Artificial Intelligence, Machine Learning and User Interface Design

Knowledge Representation in Artificial Intelligence - A Practical Approach

Author(s): Vandana C. Bagal*, Archana L. Rane, Debam Bhattacharya, Abhijeet Banubakode and Vishwanath S. Mahalle

Pp: 210-222 (13)

DOI: 10.2174/9789815179606124010013

* (Excluding Mailing and Handling)


In the realm of artificial intelligence, knowledge representation is a vital aspect that enables effective information sharing and processing. Humans excel at sharing trusted information, which is acquired through rigorous testing and validation, resulting in what we commonly refer to as knowledge. The representation of knowledge can take various forms, such as graphs, maps, or textual formats. With the continuous evolution of the IT sector, the introduction of AI has simplified many tasks, often surpassing human capabilities and effortlessly handling even the most basic activities. However, understanding the concept of knowledge representation remains a fundamental question. In this research paper, we delve into the basics of knowledge representation to directly address this question. The understanding of knowledge representation is best achieved by examining the role knowledge plays in specific case studies or systems, which includes scientific reasoning and comprehension of the world. By exploring the intricacies of knowledge representation, we aim to provide a practical approach to its implementation in the field of artificial intelligence. 

Keywords: AI capabilities, Artificial intelligence, Comprehension, Fundamental question, Graphs, IT sector, Information sharing, Knowledge representation, Maps, Practical approach, Scientific reasoning, Testing, Trusted information, Textual formats, Understanding, Validation.

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