Semantic Information Extraction for Text Data with Probability Graph
Zhouxiang Zhao Zhaohui Yang Ye Hu Licheng Lin Zhaoyang Zhang
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.