Paper Info
Title
Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction
Abstract
The continuous emergence of new technologies has contributed to the impending reality of service robots an upcoming reality. When interacting with humans, robots must adapt to changing environments. Hence, service robots at home need learning capabilities to acquire new knowledge and merge it with their own. In this study, we have developed a system for learning the ontologies of new concepts, combining textural knowledge, visual analysis, and user interaction. In this system, the robot is provided with an essential feature to adapt to the home environment. We focus on the learning of new ontological concepts oriented toward service robot applications. We propose combining textural knowledge, visual analysis, and user interaction to determine the correct placement of the new concepts in the ontology structure. We aim to enable the robot to extend its ontological knowledge as needed. We conducted a set of experiments to show the applicability of the presented method and the advantage of conceptualizing objects in ontological knowledge. The experiments consisted of two parts: concept learning experiments and experiments with an integrated robot system. In the former, the robot had to conceptualize a set of new objects in its ontological knowledge, and in the latter, the robot was asked to search and find the new objects learned.
Year
DOI
Venue
2021
10.1109/ACCESS.2021.3122295
IEEE ACCESS
Keywords
DocType
Volume
Robots, Ontologies, Visualization, Semantics, Service robots, Robot sensing systems, Tools, Concept learning, ontology learning, robot learning, human-robot interaction
Journal
9
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Liliana Villamar Gomez100.34
Jun Miura201.69