Explicable knowledge graph (X-KG): generating knowledge graphs for explainable artificial intelligence and querying them by translating natural language queries to SPARQL-Reference-Cited by-同舟云学术

Explicable knowledge graph (X-KG): generating knowledge graphs for explainable artificial intelligence and querying them by translating natural language queries to SPARQL

Published:2024-01-21 Issue:3 Volume:16 Page:1605-1615
ISSN:2511-2104
Container-title:International Journal of Information Technology
language:en
Short-container-title:Int. j. inf. tecnol.

Author:

Shaikh Numair^ORCID,Chauhan Tavishee,Patil Jayesh,Sonawane Sheetal

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s41870-023-01692-y.pdf

Reference20 articles.

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2. Wang Q, Li M, Wang X, et al (2021) COVID-19 literature knowledge graph construction and drug repurposing report generation. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations. Association for Computational Linguistics, Online, pp 66–77, https://doi.org/10.18653/v1/2021.naacl-demos.8

3. Kejriwal M (2019) Domain-specific knowledge graph construction. Springer. https://doi.org/10.1007/978-3-030-12375-8

4. Li L, Wang P, Yan J et al (2020) Real-world data medical knowledge graph: construction and applications. Artificial Intelligence in Medicine 103:101817 https://doi.org/10.1016/j.artmed.2020.101817, www.sciencedirect.com/science/article/pii/S0933365719309546

5. Ye H, Zhang N, Chen H, et al (2022) Generative knowledge graph construction: A review. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, pp 1–17, https://doi.org/10.18653/v1/2022.emnlp-main.1

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