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

arXiv: https://arxiv.org/abs/2104.11170 · HTML: https://mlsanity.com/paper/2104.11170/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [A Review of Verbal and Non-Verbal Human-Robot Interactive Communication](https://mlsanity.com/paper/1401.4994/) (2014)
- [Semi-Automatic Terminology Ontology Learning Based on Topic Modeling](https://mlsanity.com/paper/1709.01991/) (2017)
- [Paving the Way for Culturally Competent Robots: a Position Paper](https://mlsanity.com/paper/1803.08812/) (2018)
- [VOnDA: A Framework for Ontology-Based Dialogue Management](https://mlsanity.com/paper/1910.00340/) (2019)

## Built on by (most cited first, 1 of 1)

- [Voice CMS: updating the knowledge base of a digital assistant through conversation](https://mlsanity.com/paper/2505.22303/) (2025)

## Abstract (arXiv)

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.