An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
July 19, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Matteo Manica, Christoph Auer, Valery Weber, Federico Zipoli, Michele Dolfi, Peter Staar, Teodoro Laino, Costas Bekas, Akihiro Fujita, Hiroki Toda, Shuichi Hirose, Yasumitsu Orii
arXiv ID
1907.08400
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG
Citations
10
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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