An automatic knowledge graph construction system for K-12 education

Chen, Penghe; Lu, Yu; Zheng, Vincent W.; Chen, Xiyang; Li, Xiaoqing

doi:10.1145/3231644.3231698

Cited by 26 publications

(16 citation statements)

References 4 publications

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“…Other sources of uncertainty, such as the PMP model structure, also affect PMP estimation which will require further attention. For example, the model used in this research is based on a linear assumption between precipitable water and PMP, which has been confirmed [ Chen and Bradley , ] as well as challenged [ Abbs , ].…”

Section: Resultsmentioning

confidence: 99%

“…Moisture maximization, as can be seen in equation , is based on a linear relationship between precipitation and precipitable water. The method is considered as an approximate solution of the mass conservation equation for an imaginary storm cell [ Chen and Bradley , ]. It is also worth mentioning that in the maximization process winds are assumed to remain favorable in terms of feeding storms with moisture.…”

Section: Methodsmentioning

confidence: 99%

“…Later, dependence of moisture maximization and spatial scale was analyzed using a numerical model. At large spatial scales, a linear relation was observed between precipitation and precipitable water [ Chen and Bradley , ]. Recently, extreme storms have been simulated by running an atmospheric model in which initial and boundary conditions were modified to produce and maintain a severe storm [ Ohara et al ., ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel method to estimate the maximization ratio of the Probable Maximum Precipitation (PMP) using regional climate model output

Rouhani

Leconte

2016

Water Resources Research

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The moisture maximization approach has a simple technique for controlling the risk of overestimating the Probable Maximum Precipitation (PMP): the maximization ratio is limited by an upper bound. The upper bound limit depends on storm records and watershed characteristics. However, it is not readily available in many watersheds. A robust scientific justification for limiting the maximization ratio is missing. In this paper, a novel approach is proposed to estimate the maximization ratio which does not impose an upper limit. The new approach, which uses regional climate model data, is based on constructing annual maximum precipitable water time series with precipitable water values for which atmospheric variables are similar to the original event to be maximized. These time series are then used to estimate the 100 year return period of the precipitable water value required to calculate the ratio. The new approach was tested in three watersheds in the province of Québec, Canada. Results showed that maximization ratio values were lower than the proposed upper bound value for these watersheds. In comparison to the approach using an upper bound, the proposed approach reduced the PMPs in these watersheds by 11%.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel method to estimate the maximization ratio of the Probable Maximum Precipitation (PMP) using regional climate model output

Rouhani

Leconte

2016

Water Resources Research

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“…According to formula (11), both rules must be valid to determine the prerequisite correlation from notion i to notion j. Thus, it is required that: ( ) ( )P Sj Si P Si Sj minprob     (12) To calculate the possibility in formula (12), this paper adopts the p-Apriori [41] algorithm. To sum up, the authors mainly introduced how to identify prerequisite correlations of learner test data, using probabilistic correlation rules.…”

Section: P Supp Sj Si Minsupp and Conf Sjmentioning

confidence: 99%

A Knowledge Image Construction Method for Effective Information Filtering and Mining From Education Big Data

et al. 2021

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Traditionally, education resources are shared insufficiently, and updated slowly; the education data are not utilized adequately. What is worse, the conventional information filtering method cannot effectively mine desired information, if the big data has a heavy noise. This article presents an information mining method from education big data, on the basis of support vector machine (SVM), and cleans the sampled abnormal data through data integration and conversion. Besides, the authors presented a method that automatically builds education knowledge image. Based on the filtered and mined education data, a neural network was designed to retrieve the themes of classroom knowledge, and the education correlations between these notions were recognized from the evaluation data by possibility correlation rules. The results show our method achieved excellent results on teaching notion retrieval and education correlation recognition.

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“…Owing to its wide application in recommendation and information retrieval, various studies have examined the domain of knowledge graph in recent years [3,5,6,8–10,16,19,21,35,36]. Knowledge graphs are usually constructed on the basis of the entities and the relationships between entities, which are extracted from external knowledge bases such as Wikipedia, YAGO and DBpedia.…”

Section: Related Workmentioning

confidence: 99%

Intelligent personalised exercise recommendation: A weighted knowledge graph‐based approach

Wang

et al. 2021

Comp Applic In Engineering

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As a critical function for intelligent tutoring system services, personalised exercise recommendation plays an important role in boosting the study performance of students. However, recent studies on personalised exercise recommendations have only considered the ability of a student during recommendation and have failed to include the essential relationships between knowledge points, which provide a suitable learning sequence of these knowledge points during a study procedure. In this study, we propose an intelligent exercise recommendation method (weighted knowledge graph‐based recommendation [WKG‐R]) for students, based on weighted knowledge graphs, wherein each node represents a knowledge point weighted by the ability of a student and an arrowed edge between two knowledge points indicates their prerequisite relationship. The novelty of WKG‐R can be summarised as follows: (1) It makes a leading attempt to quantify the ability of a student based on various testing behaviours and (2) it attempts to employ the ability of a student and the prerequisite dependencies between knowledge points for enhancing the effectiveness of personalised exercise recommendation. A real classroom teaching practice was conducted to evaluate the effectiveness of the proposed WKG‐R method. The experimental results demonstrated the distinct advantage of WKG‐R in improving the testing scores of students as compared with contemporary solutions. The average score improvement ratio of students for WKG‐R is up to 33%, whereas for the state‐of‐the‐art solution, it is only 22%. The questionnaires collected from the students also reflected a higher level of satisfaction towards WKG‐R than with the contemporary solutions.

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An automatic knowledge graph construction system for K-12 education

Cited by 26 publications

References 4 publications

A novel method to estimate the maximization ratio of the Probable Maximum Precipitation (PMP) using regional climate model output

A novel method to estimate the maximization ratio of the Probable Maximum Precipitation (PMP) using regional climate model output

A Knowledge Image Construction Method for Effective Information Filtering and Mining From Education Big Data

Intelligent personalised exercise recommendation: A weighted knowledge graph‐based approach

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