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A Relation-Interactive Approach for Message Passing in Hyper-relational Knowledge Graphs (2402.15140v2)
Published 23 Feb 2024 in cs.AI
Abstract: Hyper-relational knowledge graphs (KGs) contain additional key-value pairs, providing more information about the relations. In many scenarios, the same relation can have distinct key-value pairs, making the original triple fact more recognizable and specific. Prior studies on hyper-relational KGs have established a solid standard method for hyper-relational graph encoding. In this work, we propose a message-passing-based graph encoder with global relation structure awareness ability, which we call ReSaE. Compared to the prior state-of-the-art approach, ReSaE emphasizes the interaction of relations during message passing process and optimizes the readout structure for link prediction tasks. Overall, ReSaE gives a encoding solution for hyper-relational KGs and ensures stronger performance on downstream link prediction tasks. Our experiments demonstrate that ReSaE achieves state-of-the-art performance on multiple link prediction benchmarks. Furthermore, we also analyze the influence of different model structures on model performance.
- Curriculum learning. In Proceedings of the 26th Annual International Conference on Machine Learning, page 41–48.
- Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD ’08, page 1247–1250, New York, NY, USA. Association for Computing Machinery.
- Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems, pages 2787–2795.
- Convolutional 2d knowledge graph embeddings. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 1811–1818.
- Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with pytorch geometric. CoRR, abs/1903.02428.
- Message passing for hyper-relational knowledge graphs. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7346–7359, Online. Association for Computational Linguistics.
- NeuInfer: Knowledge inference on N-ary facts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6141–6151, Online. Association for Computational Linguistics.
- Link prediction on n-ary relational data. In The World Wide Web Conference, WWW 2019, pages 583–593.
- Wikidata through the eyes of dbpedia. Semantic Web, 9(4):493–503.
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015.
- Tensor decompositions for temporal knowledge base completion. In International Conference on Learning Representations.
- Hahe: Hierarchical attention for hyper-relational knowledge graphs in global and local level. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics.
- Diego Marcheggiani and Ivan Titov. 2017. Encoding sentences with graph convolutional networks for semantic role labeling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017, pages 1506–1515.
- Alessio Micheli. 2009. Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks, 20(3):498–511.
- Yago 4: A reason-able knowledge base. In Extended Semantic Web Conference, ESWC 2020.
- Beyond triplets: Hyper-relational knowledge graph embedding for link prediction. In Proceedings of The Web Conference 2020, page 1885–1896.
- Modeling relational data with graph convolutional networks. In The Semantic Web - 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3-7, 2018, Proceedings, pages 593–607.
- Rotate: Knowledge graph embedding by relational rotation in complex space. In 7th International Conference on Learning Representations, ICLR 2019.
- Composition-based multi-relational graph convolutional networks. In International Conference on Learning Representations.
- Attention is all you need. In Advances in Neural Information Processing Systems NIPS 2017, pages 5998–6008.
- Graph attention networks. In 6th International Conference on Learning Representations, ICLR.
- Denny Vrandecic and Markus Krötzsch. 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM, 57(10):78–85.
- Link prediction on n-ary relational facts: A graph-based approach. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 396–407, Online. Association for Computational Linguistics.
- On the representation and embedding of knowledge bases beyond binary relations. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pages 1300–1307.
- Hyper2: Hyperbolic embedding for hyper-relational link prediction. Neurocomputing, 492:440–451.
- Donghan Yu and Yiming Yang. 2021. Improving hyper-relational knowledge graph completion. CoRR, abs/2104.08167.
- Scalable instance reconstruction in knowledge bases via relatedness affiliated embedding. In The World Wide Web Conference, WWW 2018, pages 1185–1194.