling content from PubMed.gov, arXiv, and major publishers such as Springer Nature and Wiley.
Semantic Scholar is able to search across across a range of disciplines, and features citation graphs and author profiles as features. Unlike traditional search engines, it prioritizes semantic understanding of text in order to deliver more precise results for academic queries.
AI-powered search tools use Semantic Scholar's API
Several AI-powered search tools leverage Semantic Scholar's corpus of over 228 million academic papers, through its Academic Graph API (application programming interface) to enhance their research capabilities. Here are some notable ones:
Asta (agentic AI by Ai2) from the Allen Institute for AI (Ai2) is an AI-powered platform designed to accelerate scientific research, utilizing Semantic Scholar's API; integrated, open-source ecosystem that includes an AI research assistant, a benchmark suite (AstaBench), and developer resources (Asta Resources) to support scientific workflows; not strictly a search tool but a broader framework that includes tools for finding relevant research papers, summarizing literature, and analyzing data, among other functions.
Connected Papers — uses Semantic Scholar’s data to create visual maps of related studies, showing citation relationships and relevancy percentages. It’s particularly useful for exploring connected research papers in a field.
Consensus — AI search engine that uses Semantic Scholar’s corpus to find answers in scientific research, offering insights by aggregating and summarizing findings from relevant papers. It provides a free plan with limited features and a premium option.
Elicit.com — searches Semantic Scholar’s database to find relevant papers, extract data, and summarize findings. It uses the full text when available or abstracts otherwise, focusing on research question answering and literature review assistance. Accuracy is estimated at around 90%, with users encouraged to verify results.
Research Rabbit — visualization-based literature review tool that sources papers from Semantic Scholar, CrossRef, and OpenAlex. It provides visual graphs of related studies, citation networks, and recommendations for similar, earlier, or later works.
SciSpace — uses Semantic Scholar’s database with a proprietary combination of semantic and keyword search; offers features like AI-generated summaries, paper recommendations, and a Chrome extension to enhance Google Scholar searching.
Undermind.ai — uses the Semantic Scholar as a primary data source for its AI-powered research assistant. Undermind conducts comprehensive searches of abstracts and titles on Semantic Scholar, leveraging the API to access its extensive database of scientific papers. This integration allows Undermind to perform iterative searches and deliver precise, relevant results for complex research queries.
Retrieval Augmented Generation (RAG)
Many (if not all) of the AI-powered search tools use retrieval augmented generation (RAG) techniques to deliver results. RAG refers to a technique combining the strengths of retrieval-based and generative AI models. In RAG, an AI system first retrieves information from a large dataset or knowledge base (such as Semantic Scholar) and then uses this retrieved data to generate a response or output. Essentially, the RAG model augments the generation process with additional context or information pulled from relevant sources.
Presentation (product demo)
This presentation has been selected by a librarian for its links to the product. Consider it a marketing tool and not an evaluation.
"... This research evaluates the performance of platforms such as SciSpace, Elicit, ResearchRabbit, Scite.ai, Consensus, Claude.ai, ChatGPT, Google Gemini, Perplexity, and Microsoft Co-Pilot across the key stages of SLRs—planning, conducting, and reporting. While these tools significantly enhance workflow efficiency and accuracy, challenges remain, including variability in result quality, limited access to advanced features in free-tier versions, and the necessity for human oversight to validate outputs..."
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