  In this paper, we present a probability graph-based semantic information
compression system for scenarios where the base station (BS) and the user share
common background knowledge. We employ probability graphs to represent the
shared knowledge between the communicating parties. During the transmission of
specific text data, the BS first extracts semantic information from the text,
which is represented by a knowledge graph. Subsequently, the BS omits certain
relational information based on the shared probability graph to reduce the data
size. Upon receiving the compressed semantic data, the user can automatically
restore missing information using the shared probability graph and predefined
rules. This approach brings additional computational resource consumption while
effectively reducing communication resource consumption. Considering the
limitations of wireless resources, we address the problem of joint
communication and computation resource allocation design, aiming at minimizing
the total communication and computation energy consumption of the network while
adhering to latency, transmit power, and semantic constraints. Simulation
results demonstrate the effectiveness of the proposed system.
