Semantic enrichment of life sciences content: how it works and key benefits for researchers

Abstract

In the time it takes a human life sciences researcher to read one research article machines can process hundreds of thousands of articles. An unco-ordinated army of bots, crawlers, and other software agents are active day and night on the Internet discovering, ingesting, and analyzing research content. Many of these agents are designed to help researchers rapidly filter the ever-expanding research record and surface the articles and findings most relevant to their work. For these software agents to be most effective, they need to understand the content they are reading in a manner similar to an expert human reader. (What are the main concepts being discussed and what are the main findings asserted? What is this research article telling us that is new and what is supporting or contradicting past findings?). This is where semantic enrichment comes into play — semantic enrichment adds structured machine-readable metadata to life science articles to assist software agents in ‘reading’ the content in a manner similar to a human researcher. In the present study, I'll define the mechanism of semantic enrichment of life sciences content, examine the benefits it is bringing to researchers today, and preview promising avenues for future benefits.