Chinese relation extraction for constructing satellite frequency and orbit knowledge graph: A survey^✩

Abstract

As Satellite Frequency and Orbit (SFO) constitute scarce natural resources, constructing a Satellite Frequency and Orbit Knowledge Graph (SFO-KG) becomes crucial for optimizing their utilization. In the process of building the SFO-KG from Chinese unstructured data, extracting Chinese entity relations is the fundamental step. Although Relation Extraction (RE) methods in the English field have been extensively studied and developed earlier than their Chinese counterparts, their direct application to Chinese texts faces significant challenges due to linguistic distinctions such as unique grammar, pictographic characters, and prevalent polysemy. The absence of comprehensive reviews on Chinese RE research progress necessitates a systematic investigation. A thorough review of Chinese RE has been conducted from four methodological approaches: pipeline RE, joint entity- relation extraction, open domain RE, and multimodal RE techniques. In addition, we further analyze the essential research infrastructure, including specialized datasets, evaluation benchmarks, and competitions within Chinese RE research. Finally, the current research challenges and development trends in the field of Chinese RE were summarized and analyzed from the perspectives of ecological construction methods for datasets, open domain RE, N-ary RE, and RE based on large language models. This comprehensive review aims to facilitate SFO-KG construction and its practical applications in SFO resource management.

Keywords

Relation extraction / Information extraction / Distant supervision / Parsing tree / Joint entity-relation extraction

References

[1] R. Gaizauskas, Y. Wilks, Information extraction: beyond document retrieval, J. Doc. 54 (1) (1998) 70-105.

[2] B. Li, Y. Chen, S. Yu, Research on information extraction: a survey, Comput. Eng. Appl. 39 (10) (2003) 1-5.

[3] A. Kuznetsov, P. Braslavski, V. Ivanov, Family matters: company relations extrac- tion from Wikipedia,in:Knowledge Engineering and Semantic Web: 7th Interna- tional Conference, Springer, 2016, pp. 81-92.

[4] L.F. Rau, Extracting company names from text, in: Proceedings the Seventh IEEE Conference on Artificial Intelligence Application, IEEE Computer Society, 1991, pp. 29-30.

[5] R. Grishman, B.M. Sundheim, Message understanding conference-6: a brief his- tory,in: The 16th International Conference on Computational Linguistics, 1996, pp. 466-471.

[6] W. Li, Q. Wang, J. Wu, Z. Yu, Piecewise convolutional neural networks with posi- tion attention and similar bag attention for distant supervision relation extraction, Appl. Intell. 52 (2022) 4599-4609.

[7] G. Bekoulis, J. Deleu, T. Demeester, C. Develder, Joint entity recognition and re- lation extraction as a multi-head selection problem, Expert Syst. Appl. 114 (2018) 34-45.

[8] Y. Chen, D. Zheng, T. Zhao, Chinese relation extraction based on deep belief nets, J. Softw. 23 (10) (2012) 2572-2585.

[9] R. Takanobu, T. Zhang, J. Liu, M. Huang,A hierarchical framework for relation extraction with reinforcement learning, in:Proceedings of the AAAI Conference on Artificial Intelligence, 2019, pp. 7072-7079.

[10] J. Feng, M. Huang, L. Zhao, Y. Yang, X. Zhu,Reinforcement learning for relation classification from noisy data, in:Proceedings of the AAAI Conference on Artificial Intelligence, 2018, pp. 5779-5786.

[11] X. Zeng, S. He, K. Liu, J. Zhao,Large scaled relation extraction with reinforcement learning, in:Proceedings of the AAAI Conference on Artificial Intelligence, 2018, pp. 5658-5665.

[12] B. Wang, K. Wang, C. Wang, R. Liu, W. Liu, H. Huang, Chinese entity relationship extraction for journalism, Telecommun. Eng. 1 (2021) 50-57.

[13] B. Deng, X. Fan, L. Yang, Entity relation extraction method using semantic pattern, Comput. Eng. 33 (10) (2007) 212-214.

