Research on knowledge point relationship extraction for elementary mathematics

YANG Dong-ming; YANG Da-wei; GU Hang; HONG Dao-cheng; GAO Ming; WANG Ye

doi:10.3969/j.issn.1000-5641.2019.05.004

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Dec. 2019

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YANG Dong-ming, YANG Da-wei, GU Hang, HONG Dao-cheng, GAO Ming, WANG Ye. Research on knowledge point relationship extraction for elementary mathematics[J]. Journal of East China Normal University (Natural Sciences), 2019, (5): 53-65. doi: 10.3969/j.issn.1000-5641.2019.05.004

Citation:

YANG Dong-ming, YANG Da-wei, GU Hang, HONG Dao-cheng, GAO Ming, WANG Ye. Research on knowledge point relationship extraction for elementary mathematics[J]. Journal of East China Normal University (Natural Sciences), 2019, (5): 53-65. doi: 10.3969/j.issn.1000-5641.2019.05.004

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Research on knowledge point relationship extraction for elementary mathematics

doi: 10.3969/j.issn.1000-5641.2019.05.004

School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received Date: 2019-07-29
Publish Date: 2019-09-25

Abstract

Abstract

With the development of Internet technology, online education has changed the learning style of students. However, given the lack of a complete knowledge system, online education has a low degree of intelligence and a/knowledge trek0problem. The relation-extraction concept is one of the key elements of knowledge system construction. Therefore, building knowledge systems has become the core technology of online education platforms. At present, the more efficient relationship extraction algorithms are usually supervised. However, such methods suffer from low text quality, scarcity of corpus, difficulty in labeling data, low efficiency of feature engineering, and difficulty in extracting directional relationships. Therefore, this paper studies the relation-extraction algorithm between concepts based on an encyclopedic corpus and distant supervision methods. An attention mechanism based on relational representation is proposed, which can extract the forward relationship information between knowledge points. Combining the advantages of GCN and LSTM, GCLSTM is proposed, which better extracts multipoint information in sentences. Based on the attention mechanism of Transform architecture and relational representation, a BTRE model suitable for the extraction of directional relationships is proposed, which reduces the complexity of the model. Hence, a knowledge point relationship extraction system is designed and implemented. The performance and efficiency of the model are verified by designing three sets of comparative experiments.
- knowledge system construction,
- relation-extraction,
- attention mechanism,
- distant supervisor,
- Transformer

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References(20)

References

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