  In this paper, we propose an advancement to Tarskian model-theoretic
semantics, leading to a unified quantitative theory of semantic information and
communication. We start with description of inductive logic and probabilities,
which serve as notable tools in development of the proposed theory. Then, we
identify two disparate kinds of uncertainty in semantic communication, that of
physical and content, present refined interpretations of semantic information
measures, and conclude with proposing a new measure for semantic
content-information and entropy. Our proposition standardizes semantic
information across different universes and systems, hence bringing
measurability and comparability into semantic communication. We then proceed
with introducing conditional and mutual semantic cont-information measures and
point out to their utility in formulating practical and optimizable lossless
and lossy semantic compression objectives. Finally, we experimentally
demonstrate the value of our theoretical propositions.
