Challenges and Opportunities in Knowledge Representation and Reasoning

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Author(s): Pankaj Dadure (University of Petroleum and Energy Studies, India), Partha Pakray (National Institute of Technology, Silchar, India)and Sivaji Bandyopadhyay (National Institute of Technology, Silchar, India)
Copyright: 2023
Pages: 14
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch148

Keywords: Data Analysis and Statistics / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Knowledge representation is of immense importance in the field of artificial intelligence and natural language processing. The representation of knowledge goes hand in hand with automated reasoning as one of the key goals of representing knowledge effectively is being able to reason about it. Researchers of knowledge representation and reasoning have built techniques and methods that are the main source of development in computer science and have made tremendous progress in a wide variety of real-life applications, ranging from natural language processing to robotics and software engineering. Further research is required in order to allow a more active role in guiding the reasoning process through the knowledge representation framework. This article has discussed knowledge representation and reasoning and analyzed the major challenges and new opportunities where novel knowledge representation and reasoning research have had a major impact.

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