Abstract
We present an end-to-end system, called EasyKG, throughout the whole lifecycle of knowledge graph (KG) construction. It has a pluggable pipeline architecture containing the components of knowledge modeling, knowledge extraction, knowledge reasoning, knowledge management and so forth. Users can automatically generate such a pipeline so as to obtain a domain-specific KG. Advanced users are allowed to create a pipeline in a drag-and-drop manner with customized components. EasyKG lowers the barriers of KG construction. Moreover, EasyKG allows users to evaluate different components and KGs, and share them across different domains so as to further reduce the cost of construction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Carlson, A., Betteridge, J., Kisiel, B., et al.: Toward an architecture for never-ending language learning. In: Twenty-Fourth AAAI Conference on Artificial Intelligence (2010)
Dong, X., Gabrilovich, E., Heitz, G., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601–610. ACM (2014)
Elsken, T., Metzen, J.H., Hutter, F., et al.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(55), 1–21 (2018)
Gliozzo, A., Ackerson, C., Bhattacharya, R., et al.: Building Cognitive Applications with IBM Watson Services: Volume 1 Getting Started. IBM Redbooks, New York (2017)
Jia, Y., Wang, Y., Lin, H., et al.: Locally adaptive translation for knowledge graph embedding. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 992–998. AAAI (2016)
Jia, Y., Wang, Y., Cheng, X., et al.: OpenKN: an open knowledge computational engine for network big data. In: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining, pp. 657–664. IEEE Press (2014)
Melchior, A., Peralta, E., Valiente, M., et al.: KNIME: the Konstanz information miner. ACM SIGKDD Explor. Newslett. 11(1), 26–31 (2006)
Nickel, M., Murphy, K., Tresp, V., et al.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2015)
Ratner, A., Bach, S.H., Ehrenberg, H., et al.: Snorkel: rapid training data creation with weak supervision. Proc. VLDB Endow. 11(3), 269–282 (2017)
Rotmensch, M., Halpern, Y., Tlimat, L., et al.: Learning a health knowledge graph from electronic medical records. Sci. Rep. 7(1), 5994 (2017)
Singhal, A.: Introducing the knowledge graph: things, not strings. Official Google Blog, May 2012
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: Proceedings of 5th International Conference on Learning Representations (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jia, Y., Liu, D., Sheng, Z., Feng, L., Liu, Y., Guo, S. (2020). EasyKG: An End-to-End Knowledge Graph Construction System. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_22
Download citation
DOI: https://doi.org/10.1007/978-981-15-3412-6_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3411-9
Online ISBN: 978-981-15-3412-6
eBook Packages: Computer ScienceComputer Science (R0)