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A Markov Approximation Algorithm for Computation Offloading and Resource Scheduling in Mobile Edge Computing Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Tutorial and Survey on Fault Knowledge Graph

Authors : XiuQing Wang, ShunKun Yang

Published in: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health

Publisher: Springer Singapore

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Abstract

Knowledge Graph (KG) is a graph-based data structure that can display the relationship between a large number of semi-structured and unstructured data, and can efficiently and intelligently search for information that users need. KG has been widely used for many fields including finance, medical care, biological, education, journalism, smart search and other industries. With the increase in the application of Knowledge Graphs (KGs) in the field of failure, such as mechanical engineering, trains, power grids, equipment failures, etc. However, the summary of the system of fault KGs is relatively small. Therefore, this article provides a comprehensive tutorial and survey about the recent advances toward the construction of fault KG. Specifically, it will provide an overview of the fault KG and summarize the key techniques for building a KG to guide the construction of the KG in the fault domain. What’s more, it introduces some of the open source tools that can be used to build a KG process, enabling researchers and practitioners to quickly get started in this field. In addition, the article discusses the application of fault KG and the difficulties and challenges in constructing fault KG. Finally, the article looks forward to the future development of KG.

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Literature
1.
Fu, X., Ren, X., Mengshoel, O.J., et al.: Stochastic optimization for market return prediction using financial knowledge graph. In: 2018 IEEE International Conference on Big Knowledge (ICBK). IEEE Computer Society (2018)
2.
3.
Shen, Y., Yuan, K., Dai, J., et al.: KGDDS: a System for Drug-Drug Similarity Measure in therapeutic substitution based on knowledge graph curation. J. Med. Syst. 43(4), 43 (2019)CrossRef
4.
Shengtian, S., Zhihao, Y., Lei, W., et al.: SemaTyP: a knowledge graph based literature mining method for drug discovery. BMC Bioinform. 19(1), 193 (2018)CrossRef
5.
Sang, S., Yang, Z., Liu, X., et al.: GrEDeL: a knowledge graph embedding based method for drug discovery from biomedical literature. IEEE Access 7, 8404–8415 (2018)CrossRef
6.
Ali, M., Hoyt, C.T., Domingo-Fernandez, D., et al.: BioKEEN: a library for learning and evaluating biological knowledge graph embeddings. BioRxiv, 475202 (2018)
7.
Alshahrani, M., Khan, M.A., Maddouri, O., et al.: Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics 33(17), 2723–2730 (2017)CrossRef
8.
Xiaoxue, L., Xuesong, B., Longhe, W., et al.: Review and trend analysis of knowledge graphs for crop pest and diseases. IEEE Access 7, 62251–62264 (2019)CrossRef
9.
Chenglin, Q., Qing, S., Pengzhou, Z., et al.: Cn-makg: China meteorology and agriculture knowledge graph construction based on semi-structured data. In: Proceedings of the 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), F, 2018. IEEE (2018)
10.
Sawant, U., Garg, S., Chakrabarti, S., et al.: Neural architecture for question answering using a knowledge graph and web corpus. Inf. Retrieval J. 22(3–4), 324–349 (2019)CrossRef
11.
Shin, S., Jin, X., Jung, J., et al.: Predicate constraints based question answering over knowledge graph. Inf. Process. Manage. 56(3), 445–462 (2019)CrossRef
12.
Zheng, W., Cheng, H., Yu, J.X., et al.: Interactive natural language question answering over knowledge graphs. Inf. Sci. 481, 141–159 (2019)MathSciNetCrossRef
13.
Lu, Y.-C., Wen, Y.-J., Xuan, L., et al.: Exploration of the construction and application of knowledge graph in equipment failure. DEStech Transactions on Computer Science and Engineering, (smce) (2017)
14.
Qin, Z., Cen, C., Jie, W., et al.: Knowledge-graph based multi-target deep-learning models for train anomaly detection. In: Proceedings of the 2018 International Conference on Intelligent Rail Transportation (ICIRT). IEEE (2018)
15.
Shan, X., Zhu, B., Wang, B., et al.: Research on deep learning based dispatching fault disposal robot technology. In: Proceedings of the 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE (2018)
16.
Tang, Y., Liu, T., Liu, G., et al.: Enhancement of power equipment management using knowledge graph. arXiv preprint arXiv:​190412242 (2019)
17.
Steiner, T., Verborgh, R., Troncy, R., et al.: Adding realtime coverage to the google knowledge graph. In: Proceedings of the 11th International Semantic Web Conference (ISWC 2012). Citeseer (2012)
18.
Zheng, M., Ma, Y., Zheng, A., et al.: Constructing method of public opinion knowledge graph with online news comments. In: Proceedings of the 2018 International Conference on Robots & Intelligent System (ICRIS). IEEE (2018)
19.
Choudhury, S., Agarwal, K., Purohit, S., et al.: Nous: construction and querying of dynamic knowledge graphs. In: Proceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE (2017)
20.
