A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case Study

• URL: http://arxiv.org/abs/2007.00747v2
• Date: Thu, 17 Sep 2020 17:28:08 GMT
• Title: A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case Study
• Authors: Yusuf Sermet and Ibrahim Demir
• Abstract summary: Instant Expert is an open-source semantic web framework to build and integrate voice-enabled smart assistants. The presented framework utilizes advanced web technologies to ensure reusability and reliability. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e. chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data utilization. The presented framework utilizes advanced web technologies to ensure reusability and reliability, and an inference engine for natural language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework's usage and benefits.

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