In the dynamic landscape of modern education, the search for improved pedagogical methods, enriched learning experiences, and empowered educators remains a perpetual pursuit. In recent years, a remarkable technological innovation has asserted its dominance in education: Knowledge Graphs (KGs). These structured representations of knowledge are increasingly proving to be indispensable tools, fostering advancements driven by the growing recognition of their essential role in enriching personalised learning, curriculum design, concept mapping, and educational content recommendation systems. In this paper, a systematic literature review (SLR) has been conducted to comprehensively examine KG construction methodologies and their applications across five key domains in education. In each examined study, we highlight the specific KG functionalities, knowledge extraction techniques, knowledge base characteristics, resource requirements, evaluation criteria, and limitations. This paper distinguishes itself by offering a broad overview of KGs in education, analyzing state-of-the-art methodologies, and identifying research gaps and limitations, paving the way for future advancements. 

A systematic literature review of knowledge graph construction and application in education

E-learning is an online educational system that uses electronic and technology tools and the internet. To facilitate the acquisition of knowledge, students can consult materials and interact directly with lecturers via e-learning educational systems. The method for developing an intelligent querying system for e-learning is suggested in this research. The knowledge base of the system is structured using the knowledge model combining an ontology and the two-layer knowledge graph, called Rela-KG model. The searching problems for knowledge content are studied and solved. The technique is applied to create an intelligent querying system on the course of Database Foundation in Information Technology (IT) at the university. This technique attempts to assist students in reviewing lectures and better comprehending course material through independent study. The experimental results indicate that it would be anticipated to support student access to online learning resources.

pdf/knowledge-management-for-information-querying-system-in-3kybq3e9.pdf

Knowledge Management for Information Querying System in Education via the Combination of Rela-Ops Model and Knowledge Graph

This paper presents a comprehensive survey of knowledge graphs in education. It covers the patterns and prospects of research in this area. A total of 48 relevant publications between 2011 and 2023 were collected from the Web of Science, Scopus, and ProQuest for review. The findings reveal a sharp increase in recent years in the body of research into educational knowledge graphs which was mainly conducted from institutions in China. Most of the relevant research work adopted a quantitative method, such as performance evaluation, user surveys, and controlled experiments, to assess the effectiveness of knowledge graph approaches. The findings also suggest that knowledge graph approaches were primarily researched and implemented in higher education institutions, with a focus on computer science, mathematics, and engineering. The most frequently addressed objectives included enhancing knowledge representation and providing personal learning recommendations, and the most common applications were concept instruction and educational recommendations. Diverse data resources, such as course materials, student learning behaviours, and online encyclopaedia, were processed to implement knowledge graph approaches in different scenarios. Relevant technical means employed for the implementation of knowledge graphs dealt with the purposes of building knowledge ontology, achieving recommendations, and creating knowledge graphs. Various pedagogies such as personalised learning and collaborative learning are supported by the knowledge graph approaches. The findings also identified key limitations in the relevant work, including insufficient information for knowledge graph construction, difficulty in extending applications across subject areas, the restricted scale and scope of data resources, and the lack of comprehensive user feedback and evaluation processes. 

A Survey of Knowledge Graph Approaches and Applications in Education

Today, the legal document system is increasingly strict with different levels of influence and affects activities in many different fields. The increasing number of legal documents interwoven with each other also leads to difficulties in searching and applying in practice. The construction of knowledge maps that involve one or a group of legal documents is an effective approach to represent actual knowledge domains. A legal knowledge graph constructed from laws and legal documents can enable a number of applications, such as question answering, document similarity, and search. In this paper, we describe the process of building a system of knowledge maps for the Vietnamese legal system from the source of about 325,000 legal documents that span all fields of social life. This study also proposes an integrated ontology to represent the legal knowledge from legal documents. This model integrates the ontology of relational knowledge and the graph of key phrases and entities in the form of a concept graph. It can express the semantics of the content of a given legal document. In addition, this study also describes the process of building and exploiting natural language processing tools to build a VLegalKMaps system, which is a repository of Vietnamese legal knowledge maps. We also highlight open challenges in the realization of knowledge graphs in a technical legal system that enables this approach at scale. 

Building Legal Knowledge Map Repository with NLP Toolkits

With rising environmental concerns, accurate air quality predictions have become paramount as they help in planning preventive measures and policies for potential health hazards and environmental problems caused by poor air quality. Most of the time, air quality data are time series data. However, due to various reasons, we often encounter missing values in datasets collected during data preparation and aggregation steps. The inability to analyze and handle missing data will significantly hinder the data analysis process. To address this issue, this paper offers an extensive review of air quality prediction and missing data imputation techniques for time series, particularly in relation to environmental challenges. In addition, we empirically assess eight imputation methods, including mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer, to scrutinize their impact on air quality data. The evaluation is conducted using diverse air quality datasets gathered from numerous cities globally. Based on these evaluations, we offer practical recommendations for practitioners dealing with missing data in time series scenarios for environmental data. 

The impact of data imputation on air quality prediction problem

The legal search has great demand and a role in society. Ontology is a useful solution to represent the legal domain. This paper introduces a method to extract knowledge from law documents for building a legal knowledge base as the Legal-Onto Ontology. The method also proposes a solution to extract concepts and their relationships to create a knowledge graph. This is the foundation for solving the semantic query problem and returning the correct answer to meet the user’s needs. The experimental results show that it is possible to build a law lookup application, which meets the practical requirements of users. 

Information Retrieval from Legal Documents with Ontology and Graph Embeddings Approach

Distance learning can be facilitated through e-learning, and students can enhance their understanding of course materials through the use of an intelligent querying system that enables self-learning. In this paper, an ontology-based method for organizing the knowledge of mathematics in high-school is presented. Ontology Rela-Ops model, which is a knowledge model for representing the knowledge of relations and operators, is combined with the knowledge component of functions. Based on the combining model, a structure of knowledge graph is proposed to represent relations of intellectual contents. An intelligent querying system is designed for the domain of high-school mathematics using these structures. This searching system can query contents of the course and extracts other knowledge related to querying results. The experimental results show that the proposed method is useful and effective to apply in the practice. 

Design an ontology-based model for intelligent querying system in mathematics education

E-learning is an online educational system using technological and electronic devices through the internet. Using e-learning educational systems, learners can refer to documents and communicate directly with teachers to effectuate the acquisition of knowledge. In this paper, the method for creating an intelligent querying system for e-learning is proposed. The knowledge base of this system is organized based on the integrating of ontology and the structure of database. Some searching issues for knowledge content, such as studying and resolving knowledge searches based on classification of knowledge, are studied and solved. This method is applied to build an intelligent querying system on the course of Database Foundation in Information Technology (IT) curriculum at university. This system aims to support students to review lessons and understand more the knowledge of courses by their self-learning. The experimental results show that it would be expected to contribute in supporting online-based learning facilities for students.

Construct an Intelligent Querying System in Education based on Ontology Integration

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Information Retrieval from Legal Documents with Ontology and Graph Embeddings Approach

Knowledge Management for Information Querying System in Education via the Combination of Rela-Ops Model and Knowledge Graph

Design an ontology-based model for intelligent querying system in mathematics education

Construct an Intelligent Querying System in Education based on Ontology Integration

Support Vector Machines for Control of Multimodal Processes