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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.

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Searching relevant, specific information in big data volumes is quite a challenging task. Despite the numerous strategies in the literature to tackle this problem, this task is usually carried out by resorting to a Question Answering (QA) systems. There are many ways to build a QA system, such as heuristic approaches, machine learning, and ontologies. Recent research focused their efforts on ontology-based methods since the resulting QA systems can benefit from knowledge modeling. In this paper, we present a systematic literature survey on ontology-based QA systems regarding any questions. We also detail the evaluation process carried out in these systems and discuss how each approach differs from the others in terms of the challenges faced and strategies employed. Finally, we present the most prominent research issues still open in the field. QA SYSTEM TYPESGiven the full range of topics involved in QA system, this paper focuses on an existing classification based on the type of response expected to be found (Latifi, 2018): Closed-Domain Question Answering (QADR), QA for Comprehension Reading (QACR), Community Question Answering (QAC), 532

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