Abstract
Knowledge Graphs (KGs) provide supportively structured knowledge and have been applied to various downstream applications. Given a large amount of incomplete knowledge in KGs, knowledge graph completion (KGC) is proposed to reason over known facts and infer the missing links. The previous graph embedding approaches learn graph structure (i.e., triple structure/neighborhood structure) but cannot handle unseen entities, which is addressed by textual encoding approaches that utilize the textual knowledge of graph elements (i.e., entities/relations). However, the previous textual encoding approaches only resort to triples and thus cannot exploit the knowledge of neighbors, which provides abundant evidence to facilitate prediction. Moreover, they are insensitive to changes in the position of elements in triples when performing text modeling, and thus cannot effectively distinguish triples with the same elements but completely different semantics, which is detrimental to the final result. To address the above challenges, we propose a novel Structure-Enhanced and Position-Aware Knowledge Embedding (SEPAKE) framework. Specifically, masked elements reconstruction is devised to predict missing elements by reasoning over the contexts of subgraphs. As such, we incorporate the graph structure while maintaining the feature that textual information can be encoded. Meanwhile, position-aware learning is conducted to capture the semantic knowledge implied by the relative positions of elements in textualization. In addition, we employ task-specific adapters to store knowledge in a unified way to facilitate the storage and transfer of knowledge. Extensive experiments demonstrate the effectiveness of our framework, and we achieve state-of-the-art performance on standard datasets compared with textual encoding approaches. Besides, our proposed framework can efficiently improve the previous approaches by optionally pluggable adapters, further verifying the advancement and applicability of our work.
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Data Availability
The datasets generated during and/or analysed during the current study are available in the LibKGE repository, https://github.com/uma-pi1/kge.
References
Ji S, Pan S, Cambria E, Marttinen P, Philip SY (2021) A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33(2):494–514
Rossi A, Barbosa D, Firmani D, Matinata A, Merialdo P (2021) Knowledge graph embedding for link prediction: A comparative analysis. ACM Trans Knowl Discov Data (TKDD) 15(2):1–49
Li W, Peng R, Li Z (2022) Improving knowledge graph completion via increasing embedding interactions. Appl Intell 52(8):9289–9307
Guo J, Fan Y, Pang L, Yang L, Ai Q, Zamani H, Wu C, Croft WB, Cheng X (2020) A deep look into neural ranking models for information retrieval. Inf Process Manag 57(6):102067
Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637
Yang Z, Dong S (2020) Hagerec: Hierarchical attention graph convolutional network incorporating knowledge graph for explainable recommendation. Knowl-Based Syst 204:106194
Guo Q, Zhuang F, Qin C, Zhu H, Xie X, Xiong H, He Q (2020) A survey on knowledge graph-based recommender systems. IEEE Trans Knowl Data Eng 34(8):3549–3568
Saxena A, Tripathi A, Talukdar P (2020) Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4498–4507
Hogan A, Blomqvist E, Cochez M, d’Amato C, Melo GD, Gutierrez C, Kirrane S, Gayo JEL, Navigli R, Neumaier S et al (2021) Knowledge graphs. ACM Comput Surv (CSUR) 54(4):1–37
Chen X, Jia S, Xiang Y (2020) A review: Knowledge reasoning over knowledge graph. Expert Syst Appl 141:112948
Ranganathan V, Barbosa D (2022) Hoplop: multi-hop link prediction over knowledge graph embeddings. World Wide Web 25(2):1037–1065
Vashishth S, Sanyal S, Nitin V, Talukdar P (2020) Composition-based multi-relational graph convolutional networks. ICLR
Schlichtkrull M, Kipf NT, Bloem P, Berg vdR, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. ESWC
Wang B, Shen T, Long G, Zhou T, Wang Y, Chang Y (2021) Structure-augmented text representation learning for efficient knowledge graph completion. WWW, 1737–1748
Kim B, Hong T, Ko Y, Seo J (2020) Multi-task learning for knowledge graph completion with pre-trained language models. COLING, 1737–1743
Chen S, Liu X, Gao J, Jiao J, Zhang R, Ji Y (2021) Hitter - hierarchical transformers for knowledge graph embeddings. EMNLP, 10395–10407
Feng J, Wei Q, Cui J, Chen J (2022) Novel translation knowledge graph completion model based on 2d convolution. Appl Intell 52(3):3266–3275
Bordes A, Usunier N, Garcia-Durán A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. NIPS, 2787–2795
Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28
Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI Conference on Artificial Intelligence
