Abstract
With the widespread use of big data, knowledge graph has become a new hotspot. It is used in intelligent question answering, recommendation system, map navigation and so on. Constructing a knowledge graph includes ontology construction, annotated data, relation extraction, and ontology inspection. Relation extraction is to solve the problem of entity semantic linking, which is of great significance to many natural language processing applications. Research related to relation extraction has gained momentum in recent years, necessitating a comprehensive survey to offer a bird’s-eye view of the current state of relation extraction. In this paper, we discuss the development process of relation extraction, and classify the relation extraction algorithms in recent years. Furthermore, we discuss deep learning, reinforcement learning, active learning and transfer learning. By analyzing the basic principles of supervised learning, unsupervised learning, semi-supervised learning and distant supervision, we elucidate the characteristics of different relation extraction algorithms, and give the potential research directions in the future.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xu, B., et al.: CN-DBpedia: a never-ending chinese knowledge extraction system. In: Benferhat, S., Tabia, K., Ali, M. (eds.) IEA/AIE 2017. LNCS (LNAI), vol. 10351, pp. 428–438. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60045-1_44
Niu, X., Sun, X.R., Wang, H.F., et al.: Zhishi.meweaving Chinese linking open data. In: Proceedings of the 10th International Semantic Web Conference, Bonn, Germany, pp. 205–220 (2011)
Pan, J.Z., Horrocks, I.: RDFS(FA): connecting RDF(S) and OWL DL. IEEE Trans. Knowl. Data Eng. 19(2), 192–206 (2007). https://doi.org/10.1109/TKDE.2007.37
Mcguiness, D.L., Harmelen, F.: OWL Web ontology language overview. W3C Recomm. 63(45), 990–996 (2004)
Qiao, L., Yang, L., Hong, D., et al.: Knowledge graph construction techniques. J. Comput. Res. Dev. 53(3), 582–600 (2016). (in Chinese)
Zhang, C., Chang, L., Wang, W., Chen, H., Bin, C.: Question and answer over fine-grained knowledge graph based on BiLSTM-CRF (2019)
Proceedings of the 6th Message Understanding Conference (MUC - 7). National Institute of Standars and Technology (1998)
Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge unifying WordNet and Wikipedia. In: Proceedings of WWW (2007)
Banko, M., Cafarella, M.J., Soderland, S., et al.: Open information extraction for the web. In: Proceedings of the 20th Int Joint Conf on Artificial Intelligence, pp. 2670–2676. ACM, New York (2007)
Yang, B., Cai, D.-F., Yang, H.: Progress in open information extraction. J. Chin. Inf. Process. 4, 1–11 (2014)
Etzioni, O., Cafarella, M., Downey, D., et al.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165(1), 91–134 (2005)
Banko, M., Cafarella, M.J., Soderland, S., et al.: Open information extraction from the web. In: Proceedings of IJCAI (2007)
Banko, M., Etzioni, O.: The tradeoffs between open and traditional relation extraction. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (2008)
Wu, F., Weld, D.S.: Open information extraction using Wikipedia. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, pp. 118–127 (2010)
Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (2011)
Etzioni, O., Fader, A., Christensen, J., et al.: Open information extraction: the second generation. In: Proceedings of International Joint Conference on Artificial Intelligence (2011)
Xu, J., Zhang, Z., Wu, Z.: Review on techniques of entity relation extraction. New Technol. Libr. Inf. Serv. 168(8), 18–23 (2008)
Gudovskiy, D., Hodgkinson, A.: Explanation-based attention for semi-supervised deep active learning (2019)
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M.: Dueling network architectures for deep reinforcement learning (2019)
Hasegawa, T., Sekine, S., Grishman, R.: Discovering relations among named entities from large corpora. In: Proceedings of ACL-2004, pp. 415–422 (2004)
Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion (2013)
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI (2011)
Jenatton, R., Le Roux, N., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: NIPS (2012)
Heck, L., Hakkani-Tür, D., Tur, G.: Leveraging knowledge graphs for web-scale unsupervised semantic parsing. In: ISCA (2013)
