Application of Knowledge Graph in Intelligent Education

Application of Knowledge Graph in Intelligent Education

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1. Introduction to Knowledge Graph
A knowledge graph is a large-scale semantic network that provides a semantic representation of the real world. Entities are represented as nodes, while the properties of entities and the relationships between them are represented as edges, forming a web-like graph structure. This structured format is recognizable by humans and machine-friendly, facilitating machine understanding. The vast concepts, properties, and relationships among entities in the graph provide rich semantic and relational information, enabling various graph-related operations and applications. Initially proposed by Google, the knowledge graph aimed to enhance search engine capabilities and improve search quality. Due to its unique characteristics, the knowledge graph plays an important role in various aspects of artificial intelligence.

Application of Knowledge Graph in Intelligent Education

2. Construction of Subject Knowledge Graph in Education
The construction of a knowledge graph is typically application-oriented. Before constructing the graph, it is essential to clarify the problems to be solved and the knowledge required to address these issues, and then design the ontology of the knowledge graph to cover this knowledge. First, let’s introduce some common application scenarios and logic of educational knowledge graphs, based on which we can design the ontology of the educational graph and further construct the knowledge graph.
(1) Application Logic of Educational Knowledge Graph
The educational knowledge graph centers on subject knowledge, establishing hierarchical relationships among knowledge points of various subjects, the associations between knowledge points, and the sequential relationships between different knowledge points, forming the subject knowledge graph. Using this graph, the relationships between knowledge points can be visually presented to students, making it clear and naturally helping students build their knowledge systems, reference key knowledge points, discover connections between knowledge points, and assist students in summarizing and eliminating knowledge blind spots.
Once the subject knowledge graph is constructed, it can be linked with teaching resources (textbooks, examination papers, lecture notes, teaching videos, etc.), establishing connections between knowledge points and users based on user information and learning records. Through the knowledge graph, a more precise depiction of students’ knowledge mastery can be achieved, along with more accurate resource characterization. Thus, enabling precise learning situation analysis, learning path planning, and personalized learning resource recommendations.
It can also help teachers better understand students’ learning situations, optimize teaching methods, and adjust teaching strategies. By associating with teaching research materials, proactive recommendations can enhance the efficiency and quality of teachers’ lesson preparation. An auxiliary teaching question-and-answer system centered on knowledge graph Q&A technology can effectively reduce the burden of repetitive questions for teachers and significantly meet students’ inquiry needs.
(2) Educational Knowledge Resource Construction Centered on Subject Knowledge Graph
The construction of educational knowledge resources centered on knowledge graphs establishes associations between domain knowledge, linking knowledge points with various educational resources such as different versions of textbooks, teaching aids, lecture notes, videos, and examination questions, forming a comprehensive network. These association networks support upper-level applications.