Sun Z, Deng Z-H, Nie J-Y, Tang J (2019) Rotate: Knowledge graph embedding by relational rotation in complex space. ICLR
Yang B, Yih SW-t, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (ICLR) 2015
Trouillon T, Welbl J, Riedel S, Gaussier r, Bouchard G (2016) Complex embeddings for simple link prediction. ICML, 2071–2080
Balazevic I, Allen C, Hospedales T (2019) TuckER: Tensor factorization for knowledge graph completion. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5185–5194. Association for Computational Linguistics, Hong Kong, China. https://doi.org/10.18653/v1/D19-1522. https://aclanthology.org/D19-1522
Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. national conference on artificial intelligence
Vashishth S, Sanyal S, Nitin V, Agrawal N, Talukdar P (2020) Interacte: Improving convolution-based knowledge graph embeddings by increasing feature interactions. Proc AAAI Conf Artif Intell 34:3009–3016
Xie R, Liu Z, Jia J, Luan H, Sun M (2016) Representation learning of knowledge graphs with entity descriptions. THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2659–2665
Xiao H, Huang M, Meng L, Zhu X (2017) Ssp: Semantic space projection for knowledge graph embedding with text descriptions. THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 3104–3110
Kenton JDM-WC, Toutanova LK (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171–4186
Yao L, Mao C, Luo Y (2019) Kg-bert: Bert for knowledge graph completion. arXiv:1909.03193
Xiaozhi W, Tianyu G, Zhaocheng Z, Zhiyuan L, Juanzi L, Jian T (2021) Kepler: A unified model for knowledge embedding and pre-trained language representation. Transactions of the Association for Computational Linguistics, 176–194
Zhang Z, Han X, Liu Z, Jiang X, Sun M, Liu Q (2019) Ernie: Enhanced language representation with informative entities. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1441–1451
Meng Z, Liu F, Clark HT, Shareghi E, Collier N (2021) Mixture-of-partitions - infusing large biomedical knowledge graphs into bert. EMNLP, 4672–4681
Ruize W, Duyu T, Nan D, Zhongyu W, Xuanjing H, Jianshu j, Cuihong C, Daxin J, Ming Z (2021) K-adapter - infusing knowledge into pre-trained models with adapters. ACL/IJCNLP, 1405–1418
Houlsby N, Giurgiu A, Jastrzebski S, Morrone B, Laroussilhe dQ, Gesmundo A, Attariyan M, Gelly S (2019) Parameter-efficient transfer learning for nlp. International Conference on Machine Learning, 2790–2799
Hadsell R, Rao D, Rusu AA, Pascanu R (2020) Embracing change: Continual learning in deep neural networks. Trends Cogn Sci 24(12):1028–1040
Liu P, Yuan W, Fu J, Jiang Z, Hayashi H, Neubig G (2023) Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Comput Surv 55(9):1–35
Wang L, Zhao W, Wei Z, Liu J (2022) Simkgc: Simple contrastive knowledge graph completion with pre-trained language models. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4281–4294
Yao S, Pi D, Chen J (2022) Knowledge embedding via hyperbolic skipped graph convolutional networks. Neurocomputing 480:119–130
Yao S, Pi D, Chen J, Xu Y (2022) Gckg: Novel gated convolutional embedding model for knowledge graphs. Expert Syst Appl 208:118142
Zhiqing S, Shikhar V, Soumya S, Partha T, Yiming Y (2020) A re-evaluation of knowledge graph completion methods. ACL, 5516–5522
Dai Quoc Nguyen TDN, Nguyen DQ, Phung D (2018) A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of NAACL-HLT, pp. 327–333
Nathani D, Chauhan J, Sharma C, Kaul M (2019) Learning attention-based embeddings for relation prediction in knowledge graphs. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4710–4723
Vu T, Nguyen TD, Nguyen DQ, Phung D, et al (2019) A capsule network-based embedding model for knowledge graph completion and search personalization. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 2180–2189
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: An imperative style, high-performance deep learning library. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019)
He K, Chen X, Xie S, Li Y, Dollár P, Girshick R (2022) Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009
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This work is jointly supported by National Natural Science Foundation of China (61877043) and National Natural Science of China (61877044).
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Mei Yu, Tingxu Jiang, Jian Yu, Mankun Zhao, Jiujiang Gou, Ming Yang, Ruiguo Yu are contributed equally to this work.
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Yu, M., Jiang, T., Yu, J. et al. SEPAKE: a structure-enhanced and position-aware knowledge embedding framework for knowledge graph completion. Appl Intell 53, 23113–23123 (2023). https://doi.org/10.1007/s10489-023-04723-0
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DOI: https://doi.org/10.1007/s10489-023-04723-0