[14] W. Zirui, M. Fang, J. Libiao, Review of Chinese entity relation extraction, in: 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE), IEEE, 2017, pp. 633-637.

[15] W. Wenya, C. Yufeng, X. Jinan, Z. Yujie, Review of Chinese entity relation extrac- tion, Comput. Model. 33 (08) (2018) 21-27.

[16] C. Cheng, Relationship extraction tactics of Chinese entity based on formal con- cept connectivity distance, in: 2013 International Conference on Asian Language Processing, IEEE, 2013, pp. 25-28.

[17] W. Deng, Y. Liu, Chinese triple extraction based on bert model, in: 2021 15th Inter- national Conference on Ubiquitous Information Management and Communication (IMCOM), IEEE, 2021, pp. 1-5.

[18] M. Xie, J. Wang, X. Zhang, Joint model of triple relation extraction with label em- beddings, in: 2021 International Conference on Asian Language Processing (IALP), IEEE, 2021, pp. 105-110.

[19] G.-T. Huang, H.-H. Yao, A system for Chinese question answering, in: Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003), IEEE, 2003, pp. 458-461.

[20] C. Xie, H. Yu, J. Wang, Y. N. University, I. E. Department, Research on conceptual relation extraction based on ontology learning method, Sci. Technol. S (5) (2017) 33-36.

[21] S. Zhu,Research on reaction detection and recognition system based ontology in the field of electronic, Ph.D. thesis, Xidian University, 2011.

[22] Y. Ha,Entity and relation acquisition of a domain ontology, Ph.D. thesis, Beijing University of Technology, 2014.

[23] B. Xi, L. Qian, G. Zhou, Q. Zhu, P. Qian, The application of combined linguistic features in semantic relation extraction, J. Chin. Inf. Process. 22 (3) (2008) 44-49.

[24] S. Zhang, J. Wen, Y. Qin, X. Wang, Y. Zhong, The research and application about the information extraction in Chinese domain, in: 2006 8th International Conference on Signal Processing, IEEE, 2006, https://doi.org/10.1109/ICOSP.2006.345822.

[25] J. Wu, J. Zhou, An approach of chunk parsing and entity relation extracting to Chi- nese based on conditional random fields model, in: 2008 Eighth International Con- ference on Intelligent Systems Design and Applications, IEEE, 2008, pp. 489-494.

[26] S. Zhang, J. Wen, X. Wang, L. Li, Automatic entity relation extraction based on maximum entropy, in: Sixth International Conference on Intelligent Systems Design and Applications, IEEE, 2006, pp. 540-544.

[27] S. Zhang, S. Zhang, G. Gao, Automatic entity relation extraction based on condi- tional random fields, in: 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, IEEE, 2008, pp. 286-290.

[28] W. Che, T. Liu, S. Li, Automatic entity relation extraction, J. Chin. Inf. Process. 19 (2) (2005) 1-6.

[29] R. Huang, L. Sun, Y. Feng, Study of kernel-based methods for Chinese relation extraction, in: Information Retrieval Technology: 4th Asia Information Retrieval Symposium (AIRS 2008), Springer, 2008, pp. 598-604.

[30] R. Lin, J. Chen, X. Yang, H. Xu, Research on mixed model-based Chinese relation extraction, in: 2010 3rd International Conference on Computer Science and Infor- mation Technology, IEEE, 2010, pp. 687-691.

[31] D. Zeng, K. Liu, S. Lai, G. Zhou, J. Zhao, Relation classification via convolutional deep neural network, in: Proceedings of COLING 2014, the 25th International Con- ference on Computational Linguistics, 2014, pp. 2335-2344.

[32] P. Wang, C. Zhang, D. Wang, S. Zhang, J. Wang, X. Wang, L. Huang, Relation extraction for knowledge graph generation in the agriculture domain: a case study on soybean pests and disease, Appl. Eng. Agric. 39 (2) (2023) 215-224.

