An Improved Knowledge Graph Question Answering System for English Teaching

Abstract

Knowledge graph, as a structured semantic knowledge base, has become an essential foundation for artificial intelligence applications with its flexible composition structure and rich semantic representation capability. This paper combines the knowledge graph embedding scoring algorithm with the link scoring algorithm to effectively solve the problem of missing answers in the current knowledge graph embedding question and answer method. This method constructs a query link while searching for the best answer and gives the answer set through the query, which effectively alleviates the omission of answers in the existing methods. The experimental results show that the F1 score of the English teaching test system on the data set is 86.85%, where the answer selection method weakly relies on a priori information such as predicates in the test data and can be trained on a test pair data set without human intervention, with good generalization performance.

Fused Attentive Generative Adversarial Network based Personalized English Learning Material Recommendation System utilizing Knowledge Graph

“…The majority of the knowledge content on existing online learning platforms is divided based on students' grades. This has led to problems like inadequate learning materials for good students, average and poor students never learn [11].Thus, given these two concerns, it is important to carefully analyse how to achieve personalised learning resource recommendations depend on unique necessities of learners and features of their English knowledge [12]. In reference to the first issue raised above, examined features of English as linguistic instrument, knowledge features using an English course of particular grade as example.…”