论文原址。本篇文章主要提出了一个自动化构建数学领域知识图谱的系统，主要应用的事NER和数据挖掘技术，其中NER主要是抽取数学概念，概念间的关系是作者自己构建的（例如先修关系）。对于数据集，作者主要从the Chinese curriculum standards of mathematics上提取的概念实体，从自己的SLP平台上，通过对学生表现来提取关系（把这部分作为数据挖掘）。本篇文章实际上可以作为构建特定领域的知识图谱的一个参考。
- the desired educational concept entities are more abstract than real world entities like PERSON, ORGANIZATION, LOCATION
- the desired relations are more cognitive and implicit, so cannot be derived from the literal meanings of text like generic knowledge graphs
- a novel but practical system
- entity recognition (NER) & association rule mining algorithms
- demonstrate an exemplary case with constructing a knowledge graph for the subject of mathematics
- Educational Concept Extraction Module:
- Implicit Relation Identification Module
From the perspective of prerequisite relation, if concept si is a prerequisite of concept sj, learners who do not master sivery likely do not master sj, and learners who master sjmost likely master si.
the Chinese curriculum standards of mathematics published by the ministry of education as the main data source
- adopt precision, recall and F1- score
- The ground truth is manually labeled by two domain experts.
students’ performance data collected by our SLP platform.
The ground truth of the prerequisite relations between selected 9 concepts are annotated manually by two domain experts.