Automated ontology framework for service robots

2.50
Hdl Handle:
http://hdl.handle.net/10547/622035
Title:
Automated ontology framework for service robots
Authors:
Kanjaruek, Saranya; Li, Dayou; Qiu, Renxi; Boonsim, Noppakun
Other Titles:
2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Abstract:
This paper presents an automated ontology framework for service robots. The framework is designed to automatically create an ontology and an instance of concept in dynamic environment. Ontology learning from text is applied to build a concept hierarchy using WordNet which provides a rich semantic processing for physical objects. The Automated Ontology is composed of four modules: Concept Creation, Property Creation, Relationship Creation and Instance of Concept Creation. The automated ontology algorithm was implemented in order to create the concept hierarchy in the Robot Ontology. The Semantic Knowledge Acquisition represents knowledge of physical objects in dynamic environments. In simulation experiments, the list of object names and property names was identified. The result shows the concept hierarchy which represents explicit terms and the semantic knowledge of physical objects for performing everyday manipulation tasks.
Affiliation:
University of Bedfordshire
Citation:
Kanjaruek S., Li D., Qiu R., Boonsim N. (2016) 'Automated ontology framework for service robots', 2015 IEEE-International conference on Robotics and Biometrics - Zhuhai, Institute of Electrical and Electronics Engineers Inc..
Publisher:
Institute of Electrical and Electronics Engineers Inc.
Issue Date:
25-Feb-2016
URI:
http://hdl.handle.net/10547/622035
DOI:
10.1109/ROBIO.2015.7418770
Additional Links:
http://ieeexplore.ieee.org/document/7418770/
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9781467396745
Appears in Collections:
Engineering

Full metadata record

DC FieldValue Language
dc.contributor.authorKanjaruek, Saranyaen
dc.contributor.authorLi, Dayouen
dc.contributor.authorQiu, Renxien
dc.contributor.authorBoonsim, Noppakunen
dc.date.accessioned2017-02-27T11:48:51Z-
dc.date.available2017-02-27T11:48:51Z-
dc.date.issued2016-02-25-
dc.identifier.citationKanjaruek S., Li D., Qiu R., Boonsim N. (2016) 'Automated ontology framework for service robots', 2015 IEEE-International conference on Robotics and Biometrics - Zhuhai, Institute of Electrical and Electronics Engineers Inc..en
dc.identifier.isbn9781467396745-
dc.identifier.doi10.1109/ROBIO.2015.7418770-
dc.identifier.urihttp://hdl.handle.net/10547/622035-
dc.description.abstractThis paper presents an automated ontology framework for service robots. The framework is designed to automatically create an ontology and an instance of concept in dynamic environment. Ontology learning from text is applied to build a concept hierarchy using WordNet which provides a rich semantic processing for physical objects. The Automated Ontology is composed of four modules: Concept Creation, Property Creation, Relationship Creation and Instance of Concept Creation. The automated ontology algorithm was implemented in order to create the concept hierarchy in the Robot Ontology. The Semantic Knowledge Acquisition represents knowledge of physical objects in dynamic environments. In simulation experiments, the list of object names and property names was identified. The result shows the concept hierarchy which represents explicit terms and the semantic knowledge of physical objects for performing everyday manipulation tasks.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7418770/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectontologiesen
dc.subjectrobotsen
dc.subjectsemanticsen
dc.subjectinformation retrievalen
dc.subjectknowledge acquisitionen
dc.subjectobject recognitionen
dc.subjectOWLen
dc.titleAutomated ontology framework for service robotsen
dc.title.alternative2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)en
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
dc.date.updated2017-02-27T11:28:05Z-
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