Esta demo apresenta SemanticSUS, um portal semântico para acesso, análise e visualização de grande quantidade de dados do Sistema Único de Saúde (SUS). O Portal SemanticSUS é baseado em um enfoque que combina on- tologias e Dados Interligados para enfrentar os desafios no desenvolvimento de aplicações onde existe a necessidade de integrar fontes de dados heterogêneas. O SemanticSUS tem como principal objetivo oferecer uma camada ontológica, conectada semanticamente aos dados, e que permita o acesso integrado aos dados. A plataforma disponibiliza também o serviço de integração semântica baseado na abordagem “pay-as-you-go”, o que garante flexibilidade e exten- sibilidade suficientes para que novas fontes de dados possam ser adicionadas ao portal. Outra facilidade do portal é a ferramenta “Mashup Buider”, a qual permite a construção de Mashup de Dados de forma simples e automática.
Brazil is one of the countries with the highest level of drug consumption in the world. By 2012 about 66% claimed to practice self-medication. Such activity can lead to a wide range of risks, including death from drug intoxication. Studies indicate that a lack of knowledge about drugs and their dangers is one of the main aggravating factors in this scenario. This work aims to universalize access to information about medications and their risks for different user profiles, especially Brazilian and lay users. In this paper, we presented the construction process of a Linked Data Mashup (LDM) integrating the datasets: consumer drug prices, government drug prices and drug's risks in pregnant from ANVISA and SIDER from BIO2RDF. In addition, this work presents MediBot, an ontology-based chatbot capable of responding to requests in natural language in Portuguese through the instant messenger Telegram, smoothing the process to query the data. MediBot acts like a native language query interface on an LDM that works as an abstraction layer that provides an integrated view of multiple heterogeneous data sources.
In this article, we present the MediBot. MediBot is a chatbot for querying drugs information. The presented system acted as a single access point for natural and simplified information retrieval of drugs, prices, and its risks. The chatbot has two modes of operation: Quick Response and Interactive modes. The first answers questions asked in natural language, while the second has three interactive tasks, namely Browser, Query, and Price Comparison. We present here the system architecture, the Linked Data Mashup’s construction process, and Chatbot MediBot’s activities modes, focusing on the new Price Comparison’s task. This task presents the best prices for medicines and their best potential substitutes extracted in real-time from the Web with the help of the information obtained from a linked data mashup.
Self-medication without medical advice can lead to health problems through intoxication. The objective of this study is to present MediBot -a chatbot to consult information about medications and their risks. MediBot enables queries through natural language, transformed them into SPARQL queries over a Linked Data Mashup about data on medicines provided by ANVISA and Sider sources. Finally, MediBot presents itself as a timely tool in promoting access to information by the general public.Resumo. A automedicação sem orientação médica pode vir a acarretar problemas de saúde por meio da intoxicação.O objetivo deste estudo consiste em apresentar MediBot -um chatbot para consulta de informações sobre medicamentos e seus riscos. O MediBot permite a realização de consultas através de linguagem natural, transformado-as em consultas SPARQL sobre um Linked Data Mashup envolvendo dados sobre medicamentos providos pelas fontes ANVISA e Sider. Por fim, MediBot apresenta-se como instrumento oportuno na promoção do acessoà informação por parte do público geral.
The purpose of this work is to use Linked Data and Semantic Web techniques to construct a mashup with financial execution data from PDDE and educational indicators. At the end of the process an RDF dataset was generated containing an integrated view of the sources. Resumo. O objetivo deste trabalhoé utilizar técnicas de Dados Ligados e Web Semântica para a construção de um mashup com dados de execução financeira do PDDE e de indicadores educacionais. Ao final do processo foi gerado um dataset RDF contendo uma visão integrada das fontes.
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