Knowledge Triggering, Extraction and Storage via Human-Robot Verbal Interaction

TL;DR

This paper presents a novel method for real-time knowledge expansion in conversational robots, enabling more diverse and engaging interactions by automatically extracting and inserting new concepts during dialogue.

Contribution

It introduces a new technique for automatic knowledge extraction and four methods for inserting concepts into a robot's knowledge base during conversation.

Findings

Knowledge extraction accuracy was validated through two experiments.

Insertion methods effectively expanded the robot's knowledge base.

Enhanced knowledge base increased conversation diversity and engagement.

Abstract

This article describes a novel approach to expand in run-time the knowledge base of an Artificial Conversational Agent. A technique for automatic knowledge extraction from the user's sentence and four methods to insert the new acquired concepts in the knowledge base have been developed and integrated into a system that has already been tested for knowledge-based conversation between a social humanoid robot and residents of care homes. The run-time addition of new knowledge allows overcoming some limitations that affect most robots and chatbots: the incapability of engaging the user for a long time due to the restricted number of conversation topics. The insertion in the knowledge base of new concepts recognized in the user's sentence is expected to result in a wider range of topics that can be covered during an interaction, making the conversation less repetitive. Two experiments are presented to assess the performance of the knowledge extraction technique, and the efficiency of the developed insertion methods when adding several concepts in the Ontology.