  One of the most fundamental problems in science is to define {\it
quantitatively} the complexity of organized matters, i.e., {\it organized
complexity}. Although many measures have been proposed toward this aim in
previous decades, there is no agreed upon definition. This paper presents a new
quantitative definition of organized complexity. This definition {\it
simultaneously} captures the three major features of complexity: computational
(similar to logical depth), descriptional (similar to the Kolmogorov complexity
and effective complexity) and distributional (similar to statistical
complexity). In addition, the proposed definition is computable and can measure
both probabilistic and deterministic forms of objects in a unified manner. The
proposed definition is based on circuits rather than Turing machines and
$\epsilon$-machines. We give several criteria required for organized complexity
measures and show that the proposed definition satisfies all of them for the
first time. We then apply this quantitative definition to formulate a {\it
semantic information theory}. We present the first formal definition of a {\it
semantic information amount}, which is the core concept of the semantic
information theory, that is based only on concretely defined notions. Previous
semantic information theories defined this amount under some a priori
information which is not concretely specified. We then unveil several
fundamental properties in the semantic information theory, e.g., a semantic
source coding theorem, semantic channel coding theorem, and effectiveness
coding theorem. Although the semantic information theory has a long history of
research going back more than six decades, there has been no study on its
relation to organized complexity. This paper offers the first unified paradigm
of organized complexity and semantic information theory.
