基于属性关系深度挖掘的试题知识点标注模型

在各类在线学习系统中，为了给学生提供优质的学习资源，一个基础性的任务是对大量未标注的试题进行知识点标注.已有标注方法通常基于人工专家标注或者采用传统机器学习方法.在实际应用中，这些方法普遍存在成本过高、标注精准度不足等局限.为此，本文提出了一种基于属性关系深度挖掘的试题知识点标注模型.首先，利用句法语义模型和结构语义模型分别从试题文本和试题图形中抽取试题的显性属性关系.然后，利用蒙特卡罗树搜索构建问题求解框架，挖掘试题的隐含属性关系.最后，结合学科知识图谱，将属性关系映射到知识图谱空间，生成试题知识点.实验结果表明，所提出的方法能够有效地进行试题知识点标注，将对学生认知诊断、个性化试题推荐等具有一定的实际应用价值.

Knowledge points annotation based on attribute relation mining

Online learning systems need to perform the fundamental task of annotating a large number of raw questions to be able to provide students with learning materials of high quality.The existing methods used for this task rely either on labeling by human experts or traditional ways of machine learning.In practical applications,the existing methods are limited by being either labor intensive or inaccurate.In this paper,we propose a method based on the mining of attribute relations to annotate the knowledge points of questions.We first define and extract the explicit attribute relations from the text and diagram of a given question.We then extract the implicit attribute relations of the question using Monte Carlo Tree Search (MCTS) algorithm.Next,we map the attribute relations to the knowledge graph space using a transform model,to generate the knowledge points of the question.The experimental results confirm the effectiveness of the proposed method,which demonstrates practicality for the cognitive diagnosis of students and personalized questions recommendation.