  In this paper, a semantic communication framework is proposed for textual
data transmission. In the studied model, a base station (BS) extracts the
semantic information from textual data, and transmits it to each user. The
semantic information is modeled by a knowledge graph (KG) that consists of a
set of semantic triples. After receiving the semantic information, each user
recovers the original text using a graph-to-text generation model. To measure
the performance of the considered semantic communication framework, a metric of
semantic similarity (MSS) that jointly captures the semantic accuracy and
completeness of the recovered text is proposed. Due to wireless resource
limitations, the BS may not be able to transmit the entire semantic information
to each user and satisfy the transmission delay constraint. Hence, the BS must
select an appropriate resource block for each user as well as determine and
transmit part of the semantic information to the users. As such, we formulate
an optimization problem whose goal is to maximize the total MSS by jointly
optimizing the resource allocation policy and determining the partial semantic
information to be transmitted. To solve this problem, a
proximal-policy-optimization-based reinforcement learning (RL) algorithm
integrated with an attention network is proposed. The proposed algorithm can
evaluate the importance of each triple in the semantic information using an
attention network and then, build a relationship between the importance
distribution of the triples in the semantic information and the total MSS.
Compared to traditional RL algorithms, the proposed algorithm can dynamically
adjust its learning rate thus ensuring convergence to a locally optimal
solution.
