A knowledge-based question answering system based on car manuals

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iNAGO is an international company dedicated to making computers more natural by providing a conversational assistant platform called netpeople. The netpeople platform combines innovative natural language conversational technology with tools and pre-set applications that smart device makers, such as Toyota, NEC, Clarion, and others, use to enable their own unique smart assistant service. One of iNAGO's key applications is a voice based question and answer (QA) system that will converse with users to obtain the most appropriate answers to their question. Companies such as Brother Industries, Toyota and others are currently using this system. iNAGO is looking to work with York to drastically improve this system and meet the needs of its customers. The York and iNAGO research team will employ a stepwise approach to build and test an in-car interactive QA system. This system works as the driver's navigator and digital concierge, enabling them to safely manage information and drive effectively at the same time. One of the major challenges in developing such an interactive QA system is the design and generation of a knowledge base for question answering. The current knowledge base used in iNAGO QA systems was mainly manually created, which was very time-consuming and not easy to maintain. In this Engage project, we will use machine learning techniques (such as topic modeling) to automatically create a knowledge base for an in-car QA system based on a car manual in order to answer questions that the user may have regarding the usage, operation and maintenance of the car. We will test our knowledge base by processing users' questions and providing answers to the users.