  Shannon information theory is established based on probability and bits, and
the communication technology based on this theory realizes the information age.
The original goal of Shannon's information theory is to describe and transmit
information content. However, due to information is related to cognition, and
cognition is considered to be subjective, Shannon information theory is to
describe and transmit information-bearing signals. With the development of the
information age to the intelligent age, the traditional signal-oriented
processing needs to be upgraded to content-oriented processing. For example,
chat generative pre-trained transformer (ChatGPT) has initially realized the
content processing capability based on massive data. For many years,
researchers have been searching for the answer to what the information content
in the signal is, because only when the information content is mathematically
and accurately described can information-based machines be truly intelligent.
This paper starts from rethinking the essence of the basic concepts of the
information, such as semantics, meaning, information and knowledge, presents
the mathematical characterization of the information content, investigate the
relationship between them, studies the transformation from Shannon's signal
information theory to semantic information theory, and therefore proposes a
content-oriented semantic communication framework. Furthermore, we propose
semantic decomposition and composition scheme to achieve conversion between
complex and simple semantics. Finally, we verify the proposed characterization
of information-related concepts by implementing evolvable knowledge-based
semantic recognition.
