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English Information

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Authors
# Name
1 Otávio Xavier(otaviocx@ufg.br)
2 Anderson Soares(andersonsoares@ufg.br)

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Reference
# Reference
1 Allemang, D. and Hendler, J. (2011). Semantic web for the working ontologist: effective modeling in RDFS and OWL. Elsevier.
2 Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., and Vrgoc, D. (2017). Foundations of modern query languages for graph databases. ACM Computing Surveys (CSUR), 50(5):1–40.
3 Angles, R. and Gutierrez, C. (2008). Survey of graph database models. ACM Computing Surveys (CSUR), 40(1):1–39.
4 Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26.
5 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
6 Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
7 Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., and Wang, H. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
8 Henderson, M., Al-Rfou, R., Strope, B., Sung, Y.-H., Lukács, L., Guo, R., Kumar, S., Miklos, B., and Kurzweil, R. (2017). Efficient natural language response suggestion for smart reply. arXiv preprint arXiv:1705.00652.
9 Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., and Zimmermann, A. (2021). Knowledge graphs. ACM Comput. Surv., 54(4).
10 Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. (2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474.
11 Manning, C. D. (2008). Introduction to information retrieval. Syngress Publishing,.
12 Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
13 Graph database applications and concepts with neo4j. In Proceedings of the southern association for information systems conference, Atlanta, GA, USA, volume 2324, pages 141–147.
14 Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1):11–33.
15 Pandya, K. and Holia, M. (2023). Automating customer service using langchain: Building custom open-source gpt chatbot for organizations. arXiv preprint arXiv:2310.05421.
16 Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic web, 8(3):489–508.
17 Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543.
18 Robinson, I.,Webber, J., and Eifrem, E. (2015). Graph databases: new opportunities for connected data. " O’Reilly Media, Inc.".
19 Rogers, A., Kovaleva, O., and Rumshisky, A. (2021). A primer in bertology: What we know about how bert works. Transactions of the Association for Computational Linguistics, 8:842–866.
20 Suchanek, F. M., Kasneci, G., and Weikum, G. (2007). Yago: a core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web, WWW ’07, page 697–706, New York, NY, USA. Association for Computing Machinery.
21 A survey on application of knowledge graph. In Journal of Physics: Conference Series, volume 1487, page 012016. IOP Publishing.