| Title | A Conversational Agent for Database Query: A Use Case for Thai People Map and Analytics Platform |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Simud, Thikamporn, Ruengittinun, Somchoke, Surasvadi, Navaporn, Sanglerdsinlapachai, Nuttapong, Plangprasopchok, Anon |
| Conference Name | 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) |
| Keywords | artificial intelligence, chatbot, Complexity theory, conversational agent, conversational agents, Data analysis, data analytics, Data visualization, Engines, Human Behavior, Metrics, Poverty alleviation, pubcrawl, Scalability, Transforms, visual databases |
| Abstract | Since 2018, Thai People Map and Analytics Platform (TPMAP) has been developed with the aims of supporting government officials and policy makers with integrated household and community data to analyze strategic plans, implement policies and decisions to alleviate poverty. However, to acquire complex information from the platform, non-technical users with no database background have to ask a programmer or a data scientist to query data for them. Such a process is time-consuming and might result in inaccurate information retrieved due to miscommunication between non-technical and technical users. In this paper, we have developed a Thai conversational agent on top of TPMAP to support self-service data analytics on complex queries. Users can simply use natural language to fetch information from our chatbot and the query results are presented to users in easy-to-use formats such as statistics and charts. The proposed conversational agent retrieves and transforms natural language queries into query representations with relevant entities, query intentions, and output formats of the query. We employ Rasa, an open-source conversational AI engine, for agent development. The results show that our system yields Fl-score of 0.9747 for intent classification and 0.7163 for entity extraction. The obtained intents and entities are then used for query target information from a graph database. Finally, our system achieves end-to-end performance with accuracies ranging from 57.5%-80.0%, depending on query message complexity. The generated answers are then returned to users through a messaging channel. |
| DOI | 10.1109/iSAI-NLP51646.2020.9376833 |
| Citation Key | simud_conversational_2020 |
A Conversational Agent for Database Query: A Use Case for Thai People Map and Analytics Platform