Paper Info
Title
Argo: an integrative, interactive, text mining-based workbench supporting curation.
Abstract
Curation of biomedical literature is often supported by the automatic analysis of textual content that generally involves a sequence of individual processing components. Text mining (TM) has been used to enhance the process of manual biocuration, but has been focused on specific databases and tasks rather than an environment integrating TM tools into the curation pipeline, catering for a variety of tasks, types of information and applications. Processing components usually come from different sources and often lack interoperability. The well established Unstructured Information Management Architecture is a framework that addresses interoperability by defining common data structures and interfaces. However, most of the efforts are targeted towards software developers and are not suitable for curators, or are otherwise inconvenient to use on a higher level of abstraction. To overcome these issues we introduce Argo, an interoperable, integrative, interactive and collaborative system for text analysis with a convenient graphic user interface to ease the development of processing workflows and boost productivity in labour-intensive manual curation. Robust, scalable text analytics follow a modular approach, adopting component modules for distinct levels of text analysis. The user interface is available entirely through a web browser that saves the user from going through often complicated and platform-dependent installation procedures. Argo comes with a predefined set of processing components commonly used in text analysis, while giving the users the ability to deposit their own components. The system accommodates various areas and levels of user expertise, from TM and computational linguistics to ontology-based curation. One of the key functionalities of Argo is its ability to seamlessly incorporate user-interactive components, such as manual annotation editors, into otherwise completely automatic pipelines. As a use case, we demonstrate the functionality of an in-built manual annotation editor that is well suited for in-text corpus annotation tasks.
Year
DOI
Venue
2012
10.1093/database/bas010
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Keywords
DocType
Volume
data mining,biomedical research
Journal
2012
ISSN
Citations 
PageRank 
1758-0463
37
1.27
References 
Authors
16
4
Name
Order
Citations
PageRank
Rafal Rak138218.30
Andrew Rowley21058.87
William J. Black314719.83
Sophia Ananiadou42658183.08