Application of Knowledge Graph in Intelligent Education

In the educational subject knowledge graph, the relationships between knowledge mainly include: hierarchical relationships, primarily between parent and child concepts, and between concepts and entities. The concept graph expresses the relationships between concepts in the educational field. There are many conceptual contents in the educational field, and these relationships form the context of the entire knowledge. Inclusion relationships indicate several specific sub-points under a knowledge point, representing the whole and part relationship. Sequential relationships can be used for learning planning. In different subjects, there are also some special relationships, such as exclusivity and causality, which need to be refined and clarified with domain experts and knowledge engineers during the actual construction of graph resources.
Knowledge in the educational graph also has rich attributes, such as common properties like “exam points,” “difficult points,” “common mistakes,” and “exam syllabus requirements.” Different subjects have specific fine-grained attributes, such as “definition,” “properties,” “area formula,” and “perimeter formula” in mathematics.
3. Application of Knowledge Graph in Smart Education
(1) Precise User Profiling Based on Educational Knowledge Graph
Precise user profiling based on educational graphs captures key information by analyzing user behavior information and its connection with various resource objects, characterizing users. User profiles based on knowledge graphs can enhance user profiling data, providing a more comprehensive and accurate characterization of users.
1. Accurate User Profiles
User profiling technology is well-known for better characterizing users to facilitate user understanding, essentially involving “tagging.” The accuracy of user profiles determines the accuracy of user understanding. In practical applications, there are two main issues to address in user profiling: the first is incomplete profile data, and the second is incorrect profile data.
To address these issues, knowledge graphs can be utilized. The nodes in the knowledge graph represent abstractions of knowledge in the educational domain, covering a sufficient number of entities and concepts, serving as a source for user profile tags. The high quality of these tags makes them more accurate. These tags are interconnected, and the rich semantic relationships in the graph help machines understand the meaning of these tags. The friendly structure facilitates better human understanding and intuitive discovery of relationships between tags. Tag propagation and cross-domain recommendation algorithms can be employed to mine more precise tags to describe users, enriching user tags and enhancing user accuracy. Additionally, during user profiling, user profiles can be presented as a product visualization, utilizing the relationships in the graph to generate personalized and dynamically changing user knowledge graphs.
2. Accurate Learning Situation Analysis
Knowledge graphs can facilitate more precise learning situation analysis. Traditional educational experts (teachers’ experiences) primarily rely on experience to assess learners’ knowledge and ability status, lacking integration of educational measurement concepts, leading to significant randomness and subjectivity. By leveraging knowledge graphs and big data analysis, objective learning processes can be mined from multiple dimensions, analyzing data from various dimensions, not limited to test scores, error logs, and learning records, revealing knowledge mastery conditions and weak knowledge as explicit features, while also uncovering deeper learning speeds, preferences, cognitive levels, and other implicit features. This makes the analysis results more personalized and objective.
For unmet learning objectives, knowledge graphs can conduct root cause analysis, identifying weak points and associated knowledge points, effectively addressing gaps. The diagnostic process possesses better adaptability and personalization.
(2) Enhancing Teaching Quality and Efficiency
Knowledge graphs assist teachers in lesson preparation, teaching research, question generation, and examination analysis. The system can recommend related materials (lesson plans, course explanations, assignments, etc.) to teachers to enhance teaching efficiency, and graph-based searches can return the needed content more accurately.
1. Intelligent Lesson Preparation
Using subject knowledge graphs to link subject knowledge points with textbooks, lecture notes, exercises, etc., resources that meet teaching needs are continuously pushed based on teachers’ teaching progress and textbook versions, allowing for rapid and accurate access to required resources, improving lesson preparation efficiency and quality. In addition to lesson preparation, subject knowledge graphs can also assist in intelligent paper setting and examination analysis as background knowledge for related tasks, significantly enhancing the efficiency of teaching research.

Application of Knowledge Graph in Intelligent Education

2. Smart Classroom
Through precise analysis of learning situations, the system recommends relevant consolidation exercises, formulates targeted teaching strategies, and enhances teaching specificity to conduct precise teaching.
Data mining and intelligent capabilities are comprehensively utilized before, during, and after classes. Before class, data mining techniques obtain student learning situation data to formulate teaching strategies, making decision-making data-driven. During class, targeted teaching is conducted based on the formulated strategies, explaining knowledge points and facilitating group discussions. The knowledge graph visualizes the inherent connections of knowledge, helping students build a deeper understanding of knowledge. After class, relevant exercises are recommended based on students’ learning situations, learning abilities, and personalized targeted practice questions for error consolidation. This improves the quality and specificity of classroom teaching. Dynamic data analysis and dynamic learning situation diagnosis run through the entire teaching process, achieving personalized instruction and making teaching decisions data-driven and intelligent.
(3) Deep Reading
Deep reading based on knowledge graphs aims to achieve an intelligent and comprehensive understanding of knowledge connections. Utilizing entity linking technology to recognize and connect entities in electronic publications, current knowledge information can be presented in the form of knowledge cards. It can also link to other related knowledge and recommend relevant knowledge, helping users connect knowledge. This significantly promotes users’ comprehensive understanding of knowledge. Deep reading can be applied not only in education but also in knowledge management and the publishing industry.
The core technology behind deep reading is entity linking technology. Currently, our knowledge workshop has developed entity understanding services that enable machines to comprehend entities in text, making entity search and semantic search possible, achieving over 90% accuracy and recall in general domains.
(4) Q&A Robot
Education robots have become an important application in the education field. Utilizing an education robot centered on a Q&A system can facilitate course Q&A, knowledge retrieval recommendations, teaching management, and a series of teaching tasks. This alleviates teachers’ burdens and pressures while addressing students’ actual problems. An excellent and comprehensive teaching robot integrates task-oriented Q&A, chatbots, knowledge-based Q&A, and search recommendation systems, possessing multi-turn Q&A capabilities. Knowledge graphs play a crucial role in understanding query intents and generating knowledge-guided language, serving as the core of KBQA.

