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.
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References
Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: IJCAI 2007, pp. 2670–2676 (2007)
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)
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)
Cowie, J.R., Lehnert, W.G.: Information extraction. Commun. ACM 39(1), 80–91 (1996)
Deshpande, O., et al.: Building, maintaining, and using knowledge bases: a report from the trenches. In: ACM SIGMOD 2013, pp. 1209–1220 (2013)
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)
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)
Fader, A., Zettlemoyer, L., Etzioni, O.: Open question answering over curated and extracted knowledge bases. In: 20th ACM SIGKDD 2014, pp. 1156–1165 (2014)
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)
Sen, P.: Collective context-aware topic models for entity disambiguation. In: International Conference on World Wide Web, pp. 729–738 (2012)
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)
Wang, Z., Li, J., Tang, J.: Boosting cross-lingual knowledge linking via concept annotation. In: IJCAI 2013, pp. 2733–2739 (2013)
Zins, C.: Conceptual approaches for defining data, information, and knowledge. J. Assoc. Inf. Sci. Technol. 58(4), 479–493 (2007)
Acknowledgment
This paper is supported by NSFC under Grant (No. 61363007, No. 61662021), NSF of Hainan No. ZDYF2017128 and Hainan University Project (No. hdkytg201708).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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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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