Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/SLSS-8KDPKG
Type: Dissertação de Mestrado
Title: Utilizando Evidência da wikipedia para relacionar textos a lugares
Authors: Rafael Odon de Alencar
First Advisor: Clodoveu Augusto Davis Junior
First Referee: Mirella Moura Moro
Second Referee: Altigran Soares da Silva
Abstract: Dado que uma parcela significativa de buscas na Web apresenta alguma intenção geográfica, é importante conceber formas automáticas de associar recursos a lugares (geotagging). O presente trabalho propõe duas estratégias para geotagging de textos usando a Wikipedia como fonte de evidência geográfica. Primeiro, propõe-se a classificação automática de textos com base na ocorrência de palavras-chave extraídas da Wikipedia para um conjunto de lugares. Em seguida, é proposto basear-se numa técnica de identificação de tópicos auxiliada pela Wikipedia, onde os tópicos encontrados conectam textos ao grafo da Wikipedia, permitindo a busca por lugares relacionados. Experimentos avaliaram a precisão do geotagging em uma coleção de documentos associados a estados brasileiros. Demonstrou-se a viabilidade do uso da Wikipedia como fonte de evidência geográfica, beneficiando-se de seu conhecimento livre, amplo e atualizado e apresentando uma alternativa ou extensão aos dicionários geográficos(gazetteers) em tarefas de recuperação de informação geográfica.
Abstract: Obtaining or approximating a geographic location for search results often motivates users to include place names and other geography-related terms in their queries. Previous work shows that queries that include geography-related terms correspond to a significant share of the users demand. Therefore, it is important to recognize the association of documents to places in order to adequately respond to such queries. This dissertation describes strategies for the geographic scope computation, using Wikipedia as an alternative source of direct and indirect geographic references. First we propose to perform a text classification task on geography-related classes, using textual evidence extracted from Wikipedia. We use terms that correspond to articles titles and the connections between articles in Wikipedias graph to establish a semantic network from which classification features are generated. Results of experiments using a news data-set, classified over Brazilian states, show that such terms constitute a valid evidence set for the geographic classification of documents, and demonstrate the potential of this technique for text classification. Another proposal describes a strategy for tagging documents with multiple place names, according to the geographic context of their textual content, using a topic indexing technique that considers Wikipedia articles as a controlled vocabulary. By identifying those topics in the text, we connect documents with the Wikipedia semantic network of articles, allowing us to perform operations on Wikipedias graph and find related places. We present an experimental evaluation on documents tagged as Brazilian states, demonstrating the feasibility of our proposal and opening the way to further research on geotagging based on semantic networks. Our results demonstrates the feasibility of using Wikipedia as an alternative source of geographical references. The method\\\'s main advantage is the use of free, up-to-date and wide knowledge and information from the digital encyclopedia. Finally, the Wikipedia introduction to the geographic text analysis can be faced as both, an alternative and a extension to use of geographical dictionaries (i. e. gazetteers).
Subject: Computação
Sistemas de informação geografica
Sistemas de recuperação da informação
language: Inglês
Publisher: Universidade Federal de Minas Gerais
Publisher Initials: UFMG
Rights: Acesso Aberto
URI: http://hdl.handle.net/1843/SLSS-8KDPKG
Issue Date: 29-Jul-2011
Appears in Collections:Dissertações de Mestrado

Files in This Item:
File Description SizeFormat 
rafaelodonalencar.pdf3.09 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.