Abstract
The incompleteness of the knowledge graph limits its applications to various downstream tasks. To this end, numerous influential knowledge graph embedding models have been presented and have made great achievements in the domain of knowledge graph completion. However, most of these models only pay attention to the extraction of latent knowledge or translational features, and cannot comprehensively capture the surface semantics, latent interactions, and translational characteristics of triples. In this paper, a novel multi-channel convolutional model, MConvKGC, is presented for knowledge graph completion, which has three feature extraction channels and employs them to simultaneously extract shallow semantics, latent interactions, and translational characteristics, respectively. In addition, MConvKGC adopts an asymmetric convolutional block to comprehensively extract the latent interactions from triples, and process the generated feature maps with various attention mechanisms to further learn local dependencies between entities and relations. The results of the conducted link prediction experiments on FB15k-237, WN18RR, and UMLS indicate that our proposed MConvKGC shows excellent performance and outperforms previous state-of-the-art KGE models in the majority of cases.








Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Shen T, Zhang F, Cheng J (2022) A comprehensive overview of knowledge graph completion. Knowl-Based Syst 255:109597
Ji S, Pan S, Cambria E et al (2021) A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33(2):494–514
Jin Q, Yuan Z, Xiong G et al (2022) Biomedical question answering: a survey of approaches and challenges. ACM Comput Surv (CSUR) 55(2):1–36
Zheng W, Yin L, Chen X et al (2021) Knowledge base graph embedding module design for visual question answering model. Pattern Recogn 120:108153
Liu H, Zheng C, Li D et al (2021) EDMF: efficient deep matrix factorization with review feature learning for industrial recommender system. IEEE Trans Ind Inf 18(7):4361–4371
Liu H, Zheng C, Li D et al (2022) Multi-perspective social recommendation method with graph representation learning. Neurocomputing 468:469–481
Feng J, Yu FR, Pei Q et al (2019) Cooperative computation offloading and resource allocation for blockchain-enabled mobile-edge computing: a deep reinforcement learning approach. IEEE Internet Things J 7(7):6214–6228
Liu L, Feng J, Pei Q et al (2020) Blockchain-enabled secure data sharing scheme in mobile-edge computing: an asynchronous advantage actor-critic learning approach. IEEE Internet Things J 8(4):2342–2353
Fei H, Ren Y, Zhang Y et al (2021) Enriching contextualized language model from knowledge graph for biomedical information extraction. Brief Bioinform 22(3):bbaa110
Nadkarni R, Wadden D, Beltagy I et al (2021) Scientific language models for biomedical knowledge base completion: an empirical study. ArXiv Preprint ArXiv:2106.09700
Li Z, Liu H, Zhang Z et al (2022) Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Trans Neural Netw Learn Syst 33(8):3961–3973
Li Z, Liu H, Zhang Z et al (2021) Recalibration convolutional networks for learning interaction knowledge graph embedding. Neurocomputing 427:118–130
Xue Z, Zhang Z, Liu H et al (2023) Learning knowledge graph embedding with multi-granularity relational augmentation network. Expert Syst Appl 233:120953
Bordes A, Usunier N, Garcia-Duran A et al (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, pp. 2787–2795
Yang B, Yih W, He X, et al (2014) Embedding entities and relations for learning and inference in knowledge bases. ArXiv Preprint ArXiv:1412.6575
Trouillon T, Welbl J, Riedel S, et al (2016) Complex embeddings for simple link prediction. In: International conference on machine learning. PMLR, pp 2071-2080
Liu H, Zhang C, Deng Y et al (2023) TransIFC: invariant cues-aware feature concentration learning for efficient fine-grained bird image classification. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2023.3238548
Liu T, Liu H, Yang B et al (2023) LDCNet: limb direction cues-aware network for flexible human pose estimation in industrial behavioral biometrics systems. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2023.3266366
Liu H, Liu T, Chen Y et al (2022) EHPE: skeleton cues-based Gaussian coordinate encoding for efficient human pose estimation. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2022.3197364
Dettmers T, Minervini P, Stenetorp P et al (2018) Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI conference on artificial intelligence. 32(1)
Balažević I, Allen C, Hospedales TM (2019) Hypernetwork knowledge graph embeddings. In: Artificial neural networks and machine learning-ICANN 2019: workshop and special sessions: 28th international conference on artificial neural networks, Munich, Germany, September 17-19, Proceedings 28. Springer International Publishing, pp 553-565
