An Explainable Artificial-Intelligence-Based CNN Model for Knowledge Extraction From the Social Internet of Things: Proposing a New Model

Abstract

Rich material is buried in the entity’s textual description information, its hierarchical-type information, and the graph’s topological structure information in the knowledge graph. As a result, these data can be a useful supplement to triple information in terms of improving performance. To appropriately exploit these social Internet of Things (IoT) data, entity details are first encoded using artificial-intelligence (AI)-based convolutional neural networks (CNNs). The unit vector and unit description vector are then projected into a given relational space using the hierarchical-type information, thus restricting its semantic content. The graph attention approach is then used to fuse the graph’s topological structure information to calculate the influence of various neighboring points on the entity. To deal with the data-sparse problem, the multihop relationship information among entities is calculated at the same time. Finally, a decoder is used to collect global information among dimensions. Link prediction experiments show that the multisource information combined knowledge representation learning (XAI-CNN) model based on explainable AI (XAI) can effectively use multisource social IoT information beyond triples and that other techniques may be better than the baseline model.