Information extraction from nanotoxicity related publications

High-quality experimental data are important when developing predictive models for studying nanomaterial environmental impact (NEI). Given that raw data from experimental laboratories and manufacturing workplaces are usually proprietary and small-scaled, extracting information from publications is an attractive alternative for collecting data. We developed an information extraction system that can extract useful information from full-text nanotoxicity related publications. This information extraction system consists of five components: raw data transformation into machine readable format, data preprocessing, ontology-based named entity recognition, rule-based numerical attribute extraction from both tables and unstructured text, and relation extraction among entities and attributes. The information extraction system is applied on a dataset made of 94 publications, and results in an acceptable accuracy. By storing extracted data into a table according to relations among the data, a dataset that can be used to predict nanomaterial environmental impact is obtained. Such a system is unique in current nanomaterial community, and can help nanomaterial scientists and practitioners quickly locate useful information they need without spending lots of time reading articles.