Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction
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 t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9584853/ |
| _version_ | 1818730094736179200 |
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| author | Liliana Villamar Gomez Jun Miura |
| author_facet | Liliana Villamar Gomez Jun Miura |
| author_sort | Liliana Villamar Gomez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T22:56:19Z |
| format | Article |
| id | doaj.art-9e25f4ae8f5a4129a8dcc8ef8a6455ae |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T22:56:19Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9e25f4ae8f5a4129a8dcc8ef8a6455ae2022-12-21T21:29:33ZengIEEEIEEE Access2169-35362021-01-01914602314603710.1109/ACCESS.2021.31222959584853Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User InteractionLiliana Villamar Gomez0https://orcid.org/0000-0003-1907-4686Jun Miura1https://orcid.org/0000-0003-0153-2570Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, JapanDepartment of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, JapanThe 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.https://ieeexplore.ieee.org/document/9584853/Concept learningontology learningrobot learninghuman–robot interaction |
| spellingShingle | Liliana Villamar Gomez Jun Miura Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction IEEE Access Concept learning ontology learning robot learning human–robot interaction |
| title | Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction |
| title_full | Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction |
| title_fullStr | Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction |
| title_full_unstemmed | Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction |
| title_short | Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction |
| title_sort | ontology learning of new concepts combining textural knowledge visual analysis and user interaction |
| topic | Concept learning ontology learning robot learning human–robot interaction |
| url | https://ieeexplore.ieee.org/document/9584853/ |
| work_keys_str_mv | AT lilianavillamargomez ontologylearningofnewconceptscombiningtexturalknowledgevisualanalysisanduserinteraction AT junmiura ontologylearningofnewconceptscombiningtexturalknowledgevisualanalysisanduserinteraction |