MediBot: An Ontology based Chatbot for Portuguese Speakers Drug’s Users

Avila, Caio Viktor S.; Calixto, Anderson B.; Rolim, Tulio Vidal; Franco, Wellington; Venceslau, Amanda Drielly Pires; Vidal, Vânia Maria Ponte; Pequeno, Valéria Magalhães; Moura, Francildo Felix De

doi:10.5220/0007656400250036

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…In that way, the system could produce recommendation results better than traditional collaborative-filtering methods considering only keyword similarity. In another application domain, such as medical, an ontology of drugs and their relevant information has been constructed for MediBot [8], which is Portuguese Speakers Drug. The knowledge base of the ontology is a combination of many data sources and expert knowledge.…”

Section: Existing Ontology-based Chatbot Modelsmentioning

confidence: 99%

“…Basing on the knowledge domain, a retrieval-based model can be used, in that, a domain ontology is able to be built for information retrieval since it is a powerful expression tool [5,6]. Some ontology-based chatbots have been built to support answering automatically questions in specific domains, e.g., shopping in e-commerce [7], drug information consultant in medical [8], and educational and professional orientation [9]. Theses chatbots help human much nowadays because of an overload of consultative jobs in many fields or being impracticable to access to a person in charge.…”

Section: Introductionmentioning

confidence: 99%

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An Ontology-Based Question Answering System for University Admissions Advising

Nguyen¹,

Ho²,

Nguyen³

2023

Intelligent Automation &Amp; Soft Computing

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Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in many fields. However, these systems depend on learning methods and training data. Therefore, it is necessary to prepare such a good dataset, but it is not an easy job. An ontology-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions. This study proposes a novel chatbot model involving ontology to generate efficient responses automatically. A case study of admissions advising at the International University-VNU HCMC is taken into account in the proposed chatbot. A domain ontology is designed and built based on the domain knowledge of university admissions using Protégé. The Web user interface of the proposed chatbot system is developed as a prototype using NetBeans. It includes a search engine reasoning the ontology and generating answers to users' questions. Two experiments are carried out to test how the system reacts to different questions. The first experiment examines questions made from some templates, and the second one examines normal questions taken from frequent questions. Experimental results have shown that the ontology-based chatbot can release meaningful and long answers. The results are analysed to prove the proposed chatbot is usable and promising.

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Section: Existing Ontology-based Chatbot Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Ontology-Based Question Answering System for University Admissions Advising

Nguyen¹,

Ho²,

Nguyen³

2023

Intelligent Automation &Amp; Soft Computing

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“…OntoVal [29], is a portable and domain-independent web tool for evaluating ontologies by non-technical domain experts. It presents the ontology in a textual way, making it readable for users with little to no knowledge on semantics, and allows users to give feedback and evaluate the correctness of the artifacts being developed.…”

Section: Related Workmentioning

confidence: 99%

Delta: A Modular Ontology Evaluation System

et al. 2021

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Ontologies are widely used nowadays. However, the plethora of ontologies currently available online, makes it really difficult to identify which ontologies are appropriate for a given task and to decide on their quality characteristics. This is further complicated by the fact that multiple quality criteria have been proposed for ontologies, making it even more difficult to decide which ontology to adopt. In this context, in this paper we present Delta, a modular online tool for analyzing and evaluating ontologies. The interested user can upload an ontology to the tool, which then automatically analyzes it and graphically visualizes numerous statistics, metrics, and pitfalls. Those visuals presented include a diverse set of quality dimensions, further guiding users to understand the benefits and the drawbacks of each individual ontology and how to properly develop and extend it.

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“…For example, Ahmad et al [31] developed a chatbot that is able to advice on the kind of drugs to be taken based on the data submitted by the user. Avila et al [32] developed a chatbot to find the best prices for medicines and suggest their best possible substitutes. Bao et al [33] suggested a hybrid model composed of a knowledge graph and a textual similarity model to construct a system for responding to medical questions using Hierarchical BiLSTM Attention Model (HBAM).…”

Section: Introductionmentioning

confidence: 99%

Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

Omoregbe

Ndaman

Misra

et al. 2020

Journal of Healthcare Engineering

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The use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.

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MediBot: An Ontology based Chatbot for Portuguese Speakers Drug’s Users

Cited by 13 publications

References 15 publications

An Ontology-Based Question Answering System for University Admissions Advising

An Ontology-Based Question Answering System for University Admissions Advising

Delta: A Modular Ontology Evaluation System

Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

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