PURE: a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction
Abstract Traditional education systems obscure the diverse interconnections inherent within subject knowledge, thus failing to meet the current demand for personalized and adaptive learning experiences. Recent advances have explored various relation extraction techniques to construct educational kno...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01692-w |
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| _version_ | 1832571141719326720 |
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| author | Xiaohui Cui Yu Yang Dongmei Li Jinman Cui Xiaolong Qu Chao Song Haoran Liu Siyuan Ke |
| author_facet | Xiaohui Cui Yu Yang Dongmei Li Jinman Cui Xiaolong Qu Chao Song Haoran Liu Siyuan Ke |
| author_sort | Xiaohui Cui |
| collection | DOAJ |
| description | Abstract Traditional education systems obscure the diverse interconnections inherent within subject knowledge, thus failing to meet the current demand for personalized and adaptive learning experiences. Recent advances have explored various relation extraction techniques to construct educational knowledge graphs that integrate dispersed subject knowledge into a unified framework. However, educational conceptual entities are far more abstract and intricate compared to their real-world equivalents, and these techniques primarily focus on static knowledge, overlooking the dynamic nature of knowledge in practical learning and application scenarios. To address these issues, we propose a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction (PURE). This framework embraces a prompt-tuning strategy and employs a more appropriate MacBERT-large model to encode the instances wrapped by prompt templates. Furthermore, we construct an instance-relation database that serves as an external knowledge base of our framework. A dynamic instance-relation update mechanism is proposed to refine the database, thus enhancing the accuracy of PURE in predicting new triples. We conduct experiments on a Data Structure course relation extraction dataset and three public datasets. The experimental results demonstrate that PURE achieves significant improvements and outperforms several state-of-the-art baselines in efficiency of extraction and utilization of educational information. Comparable performance is achieved even in more complex biomedical relation extraction, validating its robustness and applicability to other domains. |
| format | Article |
| id | doaj-art-0474f1454f9240e7b7aef32526943368 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-0474f1454f9240e7b7aef325269433682025-02-02T12:49:57ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111111410.1007/s40747-024-01692-wPURE: a Prompt-based framework with dynamic Update mechanism for educational Relation ExtractionXiaohui Cui0Yu Yang1Dongmei Li2Jinman Cui3Xiaolong Qu4Chao Song5Haoran Liu6Siyuan Ke7School of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversityAbstract Traditional education systems obscure the diverse interconnections inherent within subject knowledge, thus failing to meet the current demand for personalized and adaptive learning experiences. Recent advances have explored various relation extraction techniques to construct educational knowledge graphs that integrate dispersed subject knowledge into a unified framework. However, educational conceptual entities are far more abstract and intricate compared to their real-world equivalents, and these techniques primarily focus on static knowledge, overlooking the dynamic nature of knowledge in practical learning and application scenarios. To address these issues, we propose a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction (PURE). This framework embraces a prompt-tuning strategy and employs a more appropriate MacBERT-large model to encode the instances wrapped by prompt templates. Furthermore, we construct an instance-relation database that serves as an external knowledge base of our framework. A dynamic instance-relation update mechanism is proposed to refine the database, thus enhancing the accuracy of PURE in predicting new triples. We conduct experiments on a Data Structure course relation extraction dataset and three public datasets. The experimental results demonstrate that PURE achieves significant improvements and outperforms several state-of-the-art baselines in efficiency of extraction and utilization of educational information. Comparable performance is achieved even in more complex biomedical relation extraction, validating its robustness and applicability to other domains.https://doi.org/10.1007/s40747-024-01692-wRelation extractionPrompt learningDynamic update mechanismEducationData Structure |
| spellingShingle | Xiaohui Cui Yu Yang Dongmei Li Jinman Cui Xiaolong Qu Chao Song Haoran Liu Siyuan Ke PURE: a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction Complex & Intelligent Systems Relation extraction Prompt learning Dynamic update mechanism Education Data Structure |
| title | PURE: a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction |
| title_full | PURE: a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction |
| title_fullStr | PURE: a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction |
| title_full_unstemmed | PURE: a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction |
| title_short | PURE: a Prompt-based framework with dynamic Update mechanism for educational Relation Extraction |
| title_sort | pure a prompt based framework with dynamic update mechanism for educational relation extraction |
| topic | Relation extraction Prompt learning Dynamic update mechanism Education Data Structure |
| url | https://doi.org/10.1007/s40747-024-01692-w |
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