ResoluteAI Unveils Conceptual Attribute Search for Science

Discovery Engine Can Search Full Abstracts, Analyzing Intent and Context of Queries

ResoluteAI, the discovery engine for science, announced the launch of Conceptual Attribute Search (CAtS), a revolutionary enhancement to existing scientific search capabilities. Through the application of artificial intelligence and scientifically focused machine learning, CAtS returns highly relevant results for extremely long, highly technical search queries.

“What excites us about CAtS is that we’ve cracked the code on how to find the documents that are most conceptually similar to a query both within a corpus and across multiple, disparate datasets,” said Dr. Sean Cantrell, Machine Learning Engineer for ResoluteAI. “With standard keyword search, the results surfaced don’t identify what’s important about the phrase, or even the full abstract you’re searching. CAtS is cutting-edge machine learning that understands the nuances of language, allowing researchers to quickly find hidden connections that help them make new discoveries.”

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The initial use case for CAtS will be within ResoluteAI’s Foundation service, where users have wanted to search for similar documents by using a complete abstract as the search query. CAtS is currently deployed on ResoluteAI’s Grants and Technology Transfer datasets and will soon be available on Patents, Clinical Trials, and Publications, which includes PubMed and arXiv. A user will be able to enter a query containing a complete set of patent claims, and the results surfaced will include similar patents and related documents from other datasets.

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“The use case we’re most excited about is applying this technology to our clients’ unstructured content with our Nebula enterprise search platform,” said Steve Goldstein, CEO of ResoluteAI. “CAtS is a far superior search technology than what exists today for uncovering the hidden connections within institutional knowledge.”

CAtS builds upon industry-standard Elasticsearch functionality with best-in-class nearest neighbor search algorithms. Using artificial intelligence, machine learning, and a new approach to document analysis, CAtS is a unique, precise, and fast technology  for searching complex and sophisticated scientific concepts.

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