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International Conference on Collaborative Computing: Networking, Applications and Worksharing

CollaborateCom 2017: Collaborative Computing: Networking, Applications and Worksharing pp 323–332Cite as

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

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 252)

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.

Keywords

  • Resource modeling
  • Knowledge Graph
  • Service recommendation
  • Semantic modeling

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References

  1. Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: IJCAI 2007, pp. 2670–2676 (2007)

    Google Scholar 

  2. Carlson, A., Betteridge, J., Wang, R.C., Hruschka Jr., E.R., Mitchell, T.M.: Coupled semi-supervised learning for information extraction. In: WSDM 2010, pp. 101–110 (2010)

    Google Scholar 

  3. Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Ths, H.: A reference model for learning analytics. Int. J. Technol. Enhanc. Learn. 4(5/6), 318–331 (2012)

    CrossRef  Google Scholar 

  4. Cowie, J.R., Lehnert, W.G.: Information extraction. Commun. ACM 39(1), 80–91 (1996)

    CrossRef  Google Scholar 

  5. Deshpande, O., et al.: Building, maintaining, and using knowledge bases: a report from the trenches. In: ACM SIGMOD 2013, pp. 1209–1220 (2013)

    Google Scholar 

  6. Duan, Y., Fu, G., Zhou, N., Sun, X., Narendra, N.C., Hu, B.: Everything as a service (XaaS) on the cloud: origins, current and future trends. In: IEEE International Conference on Cloud Computing, pp. 621–628 (2015)

    Google Scholar 

  7. Duan, Y., Shao, L., Hu, G., Zhou, Z., Zou, Q., Lin, Z.: Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph. In: 15th IEEE SERA 2017, pp. 327–332 (2017)

    Google Scholar 

  8. Fader, A., Zettlemoyer, L., Etzioni, O.: Open question answering over curated and extracted knowledge bases. In: 20th ACM SIGKDD 2014, pp. 1156–1165 (2014)

    Google Scholar 

  9. Fu, B., Brennan, R., O’Sullivan, D.: Cross-lingual ontology mapping and its use on the multilingual semantic web. In: International Workshop on the Multilingual Semantic Web, pp. 13–20 (2010)

    Google Scholar 

  10. Sen, P.: Collective context-aware topic models for entity disambiguation. In: International Conference on World Wide Web, pp. 729–738 (2012)

    Google Scholar 

  11. Shao, L., Duan, Y., Sun, X., Zou, Q., Jing, R., Lin, J.: Bidirectional value driven design between economical planning and technical implementation based on data graph, information graph and knowledge graph. In: 15th IEEE SERA 2017, pp. 339–344 (2017)

    Google Scholar 

  12. Wang, Z., Li, J., Tang, J.: Boosting cross-lingual knowledge linking via concept annotation. In: IJCAI 2013, pp. 2733–2739 (2013)

    Google Scholar 

  13. Zins, C.: Conceptual approaches for defining data, information, and knowledge. J. Assoc. Inf. Sci. Technol. 58(4), 479–493 (2007)

    CrossRef  Google Scholar 

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Acknowledgment

This paper is supported by NSFC under Grant (No. 61363007, No. 61662021), NSF of Hainan No. ZDYF2017128 and Hainan University Project (No. hdkytg201708).

Author information

Authors and Affiliations

  1. State Key Laboratory of Marine, Resource Utilization in the South China Sea, College of Information and Technology, Hainan University, Haikou, China

    Lixu Shao & Yucong Duan

  2. Department of Information Engineering, China University of Geosciences (Beijing), Beijing, China

    Zhangbing Zhou

  3. College of Computer Science, Tianjin University, Tianjin, China

    Quan Zou

  4. Computing Center, Shanghai University, Shanghai, China

    Honghao Gao

Authors
  1. Lixu Shao
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  2. Yucong Duan
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  3. Zhangbing Zhou
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  4. Quan Zou
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  5. Honghao Gao
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Corresponding author

Correspondence to Yucong Duan .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, UK

    Imed Romdhani

  2. Guangdong University of Petrochemical Technology, Maoming, China

    Lei Shu

  3. Osaka University, Osaka, Japan

    Hara Takahiro

  4. China University of Geosciences, Beijing, China

    Zhangbing Zhou

  5. University of Nebraska–Lincoln, Lincoln, UK

    Timothy Gordon

  6. School of Computer Science, China University of Geosciences, Wuhan, Hubei, China

    Deze Zeng

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Shao, L., Duan, Y., Zhou, Z., Zou, Q., Gao, H. (2018). Learning Planning and Recommendation Based on an Adaptive Architecture on Data Graph, Information Graph and Knowledge Graph. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_30

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  • DOI: https://doi.org/10.1007/978-3-030-00916-8_30

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  • Publisher Name: Springer, Cham

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