Zheng, M., Ma, Y., Zheng, A., et al.: Constructing method of public opinion knowledge graph with online news comments. In: Proceedings of the 2018 International Conference on Robots & Intelligent System (ICRIS). IEEE (2018)
21.
Heydon, A., Najork, M.: Mercator: a scalable, extensible web crawler. World Wide Web 2(4), 219–229 (1999)CrossRef
22.
De Groc, C.: Babouk: focused web crawling for corpus compilation and automatic terminology extraction. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. IEEE (2011)
23.
Xia, J., Wan, W., Liu, R., et al.: Distributed web crawling: a framework for crawling of micro-blog data (2015)
24.
Cowie, J., Wilks, Y.: Information extraction. Handbook Nat. Lang. Process. 56, 57 (2000)
25.
Lian, H., Qin, Z., He, T., et al.: Knowledge graph construction based on judicial data with social media. In: Proceedings of the 2017 14th Web Information Systems and Applications Conference (WISA). IEEE (2017)
26.
Wang, X., Ma, C., Liu, P., et al.: A potential solution for intelligent energy management-knowledge graph. In: Proceedings of the 2018 IEEE International Conference on Energy Internet (ICEI). IEEE (2018)
27.
Li, Y., Wang, C., Han, F., et al. Mining evidences for named entity disambiguation. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2013)
28.
Urata, T., Maeda, A.: An entity disambiguation approach based on wikipedia for entity linking in microblogs. In: Proceedings of the 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE (2017)
29.
Wang, X., Ma, C., Liu, P., et al.: A potential solution for intelligent energy management-knowledge graph. In: Proceedings of the 2018 IEEE International Conference on Energy Internet (ICEI). IEEE (2018)
30.
Song, Q., Liu, J., Wang, X., et al.: A novel automatic ontology construction method based on web data. In: Proceedings of the 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE (2014)
31.
Navarro, L.F., Hruschka, E.R., Appel, A.P.: Finding inference rules using graph mining in ontological knowledge bases. In: Proceedings of the 2016 5th Brazilian Conference on Intelligent Systems (BRACIS). IEEE (2016)
32.
Appel, A.P., Junior, E.R.H.: Prophet–a link-predictor to learn new rules on NELL. In: Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE (2011)
33.
34.
Tsai, S.-F., Tang, H., Tang, F., et al.: Ontological inference framework with joint ontology construction and learning for image understanding. In: Proceedings of the 2012 IEEE International Conference on Multimedia and Expo. IEEE (2012)
35.
Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 100–110 (1999)
36.
Cucerzan, S., Yarowsky, D.: Language independent named entity recognition combining morphological and contextual evidence. In: Proceedings of the 1999 Joint SIGDAT Conference on EMNLP and VLC, pp. 90–99 (1999)
37.
Isozaki, H., Kazawa, H.:[ Association for Computational Linguistics the 19th international conference - Taipei, Taiwan (2002.08.24–2002.09.01)] Proceedings of the 19th international conference on Computational linguistics, - - Efficient support vector classifiers for named entity recognition[In: Proceedings of the 19th International Conference on Computational Linguistics, vol. 1, pp. 1–7 (2002)
38.
Borthwick, A.E.: A Maximum Entropy Approach to Named Entity Recognition. New York University, New York (1999)
39.
Bikel, D.M., Miller, S., Schwartz, R., et al.: Nymble: a High-Performance Learning Name-finder. Anlp 94–201 (1998)
40.
Bikel, D.M.: An algorithm that learns what’s in a name. Machine Learning 34 (1999)CrossRef
41.
Mccallum, A., Li, W.: [Association for Computational Linguistics the seventh conference - Edmonton, Canada (2003.05.31-.)] Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, - - Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons, vol. 4, pp. 188–191 (2003)
42.
Bordes, A., Usunier, N., Garcia-Duran, A., et al.: Translating embeddings for modeling multi-relational data. In: Proceedings of the Advances in Neural Information Processing Systems (2013)
43.
Wang, Z., Zhang, J., Feng, J., et al.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
44.
Lin, Y., Liu, Z., Sun, M., et al.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
45.
Ji, G., He, S., Xu, L., et al.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2015)
46.
Xiao, H., Huang, M., Hao, Y., et al.: TransA: An adaptive approach for knowledge graph embedding. arXiv preprint arXiv:​150905490 (2015)
47.
Ji, G., Liu, K., He S., et al.: Knowledge graph completion with adaptive sparse transfer matrix. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2016)
48.
He, S., Liu, K., Ji, G., et al.: Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM (2015)
49.
Xiao, H., Huang, M., Hao, Y., et al.: TransG: a generative mixture model for knowledge graph embedding. arXiv preprint arXiv:​150905488 (2015)
50.
Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Citeseer (2010)
51.
Manning, C., Surdeanu, M., Bauer, J., et al.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (2014)
52.
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media Inc., Beijing (2009)MATH
53.
Qiu, X., Zhang, Q., Huang, X.: Fudannlp: a toolkit for chinese natural language processing. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations (2013)
54.
Zhang, C.: DeepDive: A Data Management System for Automatic Knowledge Base Construction. University of Wisconsin-Madison, Madison (2015)
