Academic Journal
Graph NLU enabled question answering system
| Title: | Graph NLU enabled question answering system |
|---|---|
| Authors: | Sandeep Varma, Shivam Shivam, Snigdha Biswas, Pritam Saha, Khushi Jalan |
| Source: | Heliyon, Vol 7, Iss 9, Pp e08035- (2021) |
| Publisher Information: | Elsevier, 2021. |
| Publication Year: | 2021 |
| Collection: | LCC:Science (General) LCC:Social sciences (General) |
| Subject Terms: | Conversational analytics, Graph traversal, Knowledge graph, Natural language query, Question answering, Structured data, Science (General), Q1-390, Social sciences (General), H1-99 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2405-8440 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2405844021021381; https://doaj.org/toc/2405-8440 |
| DOI: | 10.1016/j.heliyon.2021.e08035 |
| Access URL: | https://doaj.org/article/9722a51ecffa483d80e884121a5a78e3 |
| Accession Number: | edsdoj.9722a51ecffa483d80e884121a5a78e3 |
| Database: | Directory of Open Access Journals |
| ISSN: | 24058440 |
|---|---|
| DOI: | 10.1016/j.heliyon.2021.e08035 |
| Published in: | Heliyon |
| Language: | English |