  The recent emergence of 6G raises the challenge of increasing the
transmission data rate even further in order to break the barrier set by the
Shannon limit. Traditional communication methods fall short of the 6G goals,
paving the way for Semantic Communication (SemCom) systems. These systems find
applications in wide range of fields such as economics, metaverse, autonomous
transportation systems, healthcare, smart factories, etc. In SemCom systems,
only the relevant information from the data, known as semantic data, is
extracted to eliminate unwanted overheads in the raw data and then transmitted
after encoding. In this paper, we first use the shared knowledge base to
extract the keywords from the dataset. Then, we design an auto-encoder and
auto-decoder that only transmit these keywords and, respectively, recover the
data using the received keywords and the shared knowledge. We show analytically
that the overall semantic distortion function has an upper bound, which is
shown in the literature to converge. We numerically compute the accuracy of the
reconstructed sentences at the receiver. Using simulations, we show that the
proposed methods outperform a state-of-the-art method in terms of the average
number of words per sentence.
