In this paper, the problem of semantic information extraction for resource
constrained text data transmission is studied. In the considered model, a
sequence of text data need to be transmitted within a communication
resource-constrained network, which only allows limited data transmission.
Thus, at the transmitter, the original text data is extracted with natural
language processing techniques. Then, the extracted semantic information is
captured in a knowledge graph. An additional probability dimension is
introduced in this graph to capture the importance of each information. This
semantic information extraction problem is posed as an optimization framework
whose goal is to extract most important semantic information for transmission.
To find an optimal solution for this problem, a Floyd's algorithm based
solution coupled with an efficient sorting mechanism is proposed. Numerical
results testify the effectiveness of the proposed algorithm with regards to two
novel performance metrics including semantic uncertainty and semantic
similarity.

本文研究了资源受限文本数据传输的语义信息提取问题。在所考虑的模型中，需要在通信资源受限的网络中传输文本数据序列，该网络仅允许有限的数据传输。因此，在发送端，使用自然语言处理技术提取原始文本数据，然后将提取的语义信息捕捉到知识图中。该图引入了一个额外的概率维度，以捕捉每个信息的重要性。这个语义信息提取问题被提出为一个优化框架，其目标是提取最重要的语义信息进行传输。为了找到这个问题的最优解，提出了一种基于 Floyd 算法的解决方案，结合了高效的排序机制。数值结果证明了所提算法在语义不确定性和语义相似性等两个新颖性能度量方面的有效性。