A London-based artificial intelligence (AI) startup raised $1 million in seed funding to develop automated “machine reading” programs that aim to digest and distill the expansive mass of documents in biomedical literature and research. Fierce Biotech brought this interesting topic to us in their article, “Startup aims to use AI to spot connections in published research.”
This software was designed to process millions of documents, connect the relevant dots, and provide answers to relatively simple questions in the form of cause-and-effect knowledge graphs.
The semantic search processes and analytics aim to allow for instant gathering of evidence for hypotheses in drug discovery, health economics and Pharmacovigilance (or drug safety).
Semantic search seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms as they appear in the searchable data space, whether on the web or within a closed system. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching, and natural language queries to provide relevant search results.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
