双向循环进化的实体链接及知识推理框架

doi:10.12305/j.issn.1001-506X.2022.09.22

Abstract

Abstract:

To improve the accuracy of entity linking task in natural language processing in a systematic and intelligent way, and to solve the problems of hybrid graph noise and sparse relationship, a bidirectional cyclic evolutionary framework of entity linking and knowledge reasoning based on the knowledge & data two-wheel driving idea of the third-generation artificial intelligence is proposed. The forward evolution module is designed based on the entity linking technology under knowledge graph, and the reverse evolution module is designed based on the knowledge reasoning and new technologies such as meta-learning. The bidirectional control process is cycled several times to realize self-iteration and intelligent upgrading of two tasks under weak supervision. Experiments show that, with the help of modular design, the framework can acquire domain-specific knowledge from domain-specific texts and rapid incremental update, which can effectively improve the efficiency of entity linking and knowledge reasoning. It also provides a new method for iterative upgrading of text analysis capability under small samples in various fields.

Key words: natural language processing, entity linking, knowledge reasoning, bidirectional control, modular design

CLC Number:

TP391.1

Haojun FENG, Li DUAN, Biying ZHANG, Haichao LIU. Bidirectional cyclic evolutionary framework of entity linking and knowledge reasoning[J]. Systems Engineering and Electronics, 2022, 44(9): 2878-2885.

