dc.creatorAmanqui, Flor Karina Mamani
dc.creatorSerique, Kleberson Junio do Amaral
dc.creatorCardoso, Silvio Domingos
dc.creatorSantos, José L. dos
dc.creatorAlbuquerque, Andrea
dc.creatorMoreira, Dilvan de Abreu
dc.date.accessioned2015-03-24T19:10:33Z
dc.date.accessioned2018-07-04T17:03:33Z
dc.date.available2015-03-24T19:10:33Z
dc.date.available2018-07-04T17:03:33Z
dc.date.created2015-03-24T19:10:33Z
dc.date.issued2014-08
dc.identifierIEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2014, Warsaw.
dc.identifier9781479941438
dc.identifierhttp://www.producao.usp.br/handle/BDPI/48658
dc.identifierhttp://dx.doi.org/10.1109/WI-IAT.2014.34
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1644087
dc.description.abstractDue to the increased amount of available biodiversity data, many biodiversity research institutions are now making their databases openly available on the web. Researchers in the field use this databases to extract new knowledge and also share their own discoveries. However, when these researchers need to find relevant information in the data, they still rely on the traditional search approach, based on text matching, that is not appropriate to be used in these large amounts of heterogeneous biodiversity’s data, leading to search results with low precision and recall. We present a new architecture that tackle this problem using a semantic search system for biodiversity data. Semantic search aims to improve search accuracy by using ontologies to understand user objectives and the contextual meaning of terms used in the search to generate more relevant results. Biodiversity data is mapped to terms from relevant ontologies, such as Darwin Core, DBpedia, Ontobio and Catalogue of Life, stored using semantic web formats and queried using semantic web tools (such as triple stores). A prototype semantic search tool was successfully implemented and evaluated by users from the National Research Institute for the Amazon (INPA). Our results show that the semantic search approach has a better precision (28% improvement) and recall (25% improvement) when compared to keyword based search, when used in a big set of representative biodiversity data (206,000 records) from INPA and the Emilio Gueldi Museum in Pará (MPEG). We also show that, because the biodiversity data is now in semantic web format and mapped to ontology terms, it is easy to enhance it with information from other sources, an example using deforestation data (from the National Institute of Space Research - INPE) to enrich collection data is shown.
dc.languageeng
dc.publisherUniversity of Warsaw
dc.publisherInstitute of Electrical and Electronics Engineers - IEEE
dc.publisherWeb Intelligence Consortium - WIC
dc.publisherAssociation for Computing Machinery - ACM
dc.publisherWarsaw
dc.relationIEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies
dc.rightsCopyright IEEE
dc.rightsclosedAccess
dc.subjectSemantic Web
dc.subjectSemantic Search
dc.subjectOntology
dc.subjectData Integration
dc.subjectBiodiversity
dc.titleImproving biodiversity data retrieval through semantic search and ontologies
dc.typeActas de congresos


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