[33] Y. Zhang, Relation extraction in Chinese using attention-based bidirectional long short-term memory networks, PeerJ Comput. Sci. 9 (2023) e1509.

[34] Y. He, Z. Li, Z. Dou, Chinese satellite frequency and orbit entity relation extraction method based on dynamic integrated learning, Digit. Commun. Netw. 11 (3) (2025) 787-794.

[35] P. Feng, N. Su, J. Xing, J. Bian, D. Ouyang, A study of entity relationship extraction algorithms based on symmetric interaction between data, models, and inference algorithms, Appl. Sci. (Basel) 14 (3) (2024) 1058.

[36] A. Wang, L. Li, X. Wu, J. Zhu, S. Yu, X. Chen, J. Li, H. Zhu, Entity relation extraction in the medical domain: based on data augmentation, Ann. Transl. Med. 10 (19) (2022) 1061.

[37] C. Wei, Y. Chen, K. Wang, Y. Qin, R. Huang, Q. Zheng, Apre: annotation-aware prompt-tuning for relation extraction, Neural Process. Lett. 56 (62) (2024), https://doi.org/10.1007/s11063-024-11437-y.

[38] X. Han, Y. Zhang, W. Zhang, T. Huang, An attention-based model using character composition of entities in Chinese relation extraction, Information 11 (2) (2020) 79.

[39] Z. Qin, E. Liu, S. Zhang, Y. Chang, L. Yan, A relational extraction approach based on multiple embedding representations and multi-head self-attention, J. Intell. Fuzzy Syst. 46 (2024) 7093-7107.

[40] Z. Zhang, T. Zhou, Y. Zhang, Y. Pang, Attention-based deep residual learning net- work for entity relation extraction in Chinese emrs, BMC Med. Inform. Decis. Mak. 19 (2019) 171-177.

[41] B. Xu, S. Li, Z. Zhang, T. Liao, Bert-pagg: a Chinese relationship extraction model fusing pagg and entity location information, PeerJ Comput. Sci. 9 (2023) e1470.

[42] J. Qiu, L. Liao, P. Li, Relation extraction from Chinese news web documents based on weakly supervised learning, in: 2009 International Conference on Intelligent Networking and Collaborative Systems, IEEE, 2009, pp. 219-225.

[43] H. Wan, M.-F. Moens, W. Luyten, X. Zhou, Q. Mei, L. Liu, J. Tang, Extracting relations from traditional Chinese medicine literature via heterogeneous entity net- works, J. Am. Med. Inform. Assoc. 23 (2) (2016) 356-365.

[44] C. Yang, D. Xiao, Y. Luo, B. Li, X. Zhao, H. Zhang, A hybrid method based on semi-supervised learning for relation extraction in Chinese emrs, BMC Med. Inform. Decis. Mak. 22 (1) (2022) 169.

[45] D. Li, Y. Zhang, D. Li, D. Lin, Review of entity relation extraction methods, J. Com- put. Res. Dev. 57 (7) (2020) 1424-1448.

[46] J. Wang, Y. Jing, Y. Teng, Q. Li, A novel clustering algorithm for unsupervised relation extraction, in: Seventh International Conference on Digital Information Management (ICDIM 2012), IEEE, 2012, pp. 16-21.

[47] Y. Liu, M. Li, H. Liu, J. Cheng, Y. Fu, Research of unsupervised entity relation extraction, J. Comput. 30 (1) (2019) 31-41.

[48] M. Mintz, S. Bills, R. Snow, D. Jurafsky,Distant supervision for relation extraction without labeled data, in:Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2009, pp. 1003-1011.

[49] S. Riedel, L. Yao, A. McCallum, Modeling relations and their mentions without la- beled text, in: Proceedings of the 2010th European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, 2010, pp. 148-163.

[50] J. Liu, W. Rui, L. Zhang, Y. Jia, Social relation extraction with improved distant supervised and word embedding features, in: 2016 IEEE International Conference on Big Data Analysis (ICBDA), IEEE, 2016, pp. 1-5.

