A graph embedding-based dynamic update method for intelligence knowledge graphs

Abstract

Dynamic updating of intelligence knowledge graphs has emerged as a significant research topic for wide range of applications. However, as intelligence data continuously accumulates, dynamic update process of knowledge graph faces the inaccuracy problem, caused by complexity of incremental data and noise interference. To address the issue, we propose a novel Graph Embedding-based Dynamic Update Method (GEDUM) for intelligence knowledge graphs, which comprehensively considers the dynamic evolution characteristics of intelligence data and optimizes the updating of knowledge graph through embedding networks. Specifically, we design a Local-to-Global Feature Aggregation Module (L2GFAM) for learning global graph embeddings, deeply exploring and optimizing intrinsic features of graph nodes and edges. Building on this, an Attention-guided Weighted Fusion Strategy (AWFS) is proposed to efficiently merge and update embeddings of local subgraphs and newly added graph components, taking into account the correlation and complementarity between new and existing data. Extensive validations on real-world dataset demonstrate the significant superiority of our proposed solution over traditional methods in handling dynamically evolving intelligence data.

Graphical abstract

Keywords

intelligence knowledge graphs / dynamic update / attention weighted fusion

References

[1] Sarhan I, Spruit M . Open-CyKG: an open cyber threat intelligence knowledge graph. Knowledge-Based Systems, 2021, 233: 107524

[2] Liu C, Xu T, Liu Q, Zheng Z, Peng J, Chen E. MERGE: multi-view relationship graph network for event-driven stock movement prediction. In: Proceedings of the 8th International Joint Conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data. 2024, 224−239

[3] Dessì D, Osborne F, Reforgiato Recupero D, Buscaldi D, Motta E, Sack H. AI-KG: an automatically generated knowledge graph of artificial intelligence. In: Proceedings of the 19th International Semantic Web Conference. 2020, 127−143

[4] Janowicz K, Hitzler P, Li W, Rehberger D, Schildhaue M, . . Know, know where, KnowWheregraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence. AI Magazine, 2022, 43( 1): 30–39

[5] Fei L, Wu T, Khan A. Online updates of knowledge graph embedding. In: Benito R M, Cherifi C, Cherifi H, Moro E, Rocha L M, Sales-Pardo M, eds. Complex Networks & Their Applications X: Volume 2, Proceedings of the 10th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021. Cham: Springer, 2022, 523−535

[6] Zhou H, Shen T, Liu X, Zhang Y, Guo P, Zhang J . Survey of knowledge graph approaches and applications. Journal on Artificial Intelligence, 2020, 2( 2): 89–101

[7] Li L, Wang P, Yan J, Wang Y, Li S, Jiang J, Sun Z, Tan B, Chang T H, Wang S, Liu Y . Real-world data medical knowledge graph: construction and applications. Artificial Intelligence in Medicine, 2020, 103: 101817

[8] Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2013, 2787−2795

[9] Wang Z, Zhang J, Feng J, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 1112−1119

[10] Lin Y, Liu Z, Sun M, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2181−2187

[11] Yang B, Yih W T, He X, Gao J, Deng L. Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the 3rd International Conference on Learning Representations. 2015

[12] Trouillon T, Welbl J, Riedel S, Gaussier E, Bouchard G. Complex embeddings for simple link prediction. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning. 2016, 2071−2080

[13] Zheng Z, Wang C, Xu T, Shen D, Qin P, Huai B, Liu T, Chen E. Drug package recommendation via interaction-aware graph induction. In: Proceedings of Web Conference 2021. 2021, 1284−1295

[14] Zheng Z, Wang C, Xu T, Shen D, Qin P, Zhao X, Hua B, Wu X, Chen E . Interaction-aware drug package recommendation via policy gradient. ACM Transactions on Information Systems, 2023, 41( 1): 3

[15] Wu L, Qiu Z, Zheng Z, Zhu H, Chen E. Exploring large language model for graph data understanding in online job recommendations. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2024, 9178−9186

[16] Chao W, Qiu Z, Wu L, Guo Z, Zheng Z, Zhu H, Liu H. A cross-view hierarchical graph learning hypernetwork for skill demand-supply joint prediction. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2024, 19813−19822

[17] Zhao Z, Zhu X, Xu T, Lizhiyu A, Yu Y, Li X, Yin Z, Chen E. Time-interval aware share recommendation via bi-directional continuous time dynamic graphs. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023, 822−831

[18] Zhao Z, Yang Y, Yin Z, Xu T, Zhu X, Lin F, Li X, Che E . Adversarial attack and defense on discrete time dynamic graphs. IEEE Transactions on Knowledge and Data Engineering, 2024, 36( 12): 7600–7611

[19] Sankar A, Wu Y, Gou L, Zhang W, Yang H. DySAT: Deep neural representation learning on dynamic graphs via self-attention networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining. 2020, 519−527

[20] Goyal P, Chhetri S R, Mehrabi N, Ferrara E, Canedo A. DynamicGEM: A library for dynamic graph embedding methods. 2018, arXiv preprint arXiv: 1811.10734

[21] García-Durán A, Dumančić S, Niepert M. Learning sequence encoders for temporal knowledge graph completion. In: Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 4816−4821

[22] Tan Q, Liu N, Huang X, Choi S H, Li L, Chen R, Hu X. S2GAE: self-supervised graph autoencoders are generalizable learners with graph masking. In: Proceedings of the 16th ACM International Conference on Web Search and Data Mining. 2023, 787−795

[23] Kreuzer D, Beaini D, Hamilton W L, Létourneau V, Tossou P. Rethinking graph transformers with spectral attention. In: Proceedings of the 35th International Conference on Neural Information Processing Systems. 2021, 1654

[24] Zhu Z, Zhang Z, Xhonneux L P, Tang J. Neural bellman-ford networks: A general graph neural network framework for link prediction. In: Proceedings of the 35th International Conference on Neural Information Processing Systems. 2021, 2256

[25] Mehran Kazemi S, Goel R, Jain K, Kobyzev I, Sethi A, Forsyth P, Poupart P . Representation learning for dynamic graphs: a survey. The Journal of Machine Learning Research, 2020, 21( 1): 70

[26] Wei Y, Chen W, Li Z, Zhao L. Incremental update of knowledge graph embedding by rotating on hyperplanes. In: Proceedings of 2021 IEEE International Conference on Web Services. 2021, 516−524

[27] Jin W, Qu M, Jin X, Ren X. Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. 2019, 6669−6683

[28] Zhang J, Shao J, Cui B. StreamE: learning to update representations for temporal knowledge graphs in streaming scenarios. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023, 622−631

[29] Zhang J, Li J . Enhanced knowledge graph embedding by jointly learning soft rules and facts. Algorithms, 2019, 12( 12): 265

[30] Devlin J, Chang M W, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019, 4171−4186

[31] Abdi H, Williams L J . Principal component analysis. WIREs Computational Statistics, 2010, 2( 4): 433–459

[32] Velickovic P, Cucurull G, Casanova A, Romero A, Li`oP, Bengio Y. Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations. 2018

[33] Sun Z, Deng Z H, Nie J Y, Tang J. RotatE: knowledge graph embedding by relational rotation in complex space. In: Proceedings of the 7th International Conference on Learning Representations. 2019

RIGHTS & PERMISSIONS

Higher Education Press