A vision sensing-enhanced knowledge graph inference method for a healthy operation index in higher education

Abstract

We adopted the method of knowledge mapping to conduct in-depth visualization to propose the construction method of knowledge mapping-based inference of a healthy operation index in higher education (HOI-HE). For the first part, an improved named entity identification and relationship extraction method is developed, incorporating a vision sensing pre-training algorithm named BERT. For the second part, a multi-decision model-based knowledge graph is used to infer the HOI-HE score by using a multi-classifier ensemble learning approach. The combination of two parts constitutes a vision sensing-enhanced knowledge graph method. The functional modules of knowledge extraction, relational reasoning and triadic quality evaluation are integrated to provide the digital evaluation platform for the HOI-HE value. The vision sensing-enhanced knowledge inference method for the HOI-HE is able to exceed the benefit of pure data-driven methods. The experimental results in some simulated scenes show that the proposed knowledge inference method can work well in the evaluation of a HOI-HE, as well as to discover some latent risk.

Keywords

• knowledge graph
• vision sensing
• healthy operation index
• data visualization
• education management