[51] Z. Chen, Y. Tian, L. xi Wang, S. Jiang,A distantly-supervised relation extraction method based on selective gate and noise correction, in:China National Conference on Chinese Computational Linguistics, 2023, pp. 159-174.

[52] Q. Zhao, D. Xu, J. Li, L. Zhao, F.A. Rajput, Knowledge guided distance supervision for biomedical relation extraction in Chinese electronic medical records, Expert Syst. Appl. 204 (2022) 117606.

[53] T. Liang, Y. Liu, X. Liu, H. Zhang, G. Sharma, M. Guo, Distantly-supervised long- tailed relation extraction using constraint graphs, IEEE Trans. Knowl. Data Eng. 35 (7) (2022) 6852-6865.

[54] M. Miwa, M. Bansal,End-to-end relation extraction using lstms on sequences and tree structures, in:Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016, pp. 1105-1116.

[55] X. Shi, Y. Yi, Y. Xiong, B. Tang, Q. Chen, X. Wang, Z. Ji, Y. Zhang, H. Xu, Extracting entities with attributes in clinical text via joint deep learning, J. Am. Med. Inform. Assoc. 26 (12) (2019) 1584-1591.

[56] W. Liu, M. Yin, J. Zhang, L. Cui, A joint entity relation extraction model based on relation semantic template automatically constructed, Comput. Mater. Continua 78 (1) (2024) 975-997.

[57] Z. Hu, H. Yin, G. Xu, Y. Zhai, D. Pan, Y. Liang,An empirical study on joint entities- relations extraction of Chinese text based on bert, in:Proceedings of the 2020 12th International Conference on Machine Learning and Computing, 2020, pp. 473-478.

[58] S. Zheng, F. Wang, H. Bao, Y. Hao, P. Zhou, B. Xu,Joint extraction of entities and relations based on a novel tagging scheme, in:Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017, pp. 1227-1236.

[59] X. Liu, Y. Liu, H. Wu, Q. Guan, A tag based joint extraction model for Chinese medical text, Comput. Biol. Chem. 93 (2021) 107508.

[60] R. Li, D. Li, J. Yang, F. Xiang, H. Ren, S. Jiang, L. Zhang, Joint extraction of entities and relations via an entity correlated attention neural model, Inf. Sci. 581 (2021) 179-193.

[61] M. Liu, Y. Zhang, W. Li, D. Ji, Joint model of entity recognition and relation ex- traction with self-attention mechanism, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 19 (4) (2020) 1-19.

[62] K. Ding, S. Liu, Y. Zhang, H. Zhang, X. Zhang, T. Wu, X. Zhou, A knowledge- enriched and span-based network for joint entity and relation extraction, Comput. Mater. Continua 68 (1) (2021) 377-389.

[63] J. Zhang, Y. Zhang, M. Wang, H. Yongjian, Joint extraction of Chinese entity rela- tions based on graph convolutional neural network, Comput. Eng. 47 (12) (2020) 103-111.

[64] Q. Zhao, T. Gao, N. Guo, A novel Chinese relation extraction method using poly- semy rethinking mechanism, Appl. Intell. 53 (7) (2023) 7665-7676.

[65] M. Tuo, W. Yang, F. Wei, Q. Dai, A novel Chinese overlapping entity relation extrac- tion model using word-label based on cascade binary tagging, Electronics 12 (4) (2023) 1013.

[66] M. Banko, O. Etzioni,The tradeoffs between open and traditional relation extrac- tion, in:Proceedings of ACL-08: HLT, 2008, pp. 28-36.

[67] B. Qin, A. Liu, T. Liu, Unsupervised Chinese open entity relation extraction, J. Com- put. Res. Dev. 52 (5) (2015) 1029-1035.

[68] J. Xu, L. Gan, L. Deng, J. Wang, Z. Yan, Dependency parsing based Chinese open relation extraction, in: 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), IEEE, 2015, pp. 552-556.