Luus, F., Khan, N., Akhalwaya, I.: Active learning with TensorBoard projector (2019)
Liu, F., Zhong, Z., Lei, L., Wu, Y.: Entity relation extraction method based on machine learning (2013)
Xia, S., Lehong, D.: Feature-based approach to Chinese term relation extraction In: 2009 International Conference on Signal Processing Systems, pp. 410–414 (2009)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge University (2000)
Zhang, T.: Regularized winnow methods. In: Advances in Neural Information Processing Systems 13, pp. 703–709 (2001)
Dong, X.L., Gabrilovich, E., Heitz, G.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: KDD (2014)
Zhou, Z.-H.: Cooperative Training Style in Semi-Supervised Learning. Machine Learning and Its Applications, pp. 259–275. Tsinghua University Press, Beijing (2007)
Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning (2006). The MIT Press, Cambridge
Pise, N.N., Kulkarni, P.: A survey of semi-supervised learning methods. In: 2008 International Conference on Computational Intelligence and Security (2008)
Zhou, Z.-H., Li, M.: Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans. Knowl. Data Eng. 17(11), 1529–1541 (2005)
Li, M., Zhou, Z.-H.: Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Trans. Syst. 19(11), 1479–1493 (2007)
Zhang, M.-L., Zhou, Z.-H.: CoTRADE: confident co-training with data editing. IEEE Trans. Syst. Man Cybern. Part B Cybern. 41, 1612–1626 (2011)
Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: CoTRADE: knowledge-based weak supervision for information extraction of overlapping relations. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 541–550 (2011)
Li, Q., Han, Z., Wu, X.-M.: CoTRADE: deeper insights into graph convolutional networks for semi-supervised learning. In: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) (2018)
Luan, Y., Wadden, D., He, L., Shah, A., Ostendorf, M., Hajishirzi, H.: CoTRADE: a general framework for information extraction using dynamic span graphs. In: NAACL (2019)
Agrawal, K., Mittal, A., Pudi, V.: CoTRADE: scalable, semi-supervised extraction of structured information from scientific literature, pp. 11–20. Association for Computational Linguistics (2019)
Kim, S.N., Medelyan, O., Kan, M.-Y., Baldwin, T.: SemEval-2010 task 5: automatic keyphrase extraction from scientifific articles. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval 2010, Stroudsburg, PA, USA, pp. 21–26. Association for Computational Linguistics (2010)
Gollapalli, S.D., Caragea, C.: Extracting keyphrases from research papers using citation networks. In: Proceedings of the Twenty-Eighth AAAI Conference on Artifificial Intelligence, AAAI 2014, pp. 1629–1635. AAAI Press (2014)
Jaidka, K., Chandrasekaran, M.K., Rustagi, S., Kan, M.-Y.: Insights from CL-SciSumm 2016: the faceted scientific document summarization shared task. Int. J. Digit. Libr. 19(2), 163–171 (2016)
Agrawal, K., Mittal, A., Pudi, V.: Scalable, semi-supervised extraction of structured information from scientifific literature (2019)
Drugman, T., Pylkkonen, J., Kneser, R.: Active and semi-supervised learning in ASR: benefits on the acoustic and language models (2019)
Arora, C., Sabetzadeh, M., Nejati, S., Briand, L.: An active learning approach for improving the accuracy of automated domain model extraction (2019)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data, ACL 2009 (2009)
Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: Proceedings of EMNLP-CoNLL, pp. 455–465 (2012)
Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING, pp. 2335–2344 (2014)
Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks (2015)
Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances (2016)
Wang, G., Zhang, W., Wang, R., Zhou, Y.: Label-free distant supervision for relation extraction via knowledge graph embedding (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, A., Wang, X., Wang, W., Zhang, A., Li, B. (2019). A Survey of Relation Extraction of Knowledge Graphs. In: Song, J., Zhu, X. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11809. Springer, Cham. https://doi.org/10.1007/978-3-030-33982-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-33982-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33981-4
Online ISBN: 978-3-030-33982-1
eBook Packages: Computer ScienceComputer Science (R0)