[1]李 振,周东岱,王 勇.“人工智能+”视域下的教育知识图谱:内涵、技术框架与应用研究[J].远程教育杂志,2019,(04):042-53.
 Li Zhen,Zhou Dongdai,Wang Yong.Research of Educational Knowledge Graph from the Perspective of Artificial Intelligence: Connotation, Technical Framework and Application[J].Distance Education Journal,2019,(04):042-53.
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“人工智能+”视域下的教育知识图谱:内涵、技术框架与应用研究()
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《远程教育杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年04期
页码:
042-53
栏目:
前沿探索
出版日期:
2019-07-10

文章信息/Info

Title:
Research of Educational Knowledge Graph from the Perspective of Artificial Intelligence: Connotation, Technical Framework and Application
作者:
李 振; 周东岱; 王 勇
1.东北师范大学 信息科学与技术学院,吉林长春 130024;2.吉林省“互联网+”教育科技创新中心,吉林长春 130117
Author(s):
Li Zhen; Zhou Dongdai; Wang Yong
1.School of Information Science and Technology, Northeast Normal University,Changchun Jilin 130024 ; 2.Science and Technology Innovation Center of Jilin Province “Internet+” Education, Changchun Jilin 130117
关键词:
人工智能教育知识图谱认知智能知识本体个性化学习
Keywords:
Artificial IntelligenceEducational Knowledge GraphCognitive IntelligenceKnowledge OntologyPersonalized Learning
分类号:
G420
文献标志码:
A
摘要:
深度学习、知识图谱、增强学习等新一代人工智能技术的发展,正驱动“互联网+教育”迈入“智能教育”新时代。知识图谱作为推动人工智能发展的核心驱动力,为教育信息化2.0时代的教育教学提供了新的赋能力量。从人工智能的研究范式来看,知识图谱是符号主义研究范式在大数据和人工智能时代的演变和发展;从人工智能的发展阶段来看,知识图谱是人工智能从“感知智能”向“认知智能”阶段进阶的重要基础。对于教育知识图谱的认知,应从资源管理、知识导航、学习认知、知识库等多维视角出发,当前的教育知识图谱可分为静态知识图谱、动态事理图谱两大类。构建教育知识图谱的关键技术主要集中在知识本体构建技术、命名实体识别技术、实体关系挖掘技术、知识融合技术等方面。从“人工智能+”视域来看,教育知识图谱在教育大数据智能化处理、教学资源语义化聚合、智慧教学优化、学习者画像模型构建、适应性学习诊断、个性化学习推荐、智能教育机器人等方面具有广阔的应用前景。
Abstract:
The development of the new generation AI technology, such as deep learning, knowledge mapping, and reinforcement learning, is driving the "Internet + education" into the new era of "intelligent education". As the core driving force to promote the development of artificial intelligence, knowledge graph provides a new capacity for education and teaching in the era of education informationization 2.0. From the research paradigm of AI, knowledge graph is the evolution and development of symbolism research paradigm in the era of big data and artificial intelligence; from the development stage of AI, knowledge graph is an important basis for the advancement of AI from "perceptual intelligence" to "cognitive intelligence". For the educational knowledge graph, we should start from multi-dimensional perspectives such as resource management, knowledge navigation, learning cognition and knowledge base. The current educational knowledge graph can be divided into static knowledge graph and dynamic reason graph. The key technologies of constructing educational knowledge graph mainly focus on the construction of knowledge ontology, named entity recognition, entity relationship mining and knowledge fusion. From the perspective of "AI +", educational knowledge graph has broad application prospects in educational fields, such as the intelligent processing of big data in education, semantic aggregation of teaching resources, intelligent teaching optimization, construction of learner model, adaptive learning diagnosis, personalized learning recommendation and intelligent educational robots.

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备注/Memo

备注/Memo:
基金项目:本文系吉林省科技发展计划项目“智能移动终端教学软件平台构建的关键技术与应用示范”(项目编号:20170204001GX)和教育部人文社会科学研究青年基金项目“智慧学习环境中精准学习者模型构建研究”(项目编号:18YJCZH169)的研究成果。
更新日期/Last Update: 1900-01-01