[69] J. Xu, L. Gan, Z. Yan, Q. Wu, Y. Jia, Open relation extraction from Chinese mi- croblog text, in: 2016 IEEE First International Conference on Data Science in Cy- berspace (DSC), IEEE, 2016, pp. 673-677.

[70] L. Meng, B. Wu, Core discovery and relation extraction in organization profiling, in: 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), IEEE, 2017, pp. 219-222.

[71] S. Jia, S. E, M. Li, Y. Xiang, Chinese open relation extraction and knowledge base establishment, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 17 (3) (2018) 1-22.

[72] S. Qi, L. Zheng, F. Shang, Dependency parsing-based entity relation extraction over Chinese complex text, ACM Trans. Asian Low-Resour. Lang. Inf. Process. 20 (4) (2021) 1-34.

[73] C. Wang, X. He, A. Zhou, Open relation extraction for Chinese noun phrases, IEEE Trans. Knowl. Data Eng. 33 (6) (2019) 2693-2708.

[74] Y. Yang, Q. Lu, T. Zhao, A clustering based approach for domain relevant relation extraction, in: 2008 International Conference on Natural Language Processing and Knowledge Engineering, IEEE, 2008, pp. 1-8.

[75] S. Zheng, J. Yang, X. Lin, J. Gu, Dependency tree based Chinese relation extraction over web data, in: 2012 Seventh International Conference on Knowledge, Informa- tion and Creativity Support Systems, IEEE, 2012, pp. 104-110.

[76] M. Wang, L. Li, F. Huang, Semi-supervised Chinese open entity relation extraction, in: 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems, IEEE, 2014, pp. 415-420.

[77] X. Wu, B. Wu, The crfs-based Chinese open entity relation extraction, in: 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), IEEE, 2017, pp. 405-411.

[78] C. Wen, X. Jia, T. Chen, Improving extraction of Chinese open relations using pre- trained language model and knowledge enhancement, Data Intell. 5 (4) (2023) 962-989.

[79] Z. Li, N. Ding, Z. Liu, H. Zheng, Y. Shen,Chinese relation extraction with multi- grained information and external linguistic knowledge, in:Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 4377-4386.

[80] Q. Zhang, M. Wu, P. Lv, M. Zhang, L. Lv, Research on Chinese medical entity re- lation extraction based on syntactic dependency structure information, Appl. Sci. (Basel) 12 (19) (2022) 9781.

[81] Q. Lai, S. Ding, J. Gong, S. Liu, et al., A Chinese multi-modal relation extraction model for Internet security of finance, in: 2022 52nd Annual IEEE/IFIP Inter- national Conference on Dependable Systems and Networks Workshops (DSN-W), IEEE, 2022, pp. 123-128.

[82] L. Chen, S. Wang, W. Fan, J. Sun, S. Naoi, Beyond human recognition: a cnn-based framework for handwritten character recognition, in: 2015 3rd IAPR Asian Confer- ence on Pattern Recognition (ACPR), IEEE, 2015, pp. 695-699.

[83] Z. Li, N. Teng, M. Jin, H. Lu, Building efficient cnn architecture for offline handwrit- ten Chinese character recognition, Int. J. Doc. Anal. Recognit. 21 (2018) 233-240.

[84] X. Zhang, F. Yin, Y. Zhang, C. Liu, Y. Bengio, Drawing and recognizing Chinese characters with recurrent neural network, IEEE Trans. Pattern Anal. Mach. Intell. 40 (4) (2017) 849-862.

[85] LDC, Ace 2004 multilingual training corpus, https://catalog.ldc.upenn.edu/LDC2005T09, 2005. (Accessed 2 June 2023).

[86] LDC, Ace 2005 multilingual training corpus, https://catalog.ldc.upenn.edu/LDC2006T06, 2006. (Accessed 2 June 2023).

