An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
This work addresses the problem of resource-intensive information extraction for researchers in biochemistry, aiming to reduce costs and accelerate discoveries in fields like food safety and pharmaceutics, though it appears incremental as it builds on existing knowledge graph methods.
The authors tackled the challenge of extracting and mining information from biochemical literature by developing a scalable document ingestion system that integrates data from databases and publications into a biochemistry knowledge graph (BCKG), enabling querying for known facts and generating novel insights, as demonstrated in the field of carbohydrate enzymes.
Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion. Being able to leverage this immense source of technical information helps to drastically reduce costs and time to solution in multiple application fields from food safety to pharmaceutics. We present a scalable document ingestion system that integrates data from databases and publications (in PDF format) in a biochemistry knowledge graph (BCKG). The BCKG is a comprehensive source of knowledge that can be queried to retrieve known biochemical facts and to generate novel insights. After describing the knowledge ingestion framework, we showcase an application of our system in the field of carbohydrate enzymes. The BCKG represents a way to scale knowledge ingestion and automatically exploit prior knowledge to accelerate discovery in biochemical sciences.