Application of Knowledge Graph in Intelligent Education

4. The Evolution Path of Intelligent Education Centered on Knowledge Graph
The knowledge graph increasingly assumes the mission of supporting industry intelligence. Therefore, exploring the evolution path of industry intelligence based on knowledge graphs is crucial. After years of practice, this path has become clearer, presenting an iterative development model of knowledge resource construction and knowledge application. The basic principles for implementation are overall planning, application-led, and promoting construction through usage. The iterative development path of knowledge resource construction and knowledge application.

Application of Knowledge Graph in Intelligent Education

(1) Application-led, Promoting Construction Through Usage
For the intelligent implementation in the education industry, it is necessary to follow the landing solutions of general industries and be application-oriented. There are numerous educational application scenarios; hence, overall planning is required, gradually landing based on the current business development needs and data and technology foundations of the enterprise, selecting suitable application scenarios, building graph capabilities in specific applications, and directly generating business value. It is unwise to construct graphs without specific business outlets to support them, merely as a technical capability reserve, unless it is a leading enterprise’s AiLab that does not pursue short-term monetization; otherwise, it is challenging to gain sufficient internal support. Only by generating specific business value can there be sufficient motivation to promote the long-term and steady development of intelligent transformation projects centered on knowledge graphs.
(2) Iterative Development
In the intelligent implementation of knowledge graphs in the education industry (and vertical industries), compared to knowledge application, knowledge acquisition and knowledge resource construction are greater bottlenecks. Knowledge resource construction is a long-term task that cannot be achieved overnight; it requires solid promotion of knowledge resource construction and accumulation of knowledge bases, with no shortcuts. An iterative spiral development model is adopted from point to area.
In each iteration cycle, the principle of moderation should be grasped, prioritizing application scenarios with better expected effects, reasonably controlling the boundaries and volume of knowledge, constructing knowledge resources centered on knowledge graphs, and developing corresponding knowledge applications. Then, based on feedback from internal and external users, refine the corresponding applications and knowledge resource construction. Once specific applications show initial results, gradually expand from limited applications to more scenarios, constructing more knowledge resources. The entire process continues to iterate until comprehensive intelligence is achieved.
Cognitive intelligence is the key to advanced artificial intelligence, and its realization relies on knowledge graphs. Today, the number of vertical industry intelligent applications based on knowledge graphs is increasing. Many application scenarios in the education field have shown good application effects. However, the overall application of cognitive intelligence in education is still in the development stage, with many areas for improvement. For instance, in graph construction, the educational knowledge graph has a clear demand for multimodal knowledge graphs, and the complexity of knowledge in the education field increases the difficulty of segmenting knowledge points, while the richness of relationships between knowledge points also needs continuous optimization. Overall, the application of knowledge graphs in educational intelligence is significant, with bright prospects. It is believed that with further technological maturity, support from national policies, continuous capital investment, and ongoing innovation from commercial companies, educational intelligence will achieve remarkable results in the coming years.

(Source: Hexi University)

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Application of Knowledge Graph in Intelligent Education

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