Abstract
Facts are conditionally established in most cases. However, current Knowledge Graph (KG) techniques only focus on the modeling and representations of facts, neglecting the presence of conditions, which are necessary to establish the validity of facts. In this paper, we propose Conditional Knowledge Graph (Conditional-KG), which employs a three-layer hierarchical network to incorporate both facts and conditions. To facilitate research on the automatic construction of Conditional-KG, we manually annotate an innovative large-scale dataset named HACISU. Based on the Conditional-KG design and HACISU, we propose a simple construction model to benchmark HACISU. Experimental results show that our benchmark model outperforms several baselines but still has a considerable margin with human performance. We highlight the significance of HACISU, as it is the first carefully annotated dataset with conditional information. Our dataset is publicly available in http://101.200.120.155:5555/, hoping to serve as a challenging testbed and an ideal benchmark for Conditional-KG construction.
Y. Lv and Z. Zheng — Equal contribution.
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References
Wu, F., Weld, D.S.: Open information extraction using wikipedia. In: Proceedings of ACL, pp. 118–127 (2010)
Ji, S., Pan, S., Cambria, E., Marttinen, P., Philip, S.Y.: A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494–514 (2021)
Jiang, T., Zhao, T., Qin, B., Liu, T., Chawla, N., Jiang, M.: Multi-input multi-output sequence labeling for joint extraction of fact and condition tuples from scientific text. In: Proceedings of EMNLP-IJCNLP, pp. 302–312 (2019)
Jiang, T., Zhao, T., Qin, B., Liu, T., Chawla, N.V., Jiang, M.: The role of “condition” a novel scientific knowledge graph representation and construction model. In: Proceedings of ACM SIGKDD, pp. 1634–1642 (2019)
Ro, Y., Lee, Y.: Multi\(\hat{}\)2OIE: multilingual open information extraction based on multi-head attention with BERT. In: Findings of EMNLP, pp. 1107–1117 (2020)
Gashteovski, K., Gemulla, R., del Corro, L.: MinIE: minimizing facts in open information extraction. In: Proceedings of EMNLP, pp. 2630–2640 (2017)
Cetto, M., Niklaus, C., Freitas, A., Handschuh, S.: Graphene: a context-preserving open information extraction system. In: Proceedings of COLING, pp. 94–98 (2018)
Bhutani, N., Jagadish, H.V., Radev, D.: Nested propositions in open information extraction. In: Proceedings of EMNLP, pp. 55–64 (2016)
Kolluru, K., Aggarwal, S., Rathore, V., Mausam, Chakrabarti, S.: IMoJIE: iterative memory-based joint open information extraction. In: Proceedings of ACL, pp. 5871–5886 (2020)
Yu, B., Wang, Y., Liu, T., Zhu, H., Sun, L., Wang, B.: Maximal clique based non-autoregressive open information extraction. In: Proceedings of EMNLP, pp. 9696–9706 (2021)
Mausam: Open information extraction systems and downstream applications. In: Proceedings of IJCAI, pp. 4074–4077 (2016)
Bhardwaj, S., Aggarwal, S., Mausam, M.: CaRB: a crowdsourced benchmark for open IE. In: Proceedings of EMNLP-IJCNLP, pp. 6262–6267 (2019)
Stanovsky, G., Dagan, I.: Creating a large benchmark for open information extraction. In: Proceedings of EMNLP, pp. 2300–2305 (2016)
Stanovsky, G., Michael, J., Zettlemoyer, L., Dagan, I.: Supervised open information extraction. In: Proceedings of NAACL, pp. 885–895 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL, pp. 4171–4186 (2019)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of NeurIPS, pp. 5998–6008 (2017)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: Proceedings of ICLR (2019)
Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. surv. 4, 40–79 (2010)
Angeli, G., Johnson Premkumar, M.J.: Leveraging linguistic structure for open domain information extraction. In: Proceedings of ACL, pp. 344–354 (2015)
Corro, L.D., Gemulla, R.: ClausIE: clause-based open information extraction. In: Proceedings of WWW ’13, pp. 355–366 (2013)
Zhan, J., Zhao, H.: Span model for open information extraction on accurate corpus. In: Proceedings of AAAI, pp. 9523–9530 (2020)
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Lv, Y., Zheng, Z., Liu, M., Qin, B. (2023). Conditional Knowledge Graph: Design, Dataset and a Preliminary Model. In: Wang, H., Han, X., Liu, M., Cheng, G., Liu, Y., Zhang, N. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence. CCKS 2023. Communications in Computer and Information Science, vol 1923. Springer, Singapore. https://doi.org/10.1007/978-981-99-7224-1_16
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