Overview of Research on Knowledge Graph Completion

doi:10.3778/j.issn.1673-9418.2502028

Abstract

Abstract: Knowledge graphs have been widely applied in various fields and have significantly advanced the development of artificial intelligence tasks. However, knowledge graphs still face the challenge of incompleteness, which severely limits their effectiveness in downstream applications. Knowledge graph completion tasks aim to predict missing links in the graph to address this issue of incompleteness. This paper provides a systematic review of the research background of knowledge graphs and their completion techniques, highlighting their pivotal role in artificial intelligence and natural language processing. Based on different information sources, existing completion methods are categorized into three types: structure-based, text-based, and hybrid methods. It introduces representative results for each category, compares their advantages and disadvantages, and summarizes their applicable scenarios, revealing the current technological development and evolutionary trends. This paper also explores advances in multilingual knowledge graph completion, focusing on key techniques such as cross-lingual entity alignment, and emphasizes the importance of cross-lingual knowledge sharing and unified modeling. Finally, it analyzes the challenges of knowledge graph completion in areas such as knowledge fusion and mining, and outlines future research directions.

Key words: knowledge graph, knowledge graph completion, graph neural networks, large language models, multilingual knowledge graph completion

摘要： 知识图谱已在众多领域得到广泛应用，显著推进了人工智能相关任务的发展。然而，知识图谱在实际应用中仍面临知识不完备的挑战，这一挑战严重限制了知识图谱在下游任务中的应用效果。知识图谱补全任务能够预测知识图谱中缺失的连接，以解决知识不完备的问题。系统梳理了知识图谱及其补全技术的研究背景，明确了其在人工智能与自然语言处理等领域的关键作用。根据信息来源的不同，将现有补全方法划分为基于结构信息、基于文本信息以及融合结构与文本信息等类型，并对各类方法的代表性成果进行了介绍、优缺点比较及适用场景的归纳，揭示了当前技术的发展脉络与演进趋势。关注多语言知识图谱补全的研究进展，探讨了跨语言实体对齐等关键技术，强调了跨语言知识共享与统一建模的重要性。分析了知识图谱补全在知识融合、知识挖掘等方面的挑战，并展望了未来可能的研究趋势。

关键词: 知识图谱, 知识图谱补全, 图神经网络, 大语言模型, 多语言知识图谱补全

Anggeluma, WANG Siriguleng, SI Qintu. Overview of Research on Knowledge Graph Completion[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(9): 2302-2318.

昂格鲁玛, 王斯日古楞, 斯琴图. 知识图谱补全研究综述[J]. 计算机科学与探索, 2025, 19(9): 2302-2318.

