Knowledge Graph-Based Core Concept Identification in Learning Resources

Automatic Construction of Educational Knowledge Graphs: A Word Embedding-Based Approach

“…These CKGs are also being widely used to improve learning outcomes, solve problems in traditional teaching, and enhance the possibility of effective learning [23]. Moreover, CKGs are being used as the basis for an intelligent question-answering system for high school courses [24] and course recommendations based on student information [25].…”

“…These relationships are mainly sequential or semantic relationships and are found using DBpedia spotlight [25], rule processing, KNN [26], probabilistic association rule mining [3], binary classification on models trained with labeled datasets [34], binary classification on trained models using feedforward neural networks [35], prompt-tuning with synergistic optimization [36], cosine similarity [27,29], PMI, normalized google distance [31], semantic role labeling [37], prerequisite relation calculations based on the preliminary knowledge tags of the courses [20], and rule-based relation extraction [38]. Another widely adopted task in the construction of EduKGs is entity linking with an external knowledge base, mainly Wikipedia [25,27,28,32,33,39]. This is achieved using Wikipedia API or different entity-linking services, such as DBpedia Spotlight [25,39] and Babelfy [39].…”

“…Another widely adopted task in the construction of EduKGs is entity linking with an external knowledge base, mainly Wikipedia [25,27,28,32,33,39]. This is achieved using Wikipedia API or different entity-linking services, such as DBpedia Spotlight [25,39] and Babelfy [39]. Some of the research studies have also focused on concept expansion for the semantic enrichment with new related concepts, for example, using Wikipedia categories [25,33,39] or the Baidu Encyclopedia [31].…”

“…This is achieved using Wikipedia API or different entity-linking services, such as DBpedia Spotlight [25,39] and Babelfy [39]. Some of the research studies have also focused on concept expansion for the semantic enrichment with new related concepts, for example, using Wikipedia categories [25,33,39] or the Baidu Encyclopedia [31]. Furthermore, some of the studies have also incorporated conceptweighting strategies to rank the identified concepts based on their weights.…”

“…Furthermore, some of the studies have also incorporated conceptweighting strategies to rank the identified concepts based on their weights. The weighting strategy used in most of the studies is term frequency-inverse document frequency (TF-IDF) [25,26,37,39].…”