Exploiting geographic references of documents in a geographical information retrieval system using an ontology-based index

Brisaboa, Nieves R.; Luaces, Miguel R.; Places, Ángeles Saavedra; Seco, Diego

doi:10.1007/s10707-010-0106-3

Cited by 17 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Diferentes tarefas são necessárias para aplicar a EI (ELLOUMI et al, 2013). Brisaboa (2010) observou que nas últimas décadas, houve um forte crescimento no número de referências geográficas presentes em notícias, páginas da internet, e outros elementos relevantes de informação.…”

Section: Referencial Teórico Extração Da Informaçãounclassified

A utilização de machine learning na identificação de elementos textuais geográficos

MAGALHÃES¹

2022

View full text Add to dashboard Cite

Editora Direitos para esta edição cedidos à Atena Editora pelos autores. Open access publication by Atena Editora Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0). O conteúdo do texto e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva do autor, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos ao autor, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.

show abstract

Section: Referencial Teórico Extração Da Informaçãounclassified

A utilização de machine learning na identificação de elementos textuais geográficos

MAGALHÃES¹

2022

View full text Add to dashboard Cite

show abstract

“…Brisaboa. Luaces, Places, and Seco () put together a spatial ontology and a gazetteer to build an index. The index is then used in GIR to solve isolated or combined spatial and textual queries.…”

Section: Related Workmentioning

confidence: 99%

Reference data enhancement for geographic information retrieval using linked data

Moura

Davis

Fonseca

2016

Transactions in GIS

View full text Add to dashboard Cite

Gazetteers are instrumental in recognizing place names in documents such as Web pages, news, and social media messages. However, creating and maintaining gazetteers is still a complex task. Even though some online gazetteers provide rich sets of geographic names in planetary scale (e.g. GeoNames), other sources must be used to recognize references to urban locations, such as street names, neighborhood names or landmarks. We propose integrating Linked Data sources to create a gazetteer that combines a broad coverage of places with urban detail, including content on geographic and semantic relationships involving places, their multiple names and related nongeographic entities. Our final goal is to expand the possibilities for recognizing, disambiguating and filtering references to places in texts for geographic information retrieval (GIR) and related applications. The resulting ontological gazetteer, named LoG (Linked OntoGazetteer), is accessible through Web services by applications and research initiatives on GIR, text processing, named entity recognition and others. The gazetteer currently contains over 13 million places, 140 million attributes and relationships, and 4.5 million non-geographic entities. Data sources include GeoNames, Freebase, DBPedia and LinkedGeoData, which is based on OpenStreetMap data. An analysis on how these datasets overlap and complement one another is also presented. K E Y W O R D S gazetteer, geocoding, linked data, knowledge bases

show abstract

“…[17] presents two types of information retrieval approaches that fall to such domain, specifically, a textual technique and a spatial technique, targeting linguistic and spatial aspects of documents, respectively. On the other hand, [13] describes a temporal analysis framework to discover the temporal dimension of a corpus.…”

Section: Geospatial Information Extraction From the Web And Text Documentioning

confidence: 99%

Spatiotemporal Information Extraction from a Historic Expedition Gazetteer

Bekele

Singh³

2016

Preprint

View full text Add to dashboard Cite

Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple site visits and their descriptions contain spatiotemporal and attributive contexts. Expeditions involve movements in space that can be represented by triplet features (location, time and description). However, such features are implicit and innate parts of textual documents. Extracting the geospatial information from these documents requires understanding the contextualized entities in the text. To this end, we developed a semi-automated framework that has multiple Information Retrieval and Natural Language Processing components to extract the spatiotemporal information from a two-volumes historic expedition gazetteer. Our framework has three basic components, namely, the Text Preprocessor, the Gazetteer Processing Machine and the JAPE (Java Annotation Pattern Engine) Transducer. We used the Brazilian Ornithological Gazetteer as an experimental dataset and extracted the spatial and temporal entities from entries that refer to three expeditioners' site visits and mapped the trajectory of each expedition using the extracted information. Finally, one of the mapped trajectories was manually compared with a historical reference map of that expedition to assess the reliability of our framework. The reference map was manually prepared in previous research work by others.

show abstract

Exploiting geographic references of documents in a geographical information retrieval system using an ontology-based index

Cited by 17 publications

References 23 publications

A utilização de machine learning na identificação de elementos textuais geográficos

A utilização de machine learning na identificação de elementos textuais geográficos

Reference data enhancement for geographic information retrieval using linked data

Spatiotemporal Information Extraction from a Historic Expedition Gazetteer

Contact Info

Product

Resources

About