Abstract
Datasets that include alignments between natural language and Knowledge Graphs are fundamental to a wide variety of Natural Language Processing and Generation tasks. Current state-of-the-art aligned datasets, though, are significantly impacted by reduced size and scarcity of covered domains, and their quality is difficult to evaluate. To compensate for these issues, we introduce SEALIon, a tool for extracting RDF triples from natural language textual corpora based on a human-in-the-loop approach. We present our first results of SEALIon’s approach, paving the way for further researches in the field of human-in-the-loop triple extraction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
The version 0.1 of the tool is available at https://sealion.ml/. The source code can be downloaded from the Git repository https://bitbucket.org/disco_unimib/sealion/.
- 4.
- 5.
Available at https://gitlab.com/shimorina/webnlg-datas.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
- 13.
References
Angeli, G., Tibshirani, J., Wu, J., Manning, C.D.: Combining distant and partial supervision for relation extraction. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1556–1567. Association for Computational Linguistics, Doha, Qatar (2014)
Bhattacharjee, B., Kender, J.R., Hill, M., Dube, P., Huo, S., Glass, M.R., Belgodere, B., Pankanti, S., Codella, N., Watson, P.: P2l: Predicting transfer learning for images and semantic relations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 760–761 (2020)
Cao, C.C., She, J., Tong, Y., Chen, L.: Whom to ask? jury selection for decision making tasks on micro-blog services. Proc. VLDB Endow. 5(11), 1495–1506 (2012)
Ferreira, T.C., Gardent, C., Ilinykh, N., van der Lee, C., Mille, S., Moussallem, D., Shimorina, A.: The 2020 bilingual, bi-directional WebNLG+ shared task: overview and evaluation results (WebNLG+ 2020). In: Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pp. 55–76. Association for Computational Linguistics, Dublin, Ireland (Virtual) (2020)
Ferreira, T.C., van der Lee, C., van Miltenburg, E., Krahmer, E.: Neural data-to-text generation: A comparison between pipeline and end-to-end architectures. In: Proceedings of the EMNLP-IJCNLP, pp. 552–562. Association for Computational Linguistics, Hong Kong, China (2019)
Cremaschi, M., De Paoli, F., Rula, A., Spahiu, B.: A fully automated approach to a complete semantic table interpretation. Futur. Gener. Comput. Syst. 112, 478–500 (2020)
Elsahar, H., Vougiouklis, P., Remaci, A., Gravier, C., Hare, J., Laforest, F., Simperl, E.: T-REx: A large scale alignment of natural language with knowledge base triples. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 3448–3452. European Language Resources Association (ELRA), Miyazaki, Japan (2018)
Faridani, S., Hartmann, B., Ipeirotis, P.G.: What’s the right price? pricing tasks for finishing on time. In: Proceedings of the 11th AAAI Conference on Human Computation, AAAIWS’11-11, pp. 26–31. AAAI Press (2011)
Gardent, C., Shimorina, A., Narayan, S., Perez-Beltrachini, L.: Creating training corpora for NLG micro-planners. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 179–188. Association for Computational Linguistics, Vancouver, Canada (2017)
Glass, M., Gliozzo, A.: A dataset for web-scale knowledge base population. In: The Semantic Web, pp. 256–271. Springer International Publishing, Cham (2018)
Glass, M., Gliozzo, A., Hassanzadeh, O., Mihindukulasooriya, N., Rossiello, G.: Inducing implicit relations from text using distantly supervised deep nets. In: The Semantic Web—ISWC 2018, pp. 38–55. Springer International Publishing, Cham (2018)
Grosman, J.S., Furtado, P.H.T., Rodrigues, A.M.B., Schardong, G.G., Barbosa, S.D.J., Lopes, H.C.V.: Eras: improving the quality control in the annotation process for natural language processing tasks. Inf. Syst. 93, 101553 (2020)
Hu, X., Wen, L., Xu, Y., Zhang, C., Yu, P.: SelfORE: Self-supervised relational feature learning for open relation extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 673–3682. Association for Computational Linguistics (2020)
Li, G., Wang, J., Zheng, Y., Franklin, M.J.: Crowdsourced data management: A survey. IEEE Trans. Knowl. Data Eng. 28(9), 2296–2319 (2016)
Li, M., Jin, J., Wu, W., Yang, Y., He, L., Yang, J.: A crowdsourcing based human-in-the-loop framework for denoising uus in relation extraction tasks. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2019)
Lin, X., Li, H., Xin, H., Li, Z., Chen, L.: Kbpearl: a knowledge base population system supported by joint entity and relation linking. Proc. VLDB Endow. 13(7), 1035–1049 (2020)
Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Inf. Comput. 108(2), 212–261 (1994)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv:1301.3781
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS’13, pp. 3111–3119. Curran Associates Inc. (2013)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003–1011. Association for Computational Linguistics, Suntec, Singapore (2009)
Mrabet, Y., Vougiouklis, P., Kilicoglu, H., Gardent, C., Demner-Fushman, D., Hare, J., Simperl, E.: Aligning texts and knowledge bases with semantic sentence simplification. In: Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016), pp. 29–36. Association for Computational Linguistics, Edinburgh, Scotland (2016)
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. CoRR (2019)
Riedel, S., Yao, L., McCallum, A., Marlin, B.M.: Relation extraction with matrix factorization and universal schemas. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 74–84. Association for Computational Linguistics, Atlanta, Georgia (2013)
Shimorina, A., Khasanova, E., Gardent, C.: Creating a corpus for Russian data-to-text generation using neural machine translation and post-editing. In: Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, pp. 44–49. Association for Computational Linguistics, Florence, Italy (2019)
Simon, E., Guigue, V., Piwowarski, B.: Unsupervised information extraction: Regularizing discriminative approaches with relation distribution losses. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1378–1387. Association for Computational Linguistics, Florence, Italy (2019)
Smirnova A., Cudré-Mauroux, P.: Relation extraction using distant supervision: A survey. ACM Comput. Surv. 51(5) (2018)
Yao, L., Riedel, S., McCallum, A.: Collective cross-document relation extraction without labelled data. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1013–1023. Association for Computational Linguistics, Cambridge, MA (2010)
Zhao, C., Walker, M., Chaturvedi, S.: Bridging the structural gap between encoding and decoding for data-to-text generation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2481–2491. Association for Computational Linguistics (2020)
Zheng, Y., Cheng, R., Maniu, S., Mo, L.: On optimality of jury selection in crowdsourcing. In: Proceedings of the 18th International Conference on Extending Database Technology, EDBT 2015, pp. 193–204 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Barbato, J.A., Cremaschi, M., Rula, A., Maurino, A. (2024). Toward a Human-in-the-Loop Approach to Create Training Datasets for RDF Lexicalisation. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 822. Springer, Cham. https://doi.org/10.1007/978-3-031-47721-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-47721-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47720-1
Online ISBN: 978-3-031-47721-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)