Extracting User Knowledge Graphs from a Large Knowledge Graph Using User's Utterance and SPARQL Templates for a Chatbot

Abstract

In recent years, research and development of dialogue systems have been actively carried out, and it is becoming a society in which humans and dialogue systems coexist. In order to conduct various dialogues in consideration of the user preferences in the chatbot, it is necessary to infer the knowledge possessed by the user from the user's utterance and to provide questions and topics based on the knowledge. In this research, we propose a dialogue system that allows users to chat based on the fact that some of the knowledge that the user is presumed to have from the user's utterance is extracted and accumulated from DBpedia using the SPARQL template as a user knowledge graph. It is considered that various dialogues considering the user's taste can be realized by the chat dialogue system presenting the topic based on the user knowledge graph.