References

[1] SUCHANEK F M, KASNECI G, WEIKUM G. Yago: a core of semantic knowledge[C]//Proceedings of the 16th International Conference on World Wide Web. New York: ACM, 2007: 697-706.
[2] BOLLACKER K, EVANS C, PARITOSH P, et al. Freebase: a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2008: 1247-1250.
[3] AUER S, BIZER C, KOBILAROV G, et al. DBpedia: a nucleus for a web of open data[C]//Proceedings of the 6th International Semantic Web Conference, the 2nd Asian Semantic Web Conference. Berlin, Heidelberg: Springer, 2007: 722-735.
[4] VRANDE?I? D, KR?TZSCH M. Wikidata[J]. Communications of the ACM, 2014, 57(10): 78-85.
[5] WANG Z, LI J, WANG Z, et al. XLore: a large-scale English-Chinese bilingual knowledge graph[C]//Proceedings of the ISWC 2013 Posters & Demonstrations Track, 2013: 121-124.
[6] CHEN H J, HU N, QI G L, et al. OpenKG chain: a blockchain infrastructure for open knowledge graphs[J]. Data Intelligence, 2021, 3(2): 205-227.
[7] XU B, LIANG J Q, XIE C H, et al. CN-DBpedia2: an extraction and verification framework for enriching Chinese encyclopedia knowledge base[J]. Data Intelligence, 2019, 1(3): 271-288.
[8] 杜雪盈, 刘名威, 沈立炜, 等. 面向链接预测的知识图谱表示学习方法综述[J]. 软件学报, 2024, 35(1): 87-117.
DU X Y, LIU M W, SHEN L W, et al. Survey on representation learning methods of knowledge graph for link prediction[J]. Journal of Software, 2024, 35(1): 87-117.
[9] 马恒志, 钱育蓉, 冷洪勇, 等. 知识图谱嵌入研究进展综述[J]. 计算机工程, 2025, 51(2): 18-34.
MA H Z, QIAN Y R, LENG H Y, et al. Review of research progress on knowledge graph embedding[J]. Computer Engineering, 2025, 51(2): 18-34.
[10] 于强, 朱颖, 冷根, 等. 面向知识图谱的知识补全方法综述[J]. 微电子学与计算机, 2025, 42(6): 1-14.
YU Q, ZHU Y, LENG G, et al. Survey of knowledge completion methods for knowledge graphs[J]. Microelectronics & Computer, 2025, 42(6): 1-14.
[11] 吴越, 孙海春. 基于图神经网络的知识图谱补全研究综述[J]. 数据分析与知识发现, 2024, 8(3): 10-28.
WU Y, SUN H C. An overview of research on knowledge graph completion based on graph neural network[J]. Data Analysis and Knowledge Discovery, 2024, 8(3): 10-28.
[12] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating embeddings for modeling multi-relational data[C]//Advances?in?Neural?Information?Processing?Systems?26, 2013: 2787-2795.
[13] YAO L, MAO C, LUO Y. KG-BERT: BERT for knowledge graph completion[EB/OL]. [2024-12-20]. https://arxiv.org/abs/1909.03193.
[14] WANG Z, ZHANG J W, FENG J L, et al. Knowledge graph embedding by translating on hyperplanes[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2014, 28(1): 1112-1119.
[15] LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2015, 29(1): 2181-2187.
[16] JI G L, HE S Z, XU L H, et al. Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2015: 687-696.
[17] XIAO H, HUANG M, HAO Y, et al. TransA: an adaptive approach for knowledge graph embedding[EB/OL]. [2024-12-20]. https://arxiv.org/abs/1509.05490.
[18] YANG B, YIH W, HE X D, et al. Embedding entities and relations for learning and inference in knowledge bases[EB/OL]. [2024-12-20]. https://arxiv.org/abs/1412.6575.
[19] NICKEL M, TRESP V, KRIEGEL H P. A three-way model for collective learning on multi-relational data[C]//Proceedings of the 28th International Conference on Machine Learning, 2011: 809-816.
[20] TROUILLON T, WELBL J, RIEDEL S, et al. Complex embeddings for simple link prediction[C]//Proceedings of the 33rd International Conference on Machine Learning, 2016: 2071-2080.
[21] DETTMERS T, MINERVINI P, STENETORP P, et al. Convolutional 2D knowledge graph embeddings[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 1811-1818.
[22] NGUYEN D Q, NGUYEN T D, NGUYEN D Q, et al. A novel embedding model for knowledge base completion based on convolutional neural network[EB/OL]. [2024-12-20]. https://arxiv.org/abs/1712.02121.
[23] PENG H L, WU Y. A dynamic convolutional network-based model for knowledge graph completion[J]. Information, 2022, 13(3): 133.
[24] SHANG C, TANG Y, HUANG J, et al. End-to-end structure-aware convolutional networks for knowledge base completion[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 3060-3067.
[25] VASHISHTH S, SANYAL S, NITIN V, et al. Composition-based multi-relational graph convolutional networks[EB/OL]. [2024-12-20]. https://arxiv.org/abs/1911.03082.
[26] DEGRAEVE V, VANDEWIELE G, ONGENAE F, et al. R-GCN: the R could stand for random[EB/OL]. [2024-12-20]. https://arxiv.org/abs/2203.02424.
