Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings

Research Article

Learning Planning and Recommendation Based on an Adaptive Architecture on Data Graph, Information Graph and Knowledge Graph

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  • @INPROCEEDINGS{10.1007/978-3-030-00916-8_30,
        author={Lixu Shao and Yucong Duan and Zhangbing Zhou and Quan Zou and Honghao Gao},
        title={Learning Planning and Recommendation Based on an Adaptive Architecture on Data Graph, Information Graph and Knowledge Graph},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2018},
        month={10},
        keywords={Resource modeling Knowledge Graph Service recommendation Semantic modeling},
        doi={10.1007/978-3-030-00916-8_30}
    }
    
  • Lixu Shao
    Yucong Duan
    Zhangbing Zhou
    Quan Zou
    Honghao Gao
    Year: 2018
    Learning Planning and Recommendation Based on an Adaptive Architecture on Data Graph, Information Graph and Knowledge Graph
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-00916-8_30
Lixu Shao1,*, Yucong Duan1,*, Zhangbing Zhou2,*, Quan Zou3,*, Honghao Gao4,*
  • 1: Hainan University
  • 2: China University of Geosciences (Beijing)
  • 3: Tianjin University
  • 4: Shanghai University
*Contact email: 751486692@qq.com, duanyucong@hotmail.com, zbzhou@cugb.edu.cn, zouquan@tju.edu.com, gaohonghao@shu.edu.cn

Abstract

With massive learning resources that contain data, information and knowledge on Internet, users are easy to get lost and confused in processing of learning. Automatic processing, automatic synthesis, and automatic analysis of natural language, such as the original representation of the resources of these data, information and knowledge, have become a huge challenge. We propose a three-layer architecture composing Data Graph, Information Graph and Knowledge Graph which can automatically abstract and adjust resources. This architecture recursively supports integration of empirical knowledge and efficient automatic semantic analysis of resource elements through frequency focused profiling on Data Graph and optimal search through abstraction on Information Graph and Knowledge Graph. Our proposed architecture is supported by the 5W (Who/When/Where, What and How) to interface users’ learning needs, learning processes, and learning objectives which can provide users with personalized learning service recommendation.