Figures/Tables 9

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References 31

1	OLIVEIRA I L , FILETO R , SPECK R , et al. Towards holistic entity linking: survey and directions[J]. Information Systems, 2021, 95, 101624. doi: 10.1016/j.is.2020.101624
2	MYTHREI S , SINGARAVELAN S . Survey on entity linking for domain specific with heterogeneous information networks[J]. Informatologia, 2020, 53 (3/4): 173- 184.
3	马忠贵, 倪润宇, 余开航. 知识图谱的最新进展、关键技术和挑战[J]. 工程科学学报, 2020, 42 (10): 1254- 1266.
	MA Z G , NI R Y , YU K H . Recent advances, key technologies and challenges in knowledge graph[J]. Journal of Engineering Science, 2020, 42 (10): 1254- 1266.
4	官赛萍, 靳小龙, 贾岩涛, 等. 面向知识图谱的知识推理研究进展[J]. 软件学报, 2018, 29 (10): 2966- 2994.
	GUAN S P , JIN X L , JIA Y T , et al. Knowledge inference based on knowledge graph[J]. Journal of Software, 2018, 29 (10): 2966- 2994.
5	张仲伟, 曹雷, 陈希亮, 等. 基于神经网络的知识推理研究综述[J]. 计算机工程与应用, 2019, 55 (12): 8- 19.8-19, 36
	ZHANG Z W , CAO L , CHEN X L , et al. A review of knowledge reasoning based on neural network[J]. Computer Engineering and Applications, 2019, 55 (12): 8- 19.8-19, 36
6	漆桂林, 高桓, 吴天星. 知识图谱研究进展[J]. 情报工程, 2017, 3 (1): 4- 25.
	QI G L , GAO H , WU T X . Progress in knowledge graph[J]. Information Engineering, 2017, 3 (1): 4- 25.
7	刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53 (3): 582- 600.
	LIU Q , LI Y , DUAN H , et al. A review of knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53 (3): 582- 600.
8	张钹, 朱军, 苏航. 迈向第三代人工智能[J]. 中国科学: 信息科学, 2020, 50 (9): 1281- 1302.
	ZHANG B , ZHU J , SU H . Towards the third generation of artificial intelligence[J]. Science China: Information Science, 2020, 50 (9): 1281- 1302.
9	LI D , FU Z J , ZHENG Z Y . An entity linking model based on candidate features[J]. Social Network Analysis and Mining, 2021, 11 (1): 50. doi: 10.1007/s13278-021-00761-z
10	WANG Y T, LI Z X, YANG Q, et al. WebEL: improving entity linking with extra web contexts[C]//Proc. of the Web Information Systems Engineering, 2019: 507-522.
11	XIE T , BIN W U , JIA B , et al. Graph-ranking collective Chinese entity linking algorithm[J]. Frontiers of Computer Science, 2020, 14 (2): 291- 303. doi: 10.1007/s11704-018-7175-0
12	FENG H J, DUAN L, ZHANG B Y, et al. A collective entity linking method based on graph embedding algorithm[C]//Proc. of the 5th International Conference on Mechanical, Control and Computer Engineering, 2020: 1479-1482.
13	FINN V K , SHESTERNIKOVA O . A new variant of the genera-lized JSM-method for automatic support of scientific research[J]. Scientific and Technical Information Processing, 2017, 44 (5): 338- 344. doi: 10.3103/S0147688217050045
14	PINGLE A, PIPLAI A, MITTAL S, et al. RelExt: relation extraction using deep learning approaches for cybersecurity knowledge graph improvement[C]//Proc. of the IEEE/ACM International Conference on Advances in Social Networks Ana-lysis and Mining, 2020: 879-886.
15	DASGUPTA S, PIPLAI A, KOTAL A, et al. A comparative study of deep learning based named entity recognition algorithms for cybersecurity[C]//Proc. of the IEEE International Conference on Big Data, 2020: 2596-2604.
16	LIU X, ATHANASIOU C E, PADTURE N P, et al. Knowledge extraction and transfer in data-driven fracture mechanics[J]. Proceedings of the National Academy of Sciences, 2021, 118(23): e2104765118.
17	WU L F, CHEN Y, SHEN K, et al. Graph neural networks for natural language processing: a survey[EB/OL]. [2021-06-13]. https://arxiv.org/abs/2106.06090.
18	YUAN H, YU H Y, GUI S R, et al. Explainability in graph neural networks: a taxonomic survey[EB/OL]. [2021-06-13]. https://arxiv.org/abs/2012.15445.
19	SUN Y N , XU Y J , LI S . Knowledge-aware path: interpretable graph reasoning in proactive dialogue generation[J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28 (1): 1- 9.
20	HOSPEDALES T . Meta-learning in neural networks: a survey[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2021, 44 (9): 5149- 5169.
21	MONDAL A. A survey of reinforcement learning techniques: strategies, recent development, and future directions[EB/OL]. [2021-06-13]. https://arxiv.org/abs/2001.06921.
22	HUISMAN M , RIJN J , PLAAT A . A survey of deep meta-learning[J]. Artificial Intelligence Review, 2021, 54, 4483- 4541. doi: 10.1007/s10462-021-10004-4
23	GHOSH S , BEQUETTE B W . Process systems engineering and the human-in-the-loop: the smart control room[J]. Industrial and Engineering Chemistry Research, 2020, 59 (6): 2422- 2429. doi: 10.1021/acs.iecr.9b04739
24	刘全, 翟建伟, 章宗长, 等. 深度强化学习综述[J]. 计算机学报, 2018, 41 (1): 1- 27.
	LIU Q , ZHAI J W , ZHANG Z C , et al. A review of deep reinforcement learning[J]. Chinese Journal of Computers, 2018, 41 (1): 1- 27.
25	MAZYAVKINA N , SVIRIDOV S , IVANOV S , et al. Reinforcement learning for combinatorial optimization: a survey[J]. Computers & Operations Research, 2021, 134 (1): 105400.
26	GRONAUER S , DIEPOLD K . Multi-agent deep reinforcement learning: a survey[J]. Artificial Intelligence Review, 2022, 55 (2): 895- 943.
27	李晨溪, 曹雷, 张永亮, 等. 基于知识的深度强化学习研究综述[J]. 系统工程与电子技术, 2017, 39 (11): 2603- 2613.
	LI C X , CAO L , ZHANG Y L , et al. A review of knowledge-based deep reinforcement learning[J]. Systems Engineering and Electronics, 2017, 39 (11): 2603- 2613.
28	丁兆云, 贾焰. 微博数据挖掘研究综述[J]. 计算机研究与发展, 2014, 51 (4): 691- 706.
	DING Z Y , JIA Y . A review of microblog data mining[J]. Journal of Computer Research and Development, 2014, 51 (4): 691- 706.
29	BIZER C , LEHMANN J , KOBILAROV G , et al. DBpedia-a crystallization point for the web of data[J]. Web Semantics Science Services and Agents on the World Wide Web, 2009, 7 (3): 154- 165.
30	LIU Y, LI H, GARCIA-DURAN A, et al. MMKG: multi-modal knowledge graphs[C]//Proc. of the European Semantic Web Conference, 2019: 459-474.
31	WANG M , WANG H F , QI G L , et al. Richpedia: a large-scale, comprehensive multi-modal knowledge graph[J]. Big Data Research, 2020, 22 (10): 100159.

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Bidirectional cyclic evolutionary framework of entity linking and knowledge reasoning

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