[27] NATHANI D, CHAUHAN J, SHARMA C, et al. Learning attention-based embeddings for relation prediction in knowl-edge graphs[EB/OL]. [2024-12-20]. https://arxiv.org/abs/1906. 01195.
[28] 马浩凯, 祁云嵩, 吴宇斌. 解纠缠邻域信息聚合的知识图谱补全方法[J]. 计算机应用研究, 2024, 41(3): 772-778.
MA H K, QI Y S, WU Y B. Knowledge graph completion method for disentangled neighborhood information aggregation[J]. Application Research of Computers, 2024, 41(3): 772-778.
[29] ZHANG S C, ZHANG J N, SONG X, et al. PaGE-Link: path-based graph neural network explanation for heterogeneous link prediction[C]//Proceedings of the ACM Web Conference 2023. New York: ACM, 2023: 3784-3793.
[30] CHANG H, YE J N, LOPEZ-AVILA A, et al. Path-based explanation for knowledge graph completion[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM, 2024: 231-242.
[31] 翟社平, 杨晴, 黄妍, 等. 融合有向关系与关系路径的层次注意力的知识图谱补全[J]. 计算机应用, 2025, 45(4): 1148-1156.
ZHAI S P, YANG Q, HUANG Y, et al. Knowledge graph completion with hierarchical attention fusing directed relationships and relational paths[J]. Journal of Computer Applications, 2025, 45(4): 1148-1156.
[32] 那宇嘉, 谢珺, 杨海洋, 等. 融合上下文的知识图谱补全方法[J]. 山东大学学报(理学版), 2023, 58(9): 71-80.
NA Y J, XIE J, YANG H Y, et al. Context fusion-based knowledge graph completion[J]. Journal of Shandong University (Natural Science), 2023, 58(9): 71-80.
[33] SCARSELLI F, GORI M, TSOI A C, et al. The graph neural network model[J]. IEEE Transactions on Neural Networks, 2009, 20(1): 61-80.
[34] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. [2024-12-20].https://arxiv.org/abs/1609.02907.
[35] VELI?KOVI? P, CUCURULL G, CASANOVA A, et al. Graph attention networks[EB/OL]. [2024-12-20]. https://arxiv.org/abs/1710.10903.
[36] WANG L, ZHAO W, WEI Z Y, et al. SimKGC: simple contrastive knowledge graph completion with pre-trained language models[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2022: 4281-4294.
[37] PENG B, LIANG S, ISLAM M, et al. Bi-Link: bridging inductive link predictions from text via contrastive learning of transformers and prompts[EB/OL]. [2024-12-20]. https://arxiv.org/abs/2210.14463.
[38] XIE X, ZHANG N Y, LI Z B, et al. From discrimination to generation: knowledge graph completion with generative transformer[C]//Companion of the Web Conference 2022. New York: ACM, 2022: 162-165.
[39] JIANG P, AGARWAL S, JIN B, et al. Text-augmented open knowledge graph completion via pre-trained language models[EB/OL]. [2024-11-12]. https://arxiv.org/abs/2305.15597.
[40] ZHU Y Q, WANG X H, CHEN J, et al. LLMs for knowledge graph construction and reasoning: recent capabilities and future opportunities[J]. World Wide Web, 2024, 27(5): 58.
[41] WEI Y, HUANG Q, KWOK J T, et al. KICGPT: large language model with knowledge in context for knowledge graph completion[EB/OL]. [2024-11-12]. https://arxiv.org/abs/2402.02389.
[42] YANG R, ZHU J H, MAN J P, et al. Enhancing text-based knowledge graph completion with zero-shot large language models: a focus on semantic enhancement[J]. Knowledge-Based Systems, 2024, 300: 112155.
[43] XU D, ZHANG Z, LIN Z, et al. Multi-perspective improvement of knowledge graph completion with large language models[EB/OL]. [2024-11-12]. https://arxiv.org/abs/2403.01972.
[44] YAO L, PENG J Z, MAO C S, et al. Exploring large language models for knowledge graph completion[C]//Proceedings of the 2025 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2025: 1-5.
[45] LIU Y, TIAN X B, SUN Z Q, et al. Finetuning generative large language models with discrimination instructions for knowledge graph completion[C]//Proceedings of the 23rd International Semantic Web Conference. Cham: Springer, 2024: 199-217.
[46] ACHIAM J, ADLER S, AGARWAL S, et al. GPT-4 technical report[EB/OL]. [2024-11-12]. https://arxiv.org/abs/2303.08774.
[47] AN J F, DING W Z, LIN C. ChatGPT: tackle the growing carbon footprint of generative AI[J]. Nature, 2023, 615(7953): 586.
[48] YU D, ZHU C, YANG Y, et al. JAKET: joint pre-training of knowledge graph and language understanding[EB/OL]. [2024-11-12]. https://arxiv.org/abs/2010.00796.
[49] 韩道军, 李云松, 张俊涛, 等. 一种融合实体描述和拓扑结构的知识图谱补全方法[J]. 计算机科学, 2025, 52(5): 260-269.
HAN D J, LI Y S, ZHANG J T, et al. Knowledge graph completion method fusing entity descriptions and topological structure[J]. Computer Science, 2025, 52(5): 260-269.