[87] T. Guan, H. Zan, X. Zhou, H. Xu, K. Zhang, Cmeie: construction and evaluation of Chinese medical information extraction dataset,in:Natural Language Processing and Chinese Computing: 9th CCF International Conference (NLPCC 2020), 2020, pp. 270-282.

[88] CHIP 2020, Extracting entity relationships in Chinese medical text, http://www.cips-chip.org.cn/2020/eval2, 2020. (Accessed 2 June 2023).

[89] S. Li, W. He, Y. Shi, W. Jiang, H. Liang, Y. Jiang, Y. Zhang, Y. Lyu, Y. Zhu, Duie: a large-scale Chinese dataset for information extraction,in:Natural Language Pro- cessing and Chinese Computing: 8th CCF International Conference (NLPCC 2019), Springer, 2019, pp. 791-800.

[90] Qianyan,Duie2.0 Chinese relational extraction dataset, https://www.luge.ai/#/luge/dataDetail?id=5, 2020. (Accessed 22 July 2023).

[91] J. Sun, X. Gu, Y. Li, W. Xu, Chinese entity relation extraction algorithms based on coae2016 datasets, J. Shandong Univ. Nat. Sci. 52 (9) (2017) 7-12.

[92] H. Wang, Z. He, T. Zhu, H. Shao, W. Chen, M. Zhang, Ccks 2019 shared task on inter-personal relationship extraction, arXiv preprint, arXiv:1908.11337.

[93] H. Wang, Z. He, J. Ma, W. Chen, M. Zhang, Ipre: a dataset for inter-personal rela- tionship extraction,in:Natural Language Processing and Chinese Computing: 8th CCF International Conference (NLPCC 2019), Springer, 2019, pp. 103-115.

[94] J. Xu, J. Wen, X. Sun,Q. Su, A discourse-level named entity recognition and relation extraction dataset for Chinese literature text, arXiv preprint, arXiv:1711.07010.

[95] J. Chen, A. Wang, J. Chen, Y. Xiao, Z. Chu, J. Liu, J. Liang, W. Wang, Cn- probase: a data-driven approach for large-scale Chinese taxonomy construction, in: 2019 IEEE 35th International Conference on Data Engineering (ICDE), IEEE, 2019, pp. 1706-1709.

[96] X. Niu, X. Sun, H. Wang, S. Rong, G. Qi, Y. Yu, Zhishi.me-weaving Chinese link- ing open data, in: The Semantic Web-ISWC 2011: 10 th International Semantic Web Conference, Proceedings, Part II 10, Bonn, Germany, October 23-27, 2011, Springer, 2011, pp. 205-220.

[97] D. Fang, Z. Chen, Y. Huang, Y. Heng, An entity relation extraction method based on automatic data set construction, Commun. Technol. 54 (8) (2021) 1862-1868.

[98] L. Ying, H. Xiao-yan, W. Yong, N-ary Chinese open entity-relation extraction, Com- put. Sci. 44 (Z6) (2017) 80-83.

[99] L. Luo, J. Ning, Y. Zhao, Z. Wang, Z. Ding, P. Chen, W. Fu, Q. Han, G. Xu, Y. Qiu, D. Pan, J. Li, H. Li, W. Feng, S. Tu, Y. Liu, Z. Yang, J. Wang, Y. Sun, L. Hongfei, Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks, J. Am. Med. Inform. Assoc. 31 (9) (2024) 1865-1874.

[100] X. Wei, X. Cui, N. Cheng, X. Wang, X. Zhang, S. Huang, P. Xie, J. Xu, Y. Chen, M. Zhang, et al., ChatIE: zero-shot information extraction via chatting with ChatGPT, arXiv preprint, arXiv:2302.10205.

[101] W. Zhao, Q. Chen, J. You, LlmRe: a zero-shot entity relation extraction method based on the large language model,in: Proceedings of the 2023 7th Interna- tional Conference on Electronic Information Technology and Computer Engineer- ing, 2023, pp. 475-480.