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Constructing Knowledge Graphs for Online Collaborative Programming
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DOI：10.1109/ACCESS.2021.3106324.png)
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Abstract
En 中文
This study aimed to automatically construct knowledge graphs for online collaborative programming. We proposed several models and developed a system to construct knowledge graphs based on online discussion texts and the target knowledge graph for the C programming language. Our system included two main modules, namely, entity recognition and relation extraction. We proposed an innovative approach for recognizing knowledge entities, which included sequence tagging, text classification, and keyword matching. The extraction of relationships among knowledge entities was performed through queries of the target knowledge graph. The six kinds of knowledge graphs could be automatically generated through our method, including the activated and unactivated knowledge graphs of each student, each group, and each class. The accuracy of entity recognition reached 87.27%. The accuracies of relation extraction for students, groups, and the class reached 89.7%, 90.4%, and 90.2%, respectively. This study is very promising and significant for both teachers and practitioners to provide interventions and personalized learning services based on the constructed knowledge graphs.
Keywords:
Collaboration
Collaborative work
Text recognition
Programming profession
Text categorization
Knowledge engineering
Tagging
Knowledge graph
entity recognition
relation extraction
deep neural network model
online collaborative learning
collaborative programming
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