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Bioinformatics Advance Access originally published online on October 15, 2008
Bioinformatics 2008 24(24):2940-2941; doi:10.1093/bioinformatics/btn534
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

BioCaster: detecting public health rumors with a Web-based text mining system

Nigel Collier 1,2,*, Son Doan 1, Ai Kawazoe 1, Reiko Matsuda Goodwin 1,3, Mike Conway 1, Yoshio Tateno 4, Quoc-Hung Ngo 5, Dinh Dien 5, Asanee Kawtrakul 6, Koichi Takeuchi 7, Mika Shigematsu 8 and Kiyosu Taniguchi 8

1National Institute of Informatics, ROIS, 2PRESTO, Japan Science and Technology Corporation, Tokyo 101-8430, Japan, 3Department of Anthropology, Lehman College, CUNY, NY 10468-1589, USA, 4National Institute of Genetics, ROIS, Mishima 411-8540, Japan, 5University of Science, Vietnam National University at HCMC, Vietnam, 6NECTEC and the Department of Computer Engineering, Kasetsart University, Bangkok, Thailand, 7Okayama University, Okayama 700-8530 and 8National Institute of Infectious Diseases, Tokyo 162-8640, Japan

*To whom correspondence should be addressed.


  Abstract

Summary: 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.

Availability: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org.

Contact: collier{at}nii.ac.jp

Associate Editor: Jonathan Wren

Received on June 11, 2008; revised on October 7, 2008; accepted on October 9, 2008

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