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:2019,28(7):26-34
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教育测评知识图谱的构建及其表示学习
(北京工业大学 信息学部, 北京 100124)
Construction and Representation Learning of EAKG
(Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)
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投稿时间:2019-01-12    修订日期:2019-02-03
中文摘要: 知识图谱旨在描述现实世界中存在的实体以及实体之间的关系.自2012年谷歌提出“Google Knowledge Graph”以来,知识图谱在学术界和工业界受到广泛关注.针对教育领域中信息缺乏系统性组织的不足,本文构建了面向高中的教育测评知识图谱(Educational Assessment Knowledge Graph,EAKG),其中EAKG的构建包括基于本体技术的知识图谱模式层构建和依托于模式层结构的知识图谱数据层构建.与传统通过网页爬虫等技术手段构建的知识图谱相比,本文构建的知识图谱优点在于逻辑结构清晰,实体间关系的刻画遵循知识图谱模式层的定义.EAKG为领域内知识共享,知识推理,知识表示学习等任务提供了良好的支撑.在真实模考数据上的实验结果表明:在试卷得分预测,知识点得分预测的实体链接预测和三元组分类嵌入式表示学习任务上,引入领域本体作为模式层构建的EAKG的性能优于没有领域本体模式层单纯由数据事实构成的EAKG,实验表明,领域本体的引入对知识图谱的表示学习具有一定的指导意义.
中文关键词: 知识图谱  教育测评  语义网  本体  表示学习
Abstract:The Knowledge Graph is intended to describe the entities that exist in the real world and the relationships between entities. Since Google introduced the "Google Knowledge Graph" in 2012, knowledge graph have received widespread attention in academia and industry. Aiming at the lack of systematic organization in the field of education, the Educational Assessment Knowledge Graph (EAKG) for high schools is constructed. The construction of EAKG includes knowledge graph schema layer construction based on ontology technology and knowledge graph data layer construction based on schema layer structure. Compared with the traditional knowledge graph constructed by web crawling and other technical means, the knowledge graph constructed in this study has the advantages of clear logical structure and the description of the relationship between entities follows the definition of knowledge graph schema layer. EAKG provides good support for knowledge sharing, knowledge reasoning, knowledge representation learning and other tasks in the field. The experimental results on real simulated test data show that the EAKG constructed by introducing domain ontology as schema layer has better performance than EAKG constructed by data facts alone without domain ontology schema layer on the embedded representation learning tasks such as entity link prediction of test paper score prediction, knowledge point score prediction and triplet classification. Experiments show that the introduction of domain ontology has a certain guiding significance for knowledge graph representation learning.
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基金项目:国家重点研发计划(2016YFB1000902);南京普雷软件工程开发有限公司
引用文本:
罗明.教育测评知识图谱的构建及其表示学习.计算机系统应用,2019,28(7):26-34
LUO Ming.Construction and Representation Learning of EAKG.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):26-34

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