Journal
BIOINFORMATICS
Volume 24, Issue 24, Pages 2940-2941Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btn534
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Funding
- Japan Science and Technology Agency (JST)
- Japan Society for the Promotion of Science [18049071]
- Research Organization of Information Systems (ROIS)
- Grants-in-Aid for Scientific Research [18049071] Funding Source: KAKEN
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BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles.
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