Real-World Knowledge Representation and Reasoning

WSN Lifetime Management with the Predictive Energy Management Mechanism for the Autonomous Cooperative Smart Logistics System - A Real World Knowledge Representation Scenario

Lecture Notes in Business Information Processing - Knowledge Management in Organizations ◽

10.1007/978-3-319-21009-4_40 ◽

2015 ◽

pp. 528-549

Author(s):

Muthuraman Thangaraj ◽

S. Anuradha

Keyword(s):

Knowledge Representation ◽

Energy Management ◽

Real World ◽

World Knowledge ◽

Management Mechanism ◽

Logistics System ◽

Smart Logistics ◽

System A ◽

Wsn Lifetime

Download Full-text

Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications

Applied Sciences ◽

10.3390/app11104324 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4324

Author(s):

Sumaira Manzoor ◽

Yuri Goncalves Rocha ◽

Sung-Hyeon Joo ◽

Sang-Hyeon Bae ◽

Eun-Jin Kim ◽

...

Keyword(s):

Knowledge Representation ◽

Real World ◽

Autonomous Robots ◽

Semantic Knowledge ◽

Lessons Learned ◽

Knowledge Representation And Reasoning ◽

Knowledge Based Systems ◽

Knowledge Based ◽

Representation Systems ◽

Knowledge Representation Systems

Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.

Download Full-text