In this work a methodology to aid the process of organizing text collections is proposed, aiming to reflect exactly the existent and recoverable publications in a specific domain

CONRADO, M. da S.

MARCACINI, R. M.

MOURA, M. F.

NOGUEIRA, B. M.

REZENDE, S. O.

English

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A proposal for building domain topic taxonomiesMaria Fernanda Moura1,2, Ricardo Marcondes Marcacini1, Bruno MagalhãesNogueira1, Merley da Silva Conrado1 and Solange Oliveira Rezende21 CNPTIA/Embrapa http://www.cnptia.embrapa.br -fernanda@cnptia.embrapa.br2 ICMC/USP http://www.icmc.usp.br - marcacini@grad.icmc.usp.br,{brunomn,merleyc,solange}@icmc.usp.brIn this work a methodology to aid the process of organizing text collectionsis proposed, aiming to reflect exactly the existent and recoverable publicationsin a specific domain [3]. Although the methodology is completely automatized,it allows the domain specialist to intervene in some of its steps; in this way, someof the comprehensibility aspects can be satisfied when necessary. The methodo-logical basis is a text mining process, which uses statistical machine learningmodels, specially exploratory and multivariate data analysis. The goals of theprocess are to identify the domain vocabulary presented in the text collection,grouping the text collection in hierarchical clusters and automatically generatinga topic taxonomy. The obtained topic taxonomy can be edited by the final usersoriented by statistical measures. Additionally, all the computational support isbeing developed in a way this permits the incorporation of new methods, whennecessary or as an alternative to the methods already incorporated. The metho-dology process can automatically decide which methods to use in some steps orcan guide the users in their decisions. The process steps are illustrated in Fig. 1.In the problem identification step, the scope and goals of the methodology ap-Fig. 1. Topic taxonomy methodology processplication are defined. In the preprocessing, the documents are standardized, theI Workshop on Web and Text Intelligence 832 Moura, M.F. Marcacini, R.M. Nogueira, B.M. Conrado, M.S. Rezende, S.O.interesting attributes are found and the attribute space dimension is reduced.This is a complex step and it is the keyprocess, because the quality of the allobtained results is going to depend on it. At the end of this step an attribute-value matrice can be constructed. In the pattern extraction step, a hierarchicalcluster algorithm is used and the resulting groups are automatically labeled [4].A label is a term or a set of terms that can discriminate the cluster. This clusterlabelling process generates a taxonomy in which there are not term repetitionsalong the labels in the same hierarchical branch; because a specific node has itsspecific label and it can have children that inherit its label. The labelling methoddoes an objective analysis of all the terms selecting those that are discriminativefor a cluster and eliminating those that do not contribute to discriminate anycluster. An objective and a subjective evaluation of the generated taxonomy aredone in the pos-processing step. If the generated taxonomy is not satisfactoryenough, the user can try to identify some criteria to return to other parts of theprocess. On the other hand, the taxonomy can be refined. The domain specialistscan edit and evaluate their modifications over the taxonomy aided by statisti-cal measures. Additionally, they can create new terms based on the taxonomyterms. Finally, the taxonomy can be used to facilitate the process of organizing,understanding and retrieving information. Moreover, the obtained taxonomy canbe used as decision support data, because the topics represent different tenden-cies. The proposed methodology has been used in production experiments withspecific goals, as the case studies of scientifc production tendencies in beef anddary cattle along with Embrapa Pecuária Sudeste [2] and a digital library orga-nization [1]. For the research, artifical intelligence text collections has been usedfor prospection and validation of new methods due to the proximity of domainspecialists in this area with the project 3.The authors would like to thank CAPES, CNPq and IFM for the technicaland finance support.References1. R. M. Marcacini, M. F. Moura, and S. O. Rezende. Biblioteca digital do ifm: umaaplicação para a organização da informação através de agrupamentos hierárquicos.In XIII Brazilian Symposium on Multimedia and the Web - WDL 2007 - III Work-shop on Digital Libraries, volume CD-ROM of WDL, pages 1–16, 2007.2. C. A. Mazzari. Gestão de pessoas e identificação de competências estratégicas emunidades descentralizadas da embrapa - o caso embrapa pecuária sudeste, 2007. Re-search and Development Project, Status: active, Research center: Embrapa PecuáriaSudeste (http://www.cppse.embrapa.br).3. M. F. Moura, R. M. Marcacini, B. M. Nogueira, M. S. Conrado, and S. O. Rezende.Uma abordagem completa para a construção de taxonomias de tópicos em umdomı́nio. Technical Report 329, ICMC/USP, 2008.4. M. F. Moura and S. O. Rezende. Choosing a hierarchical cluster labelling methodfor a specific domain document collection. In New Trends in Artificial Intelligence.,chapter 11, pages 812–823. EPIA- Encontro Portugues de Inteligência Artificial,Guimarães, Portugual, 1 edition, 2007.3 Text collections are available at http:www.labic.projects.84 WTI 2008

A proposal for building domain topic taxonomies.

http://www.alice.cnptia.embrapa.br/bitstream/doc/9618/1/PLProposalWTI2008.pdf

A proposal for building domain topic taxonomies.

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