Topic Extraction and Interactive Knowledge Graphs for Learning Resources

Abstract

Humanity development through education is an important method of sustainable development. This guarantees community development at present time without any negative effects in the future and also provides prosperity for future generations. E-learning is a natural development of the educational tools in this era and current circumstances. Thanks to the rapid development of computer sciences and telecommunication technologies, this has evolved impressively. In spite of facilitating the educational process, this development has also provided a massive amount of learning resources, which makes the task of searching and extracting useful learning resources difficult. Therefore, new tools need to be advanced to facilitate this development. In this paper we present a new algorithm that has the ability to extract the main topics from textual learning resources, link related resources and generate interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks no matter how big or small the texts are. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm’s accuracy was evaluated against Gensim, largely improving its accuracy. This could be a step towards strengthening self-learning and supporting the sustainable development of communities, and more broadly of humanity, across different generations.

KG-CFSA: A Comprehensive Approach for Analyzing Multi-source Heterogeneous Social Network Knowledge Graph

“…This approach improved the selection of properties for graph models by 85%. Another approach suggested by [12] is to use the main topics of the learning resources to extract knowledge by merging the information and updating it using a search for the subject category on Wikipedia. The algorithm has three layers: text extraction, keyword extraction, and category extraction.…”

Knowledge Graph: A Survey

“…This step is crucial in developing a knowledge graph, and it relies on relation extraction to complete the extraction of the knowledge graph task. [1] used a topic modeling approach to extract and develop a knowledge graph for learning resources. The algorithm of topic extraction for knowledge graphs has the capability to extract main topics from the text corpus using the extracted topics to link related resources and generate a dynamic knowledge graph.…”

“…Another information extraction mechanism uses graph convolution network (GCN), [2] combined event trigger and GCN in mapping related information using the shortest path between an entity and related aggregated data. The GCN approach involves tokenizing a sentence as a set of entries and passing sentence to a Bi-LSTM layer for classification.…”

“…Authors in [1] published an interesting methodology for extracting topics from learning resources and representing them as interactive knowledge graphs. The study aims to help learners understand and navigate complex learning materials, leading to more effective learning outcomes.…”

Examining the Characteristics of Practical Knowledge From Four Public Facebook Communities of Practice in Instructional Design and Technology

“…These awareness tools take the form of knowledge graphs and content recommendation systems that help with the visualization and summarization of large amounts of text. For instance, Badaway et al [66] leveraged topic modeling to initially assign labels to Wikipedia resources that served as the foundation for implementing interactive knowledge graphs. Knowledge graphs allowed for the visualization of the relationships among resources.…”