Graph NLU enabled question answering system,Heliyon

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Graph NLU enabled question answering system
Heliyon ( IF 3.4 ) Pub Date : 2021-09-24 , DOI: 10.1016/j.heliyon.2021.e08035
Sandeep Varma ₁ , Shivam Shivam ₁ , Snigdha Biswas ₁ , Pritam Saha ₁ , Khushi Jalan ₁

Affiliation

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 semantic 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. 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. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. We extract appropriate answers for different types of queries which have been illustrated in the Results section.

中文翻译：

支持图形 NLU 的问答系统

随着大量信息被存储为结构化数据，越来越需要从表中检索问题的准确答案。回答有关结构化数据的自然语言问题通常涉及将查询语义解析为机器可理解的格式，然后使用该格式从数据库中检索信息。为特定领域的任务训练语义解析器是一项乏味的工作，并且不能保证准确的结果。在本文中，我们使用会话分析工具来创建用户界面并从查询中获取所需的实体和意图，从而避免了传统的语义解析方法。然后，我们利用知识图谱在结构化数据域中进行查询。知识图可以轻松地用于问答系统，将其用作数据库。我们为不同类型的查询提取适当的答案，这些答案已在结果部分中进行了说明。

更新日期：2021-09-24

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