The word "knowledge representation" is spelled as /ˈnɒlɪdʒ/ /ˌrɛprɪzɛnˈteɪʃən/. The first part of the word, "knowledge," is pronounced as /ˈnɒlɪdʒ/, with an "o" sound followed by an "l" and a "d" sound. The second part of the word, "representation," is pronounced as /ˌrɛprɪzɛnˈteɪʃən/, with emphasis on the "p" and "t" sounds. The word refers to the way information is organized and presented in a form that can be easily understood and communicated.
Knowledge representation refers to the process of encoding and organizing information in a way that can be understood and utilized by computer systems or human beings. It involves the transformation of knowledge into a formalized structure that can be manipulated, stored, and retrieved for various purposes such as reasoning, inference, or decision-making.
In the context of artificial intelligence, knowledge representation is crucial as it enables machines to understand and reason about the world. It involves the selection of appropriate symbols or languages to represent various aspects of knowledge, including facts, concepts, relationships, rules, and constraints. These representations can take different forms such as graphs, frames, ontologies, semantic networks, or logical statements.
The key objective of knowledge representation is to create a bridge between the real world and the computational systems, allowing the extraction, organization, and utilization of knowledge for effective problem-solving or information retrieval. This process often involves abstraction and generalization, allowing complex real-world phenomena to be modeled and captured in a structured and formalized manner.
The choice of knowledge representation scheme depends on the specific requirements and constraints of the domain or problem being addressed. Different representations may offer varying levels of expressiveness, computational efficiency, or ease of manipulation. Hence, the selection of an appropriate representation is a critical decision when developing intelligent systems.
Overall, knowledge representation plays a fundamental role in the creation of intelligent systems by providing a means to encapsulate and manipulate knowledge, enabling machines to reason, learn, and interact with humans in a meaningful manner.
The term "knowledge representation" is composed of two words: "knowledge" and "representation", each with its own etymology.
1. Knowledge:
The word "knowledge" has its roots in the Old English word "cnawan", which means "to know" or "to recognize". It has Germanic origins and is related to the Middle Low German word "knowen" and Old Norse word "kenna". The term has evolved over time but has consistently referred to the understanding or awareness of information.
2. Representation:
The word "representation" comes from the Latin word "repraesentare", which consists of the prefix "re-" (meaning "again" or "back") and the verb "praesentare" (meaning "to present" or "to show"). In Latin, it was used to denote the act of showing or presenting something again.