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Search engines based on keyword retrieval can no longer adapt to the way ofinformation acquisition in the era of intelligent Internet of Things due to thereturn of keyword related Internet pages. How to quickly, accurately andeffectively obtain the information needed by users from massive Internet datahas become one of the key issues urgently needed to be solved. We propose anintelligent question-answering system based on structured KB and unstructureddata, called OpenQA, in which users can give query questions and the model canquickly give accurate answers back to users. We integrate KBQA structuredquestion answering based on semantic parsing and deep representation learning,and two-stage unstructured question answering based on retrieval and neuralmachine reading comprehension into OpenQA, and return the final answer with thehighest probability through the Transformer answer selection module in OpenQA.We carry out preliminary experiments on our constructed dataset, and theexperimental results prove the effectiveness of the proposed intelligentquestion answering system. At the same time, the core technology of each moduleof OpenQA platform is still in the forefront of academic hot spots, and thetheoretical essence and enrichment of OpenQA will be further explored based onthese academic hot spots.

OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data