| dc.description.abstract | There are a variety of problems in the way that adaptive learning (personalized learning platform) is presented information at the moment, Sql language is a field of knowledge that includes several topics and involves a wide variety of subtopics; nevertheless, the learning path that it will take is not entirely clear. in contrast to other subjects of technology like network and web development. Creating a knowledge graph (KG) in SQL education is the first step in addressing the issues and improving academic learning efficiency.
The development of Machine learning and Natural Language Processing (NLP) technology has made it possible to extract concepts from the unstructured text by using various learning approaches. This was previously impossible.
An NLP-based KG has been established that considers concept synthetics and similarity relations. We can analyze our data and improve the quality of knowledge learning by detecting links and decreasing the search scope for SQL-related concepts, as well as recommending relevant topics, using NLP and machine learning (supervised and unsupervised) by using pos tagging, word embedding, and clustering algorithms.
Several training models are compared as well as their performance on different similarly tasks. As a result, the best performance of trained word vectors has been achieved. This is achieved by using the Word2Vec (Skip-Gram) model. | |