Authors:
Caio Viktor S. Avila
1
;
Anderson B. Calixto
1
;
Tulio Vidal Rolim
1
;
Wellington Franco
2
;
Amanda D. P. Venceslau
2
;
Vânia M. P. Vidal
1
;
Valéria M. Pequeno
3
and
Francildo Felix De Moura
1
Affiliations:
1
Department of Computing, Federal University of Ceará, Campus do Pici, Fortaleza-CE and Brazil
;
2
Federal University of Ceará, Campus de Crateús, Crateús-CE and Brazil
;
3
TechLab, Departamento de Ciências e Tecnologias, Universidade Autónoma de Lisboa Luís de Camões and Portugal
Keyword(s):
Chatbot, Data Integration, Semantic Web, Medical Informatics, Drugs.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Cloud Computing
;
Coupling and Integrating Heterogeneous Data Sources
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Natural Language Interfaces to Intelligent Systems
;
Semantic Web Technologies
;
Services Science
;
Software Agents and Internet Computing
Abstract:
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 interfac
e on an LDM that works as an abstraction layer that provides an integrated view of multiple heterogeneous data sources.
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