Abstract
With a huge amount of information being stored as structured data, there is an increasing need for retrieving exact answers to questions from tables. Answering natural language questions on structured data usually involves se-mantic parsing of query to a machine understandable format which is then used to retrieve information from the database. Training semantic parsers for domain specific tasks is a tedious job and does not guarantee accurate results. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. In this paper, we used conversational analytics tool to create the user interface and to get the required entities and intents from the query thus avoiding the traditional semantic parsing approach. We then make use of Knowledge Graph for querying in structured data domain. We extract appropriate answers for different types of queries which have been illustrated in the Results section.
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Varma, S., Ramkrishnan, A., Shivam, S., Natarajan, S., Biswas, S., Anukriti, A. (2021). Graph NLU Enabled Question Answering System. In: Meesad, P., Sodsee, D.S., Jitsakul, W., Tangwannawit, S. (eds) Recent Advances in Information and Communication Technology 2021. IC2IT 2021. Lecture Notes in Networks and Systems, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-79757-7_21
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DOI: https://doi.org/10.1007/978-3-030-79757-7_21
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