ABSTRACT
In recent years, knowledge graph embeddings have achieved great success. Many methods have been proposed and achieved state-of-the-art results in various tasks. However, most of the current methods present one or more of the following problems: (i) They only consider fact triplets, while ignoring the ontology information of knowledge graphs. (ii) The obtained embeddings do not contain much semantic information. Therefore, using these embeddings for semantic tasks is problematic. (iii) They do not enable large-scale training. In this paper, we propose a new algorithm that incorporates the ontology of knowledge graphs and partitions the knowledge graph based on classes to include more semantic information for parallel training of large-scale knowledge graph embeddings. Our preliminary results show that our algorithm performs well on several popular benchmarks.
- Ivana Balažević, Carl Allen, and Timothy M Hospedales. 2019. Tucker: Tensor factorization for knowledge graph completion. arXiv preprint arXiv:1901.09590 (2019).Google Scholar
- Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, and Ian Horrocks. 2021. Owl2vec*: Embedding of owl ontologies. Machine Learning 110, 7 (2021), 1813–1845.Google ScholarCross Ref
- Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018. Convolutional 2d knowledge graph embeddings. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.Google ScholarCross Ref
- Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018. Convolutional 2d knowledge graph embeddings. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.Google ScholarCross Ref
- Chi Thang Duong, Trung Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. 2021. Scalable robust graph embedding with Spark. Proceedings of the VLDB Endowment 15, 4 (2021), 914–922.Google ScholarDigital Library
- Bordes et al.2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 (2013).Google Scholar
- Dong et al.2014. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 601–610.Google Scholar
- Guo et al.2015. Semantically smooth knowledge graph embedding. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 84–94.Google Scholar
- Hao et al.2017. An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 221–231.Google Scholar
- Kurt et al.2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data. 1247–1250.Google Scholar
- Lehmann et al.2015. Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia. Semantic web 6, 2 (2015), 167–195.Google Scholar
- Yang et al.2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014).Google Scholar
- Zhang et al.2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 353–362.Google Scholar
- Nitisha Jain, Jan-Christoph Kalo, Wolf-Tilo Balke, and Ralf Krestel. 2021. Do embeddings actually capture knowledge graph semantics?. In The Semantic Web: 18th International Conference, ESWC 2021, Virtual Event, June 6–10, 2021, Proceedings 18. Springer, 143–159.Google ScholarDigital Library
- Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In International joint conference on natural language processing. 687–696.Google Scholar
- Seyed Mehran Kazemi and David Poole. 2018. Simple embedding for link prediction in knowledge graphs. Advances in neural information processing systems 31 (2018).Google Scholar
- Adrian Kochsiek and Rainer Gemulla. 2021. Parallel training of knowledge graph embedding models: a comparison of techniques. Proceedings of the VLDB Endowment 15, 3 (2021), 633–645.Google ScholarDigital Library
- Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Twenty-ninth AAAI conference on artificial intelligence.Google ScholarDigital Library
- Changsung Moon, Paul Jones, and Nagiza F Samatova. 2017. Learning entity type embeddings for knowledge graph completion. In Proceedings of the 2017 ACM on conference on information and knowledge management. 2215–2218.Google ScholarDigital Library
- Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Phung. 2017. A novel embedding model for knowledge base completion based on convolutional neural network. arXiv preprint arXiv:1712.02121 (2017).Google Scholar
- Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A three-way model for collective learning on multi-relational data. In Icml.Google Scholar
- Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15. Springer, 593–607.Google ScholarDigital Library
- Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web. 697–706.Google ScholarDigital Library
- Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019).Google Scholar
- Zequn Sun, Wei Hu, Qingheng Zhang, and Yuzhong Qu. 2018. Bootstrapping Entity Alignment with Knowledge Graph Embedding.. In IJCAI, Vol. 18. 4396–4402.Google Scholar
- Kristina Toutanova and Danqi Chen. 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. 57–66.Google ScholarCross Ref
- Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, and Guillaume Bouchard. 2016. Complex Embeddings for Simple Link Prediction. In Proceedings of The 33rd International Conference on Machine Learning. 2071–2080.Google Scholar
- Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019. Composition-based multi-relational graph convolutional networks. arXiv preprint arXiv:1911.03082 (2019).Google Scholar
- Denny Vrandečić and Markus Krötzsch. 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM 57, 10 (2014), 78–85.Google ScholarDigital Library
- Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering 29, 12 (2017), 2724–2743.Google ScholarCross Ref
- Quan Wang, Bin Wang, and Li Guo. 2015. Knowledge base completion using embeddings and rules. In Twenty-fourth international joint conference on artificial intelligence.Google Scholar
- Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI conference on artificial intelligence, Vol. 28.Google ScholarCross Ref
- Ruobing Xie, Zhiyuan Liu, Maosong Sun, 2016. Representation learning of knowledge graphs with hierarchical types.. In IJCAI, Vol. 2016. 2965–2971.Google Scholar
- Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, and Jie Wang. 2020. Learning hierarchy-aware knowledge graph embeddings for link prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 3065–3072.Google ScholarCross Ref
- Zhao Zhang, Fuzhen Zhuang, Meng Qu, Fen Lin, and Qing He. 2018. Knowledge graph embedding with hierarchical relation structure. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 3198–3207.Google ScholarCross Ref
- Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, and George Karypis. 2020. Dgl-ke: Training knowledge graph embeddings at scale. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 739–748.Google ScholarDigital Library
Index Terms
- A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings
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