55.
Suchanek, F.M., Sozio, M., Weikum, G.: SOFIE: a self-organizing framework for information extraction. In: Proceedings of the 18th International Conference on World wide web. ACM (2009)
56.
Baldridge, J., Chatterjee, S., Palmer, A., et al.: DotCCG and VisCCG: Wiki and programming paradigms for improved grammar engineering with OpenCCG; proceedings of the CSLI Studies in Computational Linguistics Online. Citeseer (2007)
57.
Miller, E.: An Introduction to the Resource Description Framework. Bull. Am. Soc. Inf. Sci. Technol. 25(1), 15–19 (1998)MathSciNetCrossRef
58.
Bechhofer, S.: OWL: web ontology language. Encyclopedia Inf. Sci. Technol. Second Ed. 63(45), 990–996 (2004)
59.
Partner, J., Vukotic, A., Watt, N.: Neo4j in Action. Pearson Schweiz Ag (2014)
60.
Chinchor, N., Marsh, E.: Muc-7 information extraction task definition. In: Proceeding of the Seventh Message Understanding Conference (MUC-7), Appendices (1998)
61.
Vilain, M., Burger, J., Aberdeen, J.: Proceedings of the 6th Conference on Message Understanding (MUC-6) (1995)
62.
Brants, T.: Proceedings of the Sixth Conference on Applied Natural Language Processing (2000)
63.
Kambhatla, N.: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions (2004)
64.
Gonzalez, E., Turmo, J.: Unsupervised relation extraction by massive clustering. In: Proceedings of the 2009 Ninth IEEE International Conference on Data Mining. IEEE (2009)
65.
Liu, X., Yu, N.: Multi-type web relation extraction based on bootstrapping. In: proceedings of the 2010 WASE International Conference on Information Engineering. IEEE (2010)
66.
Hendrickx, I., Kim, S.N., Kozareva, Z., et al.: Semeval-2010 task 8: multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Association for Computational Linguistics (2009)
67.
Socher, R., Huval, B., Manning, C.D., et al.: Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics (2012)
68.
Zeng, D., Liu, K., Lai, S., et al.: Relation classification via convolutional deep neural network (2014)
69.
Nguyen, T.H., Grishman, R.: Relation extraction: perspective from convolutional neural networks. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing (2015)
70.
Lin, Y., Shen, S., Liu, Z., et al.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2016)
71.
Zheng, S., Hao, Y., Lu, D., et al.: Joint entity and relation extraction based on a hybrid neural network. Neurocomputing 257, 59–66 (2017)CrossRef
72.
Li, Q., Ji, H.: Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers (2014)
73.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: proceedings of the Advances in Neural Information Processing Systems (2012)
74.
Goller, C., Kuchler, A.: Learning task-dependent distributed representations by backpropagation through structure. In: Proceedings of International Conference on Neural Networks (ICNN 1996). IEEE (1996)
75.
Wang, C., Gao, M., He, X., et al.: Challenges in chinese knowledge graph construction. In: Proceedings of the 2015 31st IEEE International Conference on Data Engineering Workshops. IEEE (2015)
76.
Duan, Y., Shao, L., Hu, G., et al.: Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph. In: Proceedings of the 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA). IEEE (2017)
77.
Neil, D., Briody, J., Lacoste, A., et al.: Interpretable graph convolutional neural networks for inference on noisy knowledge graphs. arXiv preprint arXiv:​181200279 (2018)
78.
He, Z., Chen, W., Li, Z., et al.: SEE: syntax-aware entity embedding for neural relation extraction. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (2018)
79.
Guo, X., Zhang, H., Yang, H., et al.: A single attention-based combination of CNN and RNN for relation classification. IEEE Access 7, 12467–12475 (2019)CrossRef
80.
Nie, B., Sun, S.: Knowledge graph embedding via reasoning over entities, relations, and text. Future Gener. Comput. Syst. 91, 426–433 (2019)CrossRef
81.
Yan, D., Hu, B.: Shared representation generator for relation extraction with Piecewise-LSTM convolutional neural networks. IEEE Access 7, 31672–31680 (2019)CrossRef
82.
Zhang, C., Cui, C., Gao, S., et al.: Multi-gram CNN-based self-attention model for relation classification. IEEE Access 7, 5343–5357 (2019)CrossRef
83.
Guo, X., Zhang, H., Yang, H., et al.: A single attention-based combination of CNN and RNN for relation classification. IEEE Access 7, 12467–12475 (2019)CrossRef
84.
Shen, Y., Sun, J, Jia, P., et al.: Entity-dependent long-short time memory network for semantic relation extraction. In: Proceedings of the 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE (2019)
85.
Le, H.Q., Nguyen, T.M., Vu, S.T., et al.: D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information. Bioinformatics (2018)
86.
Chen, H., Lin, Z., Ding, G., et al.: GRN: gated relation network to enhance convolutional neural network for named entity recognition (2019)
Metadata
Title
A Tutorial and Survey on Fault Knowledge Graph
Authors
XiuQing Wang
ShunKun Yang
Copyright Year
2019
Publisher
Springer Singapore
Book
Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health

Print ISBN: 978-981-15-1924-6

Electronic ISBN: 978-981-15-1925-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-15-1925-3_19

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