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In this paper, we present a probability graph-based semantic informationcompression system for scenarios where the base station (BS) and the user sharecommon background knowledge. We employ probability graphs to represent theshared knowledge between the communicating parties. During the transmission ofspecific text data, the BS first extracts semantic information from the text,which is represented by a knowledge graph. Subsequently, the BS omits certainrelational information based on the shared probability graph to reduce the datasize. Upon receiving the compressed semantic data, the user can automaticallyrestore missing information using the shared probability graph and predefinedrules. This approach brings additional computational resource consumption whileeffectively reducing communication resource consumption. Considering thelimitations of wireless resources, we address the problem of jointcommunication and computation resource allocation design, aiming at minimizingthe total communication and computation energy consumption of the network whileadhering to latency, transmit power, and semantic constraints. Simulationresults demonstrate the effectiveness of the proposed system.

Semantic Communication with Probability Graph: A Joint Communication and Computation Design