Deep Learning-Based Reasoning With Multi-Ontology for IoT Applications

Abstract

In the era of mobile big data, data driven intelligent Internet of Things (IoT) applications are becoming widespread, and knowledge-based reasoning is one of the essential tasks of these applications. While most knowledge-based reasoning work is conducted with knowledge graph, ontology-based reasoning method can inherently achieve higher level intelligence by leveraging both explicit and tacit knowledge in specific domains, and its performance is determined by precise refinement of the inference rules. However, most ontology-based reasoning work concentrates on semantic reasoning in a single ontology, and fail to utilize association of multiple ontologies in various domains to extend reasoning capacity. This is even the case for the IoT applications where knowledge from multiple domains needs to be utilized. To overcome this issue, we propose a deep learning-based method to associate multiple ontology rule bases, thereby discover new inference rules. In our method, we first use a regression tree model to determine the threshold value for parameters in inference rules that constitute the ontology rule base, avoiding the influence of uncertainty factors on knowledge reasoning results. Then, a two-way GRU (Gated Recurrent Unit) neural network with attention mechanism is used to discover semantic relations among the rule bases of ontologies. Therefore, the association of multiple ontology rule bases is realized, and the rule base of knowledge reasoning is expanded by acquiring some unspecified rules. To the best our knowledge, this work is the first one to leverage deep learning in reasoning with multiple ontologies. In order to verify the effectiveness of our method, we apply it in a real traffic safety monitoring application by relating rule bases of a vehicle ontology and a traffic management ontology, and achieve effective knowledge reasoning.