  Classical medium access control (MAC) protocols are interpretable, yet their
task-agnostic control signaling messages (CMs) are ill-suited for emerging
mission-critical applications. By contrast, neural network (NN) based protocol
models (NPMs) learn to generate task-specific CMs, but their rationale and
impact lack interpretability. To fill this void, in this article we propose,
for the first time, a semantic protocol model (SPM) constructed by transforming
an NPM into an interpretable symbolic graph written in the probabilistic logic
programming language (ProbLog). This transformation is viable by extracting and
merging common CMs and their connections while treating the NPM as a CM
generator. By extensive simulations, we corroborate that the SPM tightly
approximates its original NPM while occupying only 0.02% memory. By leveraging
its interpretability and memory-efficiency, we demonstrate several SPM-enabled
applications such as SPM reconfiguration for collision-avoidance, as well as
comparing different SPMs via semantic entropy calculation and storing multiple
SPMs to cope with non-stationary environments.
