  With the development of Artificial Intelligence (AI) and Internet of Things
(IoT) technologies, network communications based on the Shannon-Nyquist theorem
gradually reveal their limitations due to the neglect of semantic information
in the transmitted content. Semantic communication (SemCom) provides a solution
for extracting information meanings from the transmitted content. The semantic
information can be successfully interpreted by a receiver with the help of a
shared knowledge base (KB). This paper proposes a two-stage hierarchical
qualification and validation model for natural language-based
machine-to-machine (M2M) SemCom. The approach can be applied in various
applications, such as autonomous driving and edge computing. In the proposed
model, we quantitatively measure the degree of understanding (DoU) between two
communication parties at the word and sentence levels. The DoU is validated and
ensured at each level before moving to the next step. The model's effectiveness
is verified through a series of experiments, and the results show that the
quantification and validation method proposed in this paper can significantly
improve the DoU of inter-machine SemCom.
