Abstract
The task of entity linking aims to map the entity reference in the text with the unambiguous entity in the knowledge base. However, under the background of the continuous growth of science and technology service platform services, the complexity and diversity of domain semantic information, and the vagueness and ambiguity of natural language, the task of linking industrial chain knowledge graphs and technology service resources faces the following problems: (1) The increase in the types of semantic information in the do-main will lead to the lack of cognition and ambiguity in the entity linking process, which will affect the accuracy of entity linking; (2) the context and interaction of corpus information Dependencies become more complex, making insufficient consideration of the mapping relationship between linked entities. To address the above issues, this paper proposes a generative method and multiple sources of information Autoregressive Entity Linking (GMoAEL), which uses data sets such as unstructured textual descriptions, related reference links, and fine-grained structured types Information and other sources of information are used to build a unified dense representation for entity learning, and an autoregressive model in the form of encoder-decoder is used to adjust the link generation process to handle the complex mapping relationship between in-put and output. This paper uses the AIDA CoNLL-YAGO data set to conduct experiments. Compared with other methods, the Micro-F1 value is 1.6% points ahead, which verifies the feasibility and effectiveness of this method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Li, J., Sun, A., Ma, Y.: Neural named entity boundary detection. IEEE Trans. Knowl. Data Eng. 33(4), 1790–1795 (2020). https://doi.org/10.1109/TKDE.2020.2981329
Li, T.R., Liu, M.T., Zhang, Y.J., et al.: A Review of Entity Linking Research Based on Deep Learning. Acta Scientiarum Naturalium Universitatis Pekinensis 57(01), 91–98 (2021). https://doi.org/10.13209/j.0479-8023.2020.077
Zhang, T., Jia, Z., Li, T.R., et al.: Open-domain question-answering system based on large-scale knowledge base. CAAI Trans. Intell. Syst. 13(04), 557–563 (2018). https://doi.org/10.11992/tis.201707039
Zhu, M., Celikkaya, B., Bhatia, P., et al.: Latte: latent type modeling for biomedical entity linking. In: Proceedings of the AAAI Conference on Artificial Intelligence. 34(05), 9757–9764 (2020). https://doi.org/10.1609/aaai.v34i05.6526
Ji, S., Pan, S., Cambria, E., et al.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494–514 (2021). https://doi.org/10.1109/TNNLS.2021.3070843
Kusum, L., Pardeep, S., Kamlesh, D.: Mention detection in coreference resolution: survey. Appl. Intell.. Intell. 52(9), 9816–9860 (2022). https://doi.org/10.1007/s10489-021-02878-2
Mulang’, I O, Singh, K., Prabhu, C., et al.: Evaluating the impact of knowledge graph context on entity disambiguation models. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2157–2160 (2020). https://doi.org/10.1145/3340531.3412159
Daiber, J., Jakob, M., Hokamp, C., et al.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems, pp.121–124 (2013). https://doi.org/10.1145/2506182.2506198
Özge, S., Artem, S., Mikhail, A., Alexander, P., Chris, B.: Neural entity linking: a survey of models based on deep learning. Semantic Web 13(3), 527–570 (2022). https://doi.org/10.3233/SW-222986
Luo, A., Gao, S., Xu, Y.: Deep semantic match model for entity linking using knowledge graph and text. Proc. Comput. Sci. 129, 110–114 (2018). https://doi.org/10.1016/j.procs.2018.03.057
Martins, P.H., Marinho, Z., Martins, A.F.T.: Joint learning of named entity recognition and entity linking. arXiv preprint arXiv:1907.08243, 2019.https://doi.org/10.48550/arXiv.1907.08243
Fang, Z., Cao, Y., Li, R., et al.: High quality candidate generation and sequential graph attention network for entity linking. In: Proceedings of the Web Conference, pp. 640–650 (2020). https://doi.org/10.1145/3366423.3380146
Broscheit, S.: Investigating entity knowledge in BERT with simple neural end-to-end entity linking[J]. arXiv preprint arXiv:2003.05473. (2020)
Navigli, R., Velardi, P.: Structural semantic interconnections: a knowledge-based approach to word sense disambiguation. IEEE Trans. Pattern Anal. Mach. Intell. 27(7), 1075–1086 (2005). https://doi.org/10.1109/TPAMI.2005.149
Zan, H.Y.,Wu, Y.G., Jia, Y.X., et al.: Chinese Micro-blog named entity linking based on multiorigin knowledge. J. Shandong Univ.(Natural Science) 50(07), 9–16 (2015). https://doi.org/10.6040/j.issn.1671-9352.3.2014.026
Dredze, M., Mcnamee, P., Rao, D., et al.: Entity disambiguation for knowledge base population. In: International Conference on Computational Linguistics. Association for Computational Linguistics (2010)
Christophides, V., Efthymiou, V., Palpanas, T., et al.: An overview of end-to-end entity resolution for big data. ACM Comput. Surv. (CSUR) 53(6), 1–42 (2020)
Nicola De, C., Gautier, I., Sebastian, R., Fabio, P.: Autoregressive entity retrieval. In: International Conference on Learning Representations (2021b)
Lewis, M., Liu, Y., Goyal, N., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. (2019)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, 27 (2014). https://doi.org/10.5555/2969033.2969173
Evgeniy, G., Michael, R., Amarnag, S.: “FACC1: freebase annotation of ClueWeb corpora, Version 1 (Release date 2013–06–26, Format version 1, Correction level 0)”, June (2013)
Hoffart, J., Yosef, M.A., Bordino, I., et al.: Robust disambiguation of named entities in text. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 782–792 (2011)
Gupta, N., Singh, S., Roth, D.: Entity linking via joint encoding of types, descriptions, and context[C]//Proceedings of the. Conf. Empirical Methods Natural Lang. Process. 2017, 2681–2690 (2017). https://doi.org/10.18653/v1/D17-1284
Nicola De, C., Wilker, A., Ivan, T.: Highly parallel autoregressive entity linking with discriminative correction. arXiv preprint arXiv:2109.03792 (2021a)
Ravi, M.P.K., Singh, K., Mulang, I.O., et al.: CHOLAN: a modular approach for neural entity linking on Wikipedia and Wikidata. arXiv preprint arXiv:2101.09969 (2021)
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 Singapore Pte Ltd.
About this paper
Cite this paper
Yang, D., Lan, W. (2024). Multi-source Autoregressive Entity Linking Based on Generative Method. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_30
Download citation
DOI: https://doi.org/10.1007/978-981-99-9637-7_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9636-0
Online ISBN: 978-981-99-9637-7
eBook Packages: Computer ScienceComputer Science (R0)
