Ontologia pra que te quero

quinta-feira, maio 21, 2009

Issues in learning an ontology from text

Christopher Brewster1 , Simon Jupp2 , Joanne Luciano3 , David Shotton4 , Robert D Stevens2 and Ziqi Zhang5

1Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK

2School of Computer Science, Manchester University, Oxford Road, Manchester, M13 9PL, UK

3Harvard Medical School, Avenue Louis Pasteur, Boston, MA 02115, USA

4Image Bioinformatics Research Group, Department of Zoology, South Parks Road, Oxford, OX1 3PS, UK

5Department of Computer Science, University of Sheffield, Sheffield, S1 4DP, UK
author email corresponding author email

BMC Bioinformatics 2009, 10(Suppl 5):S1doi:10.1186/1471-2105-10-S5-S1

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/S5/S1

Published: 6 May 2009
© 2009 Brewster et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus.


Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/~kiffer/animalbehaviour/ webcite.


We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.


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