Please use this identifier to cite or link to this item: https://repositorio.inpa.gov.br/handle/1/19989
Title: Applying ontology for amazon biodiversity data extraction
Authors: Albuquerque, Andréa Corrêa Flôres
Campos dos Santos, José L.
Keywords: Biological Collections
Computer Technology
Conventional Approach
Data Extraction
Data Heterogeneity
Database Design
Scientific Documents
Scientific Studies
Semantic Data
Semi Structured Data
Semi-structured
Cybernetics
Data Mining
Experiments
Information Management
Information Science
Ontology
Biodiversity
Issue Date: 2005
Publisher: WMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
metadata.dc.relation.ispartof: Volume 1, Pags. 54-59
Abstract: The richest region in biodiversity in the world, the Amazon, became object of extreme attention and target for multidisciplinary scientific studies. Collected data from forests and rivers' expeditions generate a large volume of data that are managed supported by databases technology that are useful for strategic planning of the future of the region and its contribution to the planet. The data treated and manipulated by each experiment constitute, strategic information for others scientific studies. The data used by scientific experiments in the Amazon are described in a semi-structured form, which make conventional approaches of data modelling inappropriate for the process of database design. The infrastructure and technologies to support semi-structured data must offer solutions for the problems created by the data heterogeneity. Computer technology is a fundamental resource applied for biodiversity information management. In the scope of our study, the need of using ontology for extracting semi-structured data of scientific documents appears, taking into account the semantic aspects of these data and its need of interoperability. This paper presents an approach for semantic data extraction using ontology, the integration aspects of the suitable ontology applied to Amazonian biodiversity data documents.
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