Vashishth S, Sanyal S, Nitin V et al (2020) Interacte: improving convolution-based knowledge graph embeddings by increasing feature interactions. In: Proceedings of the AAAI conference on artificial intelligence. 34(03): 3009-3016
Jiang D, Wang R, Yang J et al (2021) Kernel multi-attention neural network for knowledge graph embedding. Knowl-Based Syst 227:107188
Feng J, Wei Q, Cui J et al (2022) Novel translation knowledge graph completion model based on 2D convolution. Appl Intell 52(3):3266–3275
Dai Y, Wang S, Xiong NN et al (2020) A survey on knowledge graph embedding: approaches, applications and benchmarks. Electronics 9(5):750
Zhang Z, Cai J, Zhang Y et al (2020) Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI conference on artificial intelligence. 34(03): 3065-3072
Zhang F, Wang X, Li Z et al (2020) TransRHS: a representation learning method for knowledge graphs with relation hierarchical structure. In: International joint conference on artificial intelligence, IJCAI, pp. 2987–2993
Kazemi SM, Poole D (2018) Simple embedding for link prediction in knowledge graphs. ArXiv Preprint arXiv:1802.04868
Zhang W, Paudel B, Zhang W et al (2019) Interaction embeddings for prediction and explanation in knowledge graphs. In: Proceedings of The twelfth ACM international conference on web search and data mining, pp 96-104
Schlichtkrull M, Kipf TN, Bloem P et al (2018) Modeling relational data with graph convolutional networks. In: the Semantic Web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3-7, Proceedings 15. Springer International Publishing, pp 593-607
Nguyen DQ, Nguyen TD, Nguyen DQ et al (2017) A novel embedding model for knowledge base completion based on convolutional neural network. ArXiv Preprint ArXiv:1712.02121
Bansal T, Juan DC, Ravi S et al (2019) A2N: attending to neighbors for knowledge graph inference. In: Proceedings of The 57th annual meeting of the association for computational linguistics, pp 4387-4392
Jiang X, Wang Q, Wang B (2019) Adaptive convolution for multi-relational learning. In: Proceedings of The 2019 conference of The North American Chapter of The Association for Computational Linguistics: Human Language Technologies, Vol 1 (Long and Short Papers), pp 978-987
Vashishth S, Sanyal S, Nitin V et al (2020) Composition-based multi-relational graph convolutional networks. ArXiv Preprint ArXiv:1911.03082
Ren F, Li J, Zhang H et al (2020) Knowledge graph embedding with atrous convolution and residual learning. ArXiv Preprint ArXiv:2010.12121
Huang J, Zhang TH, Zhu J et al (2021) A deep embedding model for knowledge graph completion based on attention mechanism. Neural Comput Appl 33(15):9751–9760
Ding X, Guo Y, Ding G et al (2019) Acnet: strengthening the Kernel skeletons for powerful CNN via asymmetric convolution blocks. In: Proceedings of The IEEE/CVF international conference on computer vision, pp 1911-1920
He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of The IEEE conference on computer vision and pattern recognition, pp 770-778
Toutanova K, Chen D (2015) Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd workshop on continuous vector space models and their compositionality, pp 57-66
Yue X, Wang Z, Huang J et al (2020) Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4):1241–1251
García-Durán A, Niepert M (2017) Kblrn: end-to-end learning of knowledge base representations with latent, relational, and numerical features. ArXiv Preprint ArXiv:1709.04676
Cai L, Wang WY (2017) Kbgan: Adversarial learning for knowledge graph embeddings. ArXiv Preprint ArXiv:1711.04071
Wang K, Liu Y, Xu X et al (2018) Knowledge graph embedding with entity neighbors and deep memory network. ArXiv Preprint ArXiv:1808.03752
Acknowledgements
This study was supported by the Open Fund Project from Marine Ecological Restoration and Smart Ocean Engineering Research Center of Hebei Province (Grant No. HBMESO2315), the Science and Technology Project of the Hebei Education Department (Grant No. ZD2021088), and Vital Signs Detection based on milimeter-wave radar (Grant No. ZD-YG-202317-23).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
We declare that we have no financial and personal relationships with other people or organizations that can influence our work.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sun, X., Chen, Q., Hao, M. et al. Mconvkgc: a novel multi-channel convolutional model for knowledge graph completion. Computing 106, 915–937 (2024). https://doi.org/10.1007/s00607-023-01247-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-023-01247-w
Keywords
- Knowledge graph completion
- Knowledge graph embedding
- Asymmetric convolutional block
- Feature extraction
- Attention mechanism