A Hierarchical Information Compression Approach for Knowledge Discovery From Social Multimedia

Knowledge discovery is an ongoing research endeavor aimed at uncovering valuable insights and patterns from large volumes of data in massive social systems (MSSs). Although recent advances in deep learning have made significant progress in knowledge discovery, the “data dimensionality reduction” problem still poses practical challenges. To address this, we have introduced a hierarchical information compression (IC) approach, which emphasizes the elimination of redundant and irrelevant features and the generation of high-quality knowledge representation, aiming to enhance the information density of the knowledge discovery process. Our approach consists of coarse-grained and fine-grained stages for data compression. In the coarse-grained stage, our method employs the key feature distiller based on the Siamese network to effectively identify a substantial number of irrelevant features and latent redundancies within coarse-grained data blocks. Moving on to the fine-grained stage, our model further compresses the internal features of the data, extracting the most crucial knowledge and facilitating data compression by cross-block learning. By implementing these two stages, the approach achieves both inter and innerblock IC while preserving essential knowledge. To validate the performance of our proposed model, we conducted several experiments using WikiSum, a large knowledge corpus based on English Wikipedia in MSSs. The experimental results demonstrate that our model achieved a 2.38% increase on recall-oriented understudy for gisting evaluation (ROUGE)-2 and an improvement of over 7% on the informativeness and conciseness metrics, as evidenced by the improved scores obtained from both automatic and human evaluations. The experimental results prove that our model can effectively select the most pertinent and meaningful content and reduce the redundancy to generate better knowledge representation.

I. Introduction

In the era of digital communication, social systems have undergone a dramatic transformation, becoming massive repositories of textual and interactive data [1]. These data-rich environments encapsulate the intricacies of human interactions, opinions, and knowledge exchanges, rendering social networks as pivotal hubs for understanding the deep relation among massive social systems (MSSs) [2]. Thus, knowledge discovery has garnered significant attention due to the plethora of social knowledge on MSS that, when analyzed, can reveal patterns and insights into societal trends previously unknown or hidden [3]. The ultimate goal of knowledge discovery is to uncover meaningful information from a large data corpus and extract valuable knowledge that can be used for decision-making, problem-solving, and gaining a deeper understanding of MSS. Knowledge discovery technologies find applications in various domains, such as generating knowledge representation from Wikipedia, articles, meeting dialogues, and coding activities [4], [5].