Semantic Information Extraction for Text Data with Probability Graph

TL;DR

This paper presents a method for extracting and transmitting the most important semantic information from text data within resource-limited networks using a probability-augmented knowledge graph and an optimized algorithm.

Contribution

It introduces a novel semantic information extraction framework with a probability dimension and an efficient Floyd's algorithm-based solution for resource-constrained text transmission.

Findings

The proposed method effectively reduces semantic uncertainty.

It improves semantic similarity in transmitted data.

Numerical results validate the efficiency of the algorithm.

Abstract

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.