Mining chemical information from open patents.


Change log
Authors
Jessop, David M 
Adams, Sam E 
Murray-Rust, Peter 
Abstract

Linked Open Data presents an opportunity to vastly improve the quality of science in all fields by increasing the availability and usability of the data upon which it is based. In the chemical field, there is a huge amount of information available in the published literature, the vast majority of which is not available in machine-understandable formats. PatentEye, a prototype system for the extraction and semantification of chemical reactions from the patent literature has been implemented and is discussed. A total of 4444 reactions were extracted from 667 patent documents that comprised 10 weeks' worth of publications from the European Patent Office (EPO), with a precision of 78% and recall of 64% with regards to determining the identity and amount of reactants employed and an accuracy of 92% with regards to product identification. NMR spectra reported as product characterisation data are additionally captured.

Description
Keywords
WORLD-WIDE-WEB, PRIMARY JOURNAL TEXT, COMPUTATIONAL-LINGUISTICS TECHNIQUES, STRUCTURE RECOGNITION, MARKUP, XML, EXTRACTION, SCIENCE
Journal Title
J Cheminform
Conference Name
Journal ISSN
1758-2946
1758-2946
Volume Title
Publisher
Springer Science and Business Media LLC
Sponsorship
We thank Unilever (DMJ’s PhD studentship) and the EPSRC (Pathways to Impact Award) for funding.