[50] 杨荣泰, 邵玉斌, 杜庆治, 等. 基于子图结构语义增强的少样本知识图谱补全[J]. 北京邮电大学学报, 2024, 47(4): 71-76.
YANG R T, SHAO Y B, DU Q Z, et al. Few-shot knowledge graph completion based on subgraph structure semantic enhancement[J]. Journal of Beijing University of Posts and Telecommunications, 2024, 47(4): 71-76.
[51] GUO J T, ZHANG C X, LI L X, et al. A unified joint approach with topological context learning and rule augmentation for knowledge graph completion[C]//Findings of the Association for Computational Linguistics: ACL 2024. Stroudsburg: ACL, 2024: 13686-13696.
[52] YU C M, ZHANG Z G, AN L, et al. A knowledge graph completion model integrating entity description and network structure[J]. Aslib Journal of Information Management, 2023, 75(3): 500-522.
[53] CHEN C, WANG Y, SUN A, et al. Dipping PLMs sauce: bridging structure and text for effective knowledge graph completion via conditional soft prompting[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2307.01709.
[54] WANG J, HUANG W, SHI Q, et al. Knowledge prompting in pre-trained language model for natural language understanding[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2210.08536.
[55] ZHENG Z Y, DONG Y S, WANG S, et al. KG-CF: knowledge graph completion with context filtering under the guidance of large language models[C]//Proceedings of the 2024 IEEE International Conference on Big Data. Piscataway: IEEE, 2024: 805-810.
[56] LUO L, JU J, XIONG B, et al. ChatRule: mining logical rules with large language models for knowledge graph reasoning[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2309.01538.
[57] ZHANG Y C, CHEN Z, GUO L B, et al. Making large language models perform better in knowledge graph completion[C]//Proceedings of the 32nd ACM International Conference on Multimedia. New York: ACM, 2024: 233-242.
[58] SHEN J, WANG C, GONG L, et al. Joint language semantic and structure embedding for knowledge graph completion[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2209.08721.
[59] LIN Q K, MAO R, LIU J, et al. Fusing topology contexts and logical rules in language models for knowledge graph completion[J]. Information Fusion, 2023, 90: 253-264.
[60] SHU D, CHEN T, JIN M, et al. Knowledge graph large language model (KG-LLM) for link prediction[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2403.07311.
[61] HE T, LIU M, CAO Y X, et al. Exploring & exploiting high-order graph structure for sparse knowledge graph completion[J]. Frontiers of Computer Science, 2024, 19(2): 192306.
[62] CHAKRABARTI S, SINGH H, LOHIYA S, et al. Joint completion and alignment of multilingual knowledge graphs[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2022: 11922-11938.
[63] TANG R C, ZHAO Y, ZONG C Q, et al. Multilingual knowledge graph completion with language-sensitive multi-graph attention[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2023: 10508-10519.
[64] TONG V, NGUYEN D Q, HUYNH T T, et al. Joint multilingual knowledge graph completion and alignment[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2210.08922.
[65] CHEN X, CHEN M, FAN C, et al. Multilingual knowledge graph completion via ensemble knowledge transfer[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2010.03158.
[66] CHEN S, ZHANG Q, DONG J, et al. Entity alignment with noisy annotations from large language models[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2405.16806.
[67] SONG R, HE S, GAO S, et al. Multilingual knowledge graph completion from pretrained language models with knowledge constraints[EB/OL]. [2024-12-10]. https://arxiv.org/abs/2406.18085.
[68] CHEN M, TIAN Y, YANG M, et al. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[EB/OL]. [2024-12-10]. https://arxiv.org/abs/1611.03954.
[69] TAMM Y M, DAMDINOV R, VASILEV A. Quality metrics in recommender systems: do we calculate metrics consistently?[C]//Proceedings of the 15th ACM Conference on Recommender Systems. New York: ACM, 2021: 708-713.
[70] MILLER G A. WordNet[J]. Communications of the ACM, 1995, 38(11): 39-41.
[71] TRAN H N, TAKASU A. MEIM: multi-partition embedding interaction beyond block term format for efficient and expressive link prediction[C]//Proceedings of the 31st International Joint Conference on Artificial Intelligence, 2022: 2262-2269.