Event-Centralized Management of Geographic Information Collected from Blog

Yasumura, Shoko; Ikezaki, Masakazu; Watanabe, Toshio; Ushiama, Taketoshi

doi:10.1007/978-3-540-74827-4_97

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…While GeoTacker presents contents in spatial and temporal dimensions, NewsStand presents contents in spatial and thematic dimensions. In [18], a method to automatically extract events from blog entries that describe events as experienced in the real world is presented. We can find contents related to events of the real world described along the three dimensions-spatial, temporal, and thematic.…”

Section: Related Workmentioning

confidence: 99%

Phenomena-Based Management of Geotime-Tagged Contents on the Web

Kim

Zettsu

Kidawara

et al. 2010

2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications

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Recently, Geoweb services help people easily create and share geo-spatiotemporal contents through Internet. As a result, it becomes an emerging issue to manage Geoweb contents in exploding lots of spatiotemporal information and to obtain comprehensive knowledge about the real world using web contents. This paper proposes a novel approach for managing geotime-tagged contents on the basis of moving phenomena, such as typhoons, earthquakes, and flooding. In this study, a moving phenomenon is represented by a predefined three-dimensional object in a spatiotemporal domain with predefined meanings, and we define a phenomena-based predicate template named Relates as a combination of spatial, temporal, and thematic constraints. By using the Relates predicate, we can dynamically aggregate geotime-tagged contents along three aspects: location, time, and topic. Moreover, we present an application of the phenomena-based contents management, called k-Sticker. k-Sticker allows users to find top-k terms related to their own moving phenomenon through a threedimensional (two spatial dimensions and one temporal dimension) visualization method.

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Section: Related Workmentioning

confidence: 99%

Phenomena-Based Management of Geotime-Tagged Contents on the Web

Kim

Zettsu

Kidawara

et al. 2010

2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications

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“…Previous researches on the word level and the sentence level are included in this research as [2], [3], [4] and [7].…”

Section: Related Workmentioning

confidence: 99%

“…In 2007, [3] propose concert and baseball events extraction from web blogs using words that are nouns, adjectives, and verbs. The research conducted in [3] only selected words within the top 1000 frequency rank for weight determination by TFIDF of each word. The event is determined from the documents' topic name, which selected by words' TFIDF threshold.…”

Section: Related Workmentioning

confidence: 99%

“…The previous researches [2], [3], and [4] worked on the relation between/among a verb, an adjective and a noun without concerning either the noun ellipsis or the theme extraction. Therefore, this research propose using the Naïve Bayes to learn the (vn)taco-occurrence with the noun ellipsis for the tourism activity extraction.…”

Section: Related Workmentioning

confidence: 99%

“…Apriori algorithm was applied to determine the association rules for implying the tourism activity from all words in term of nouns and verbs. In 2007, Yasumura S. et al [3] used term frequency-inverse document frequency (TFIDF) technique to extracting events from web blogs. And, Diarmuid Ó Séaghdha and Ann Copestake [4] determined word co-occurrence between two nouns without any noun ellipsis by using Support Vector Machine (SVM) for identifying the compound words.…”

Section: Introductionmentioning

confidence: 99%

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Touristic Destinations’ Theme Determination for GIS Applications

Savavibool

Pechsiri

2011

AMM

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This research aims to determine touristic destination’s theme (especially tourism activity theme) from the tourism web documents for geographic information system (GIS) applications, i.e. guiding the main interesting tourism activities to tourists. There are two major problems of the theme acquisition; tourism activity extraction and tourism activity generalization. Therefore, this research proposes of using Naïve Bayes Classifier to determine word co-occurrences between verbs and nouns with the tourism activity concept from web documents. Furthermore, this research also applies the fuzzy concept along with the imputation technique, to determine the tourism activity theme by generalizing the extracted tourism activity. The result of the tourism activity extraction shows successfully the precision and recall of 85% and 77%, respectively, with Mean Reciprocal Rank (MRR) of the tourism activity theme is 0.5.

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