Unsupervised structure discovery for biodiversity information

The project presented concerns with improving the access to biodiversity information in legacy formats. The majority of biodiversity information, for example, floras or faunas, is still in legacy format. To mobilize these information resources, a variety of techniques have been used to 1) fit the bi...

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Vydané v:International Conference on Digital Libraries: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries; 11-15 June 2006 s. 382
Hlavní autori: Cui, Hong, McCourt, Richard M., Feist, Monique
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 11.06.2006
IEEE
Edícia:ACM Conferences
Predmet:
Applied computing > Physical sciences and engineering > Mathematics and statistics
Biodiversity
Buildings
Computing methodologies > Symbolic and algebraic manipulation > Computer algebra systems
Data mining
document structure
Information resources
Machine learning
North America
Prototypes
Relational databases
Tagging
unsupervised machine learning
XML
unsupervised machine learning
document structure
ISBN:1595933549, 9781595933546
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Shrnutí:The project presented concerns with improving the access to biodiversity information in legacy formats. The majority of biodiversity information, for example, floras or faunas, is still in legacy format. To mobilize these information resources, a variety of techniques have been used to 1) fit the biodiversity information into the predefined structure of, typically, a relational database, using techniques such as information extraction, 2) make explicit the inherent yet implicit semantic structure in the documents, using techniques such as XML tagging. In either case, the expected outcome is to structure the originally less structured information prepared by taxonomists. In current research, domain experts and existing literature seem to play a crucial role in defining the target structure, either the templates for information extraction tasks, or the XML schema for the markup tasks.
Bibliografia:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:1595933549
9781595933546
DOI:10.1145/1141753.1141878