@inproceedings{zhu-etal-2023-descriptive,
title = "Descriptive Knowledge Graph in Biomedical Domain",
author = "Zhu, Kerui and
Huang, Jie and
Chang, Kevin Chen-Chuan",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.42",
doi = "10.18653/v1/2023.emnlp-demo.42",
pages = "462--470",
abstract = "We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences as a relational graph, enabling researchers to explore closely related biomedical entities (e.g., diseases treated by a chemical) or indirectly connected entities (e.g., potential drugs for treating a disease). Our system also uses ChatGPT and a fine-tuned relation synthesis model to generate concise and reliable descriptive sentences from retrieved information, reducing the need for extensive human reading effort. With our system, researchers can easily obtain both high-level knowledge and detailed references and interactively steer to the information of interest. We spotlight the application of our system in COVID-19 research, illustrating its utility in areas such as drug repurposing and literature curation.",
}
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%0 Conference Proceedings
%T Descriptive Knowledge Graph in Biomedical Domain
%A Zhu, Kerui
%A Huang, Jie
%A Chang, Kevin Chen-Chuan
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhu-etal-2023-descriptive
%X We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences as a relational graph, enabling researchers to explore closely related biomedical entities (e.g., diseases treated by a chemical) or indirectly connected entities (e.g., potential drugs for treating a disease). Our system also uses ChatGPT and a fine-tuned relation synthesis model to generate concise and reliable descriptive sentences from retrieved information, reducing the need for extensive human reading effort. With our system, researchers can easily obtain both high-level knowledge and detailed references and interactively steer to the information of interest. We spotlight the application of our system in COVID-19 research, illustrating its utility in areas such as drug repurposing and literature curation.
%R 10.18653/v1/2023.emnlp-demo.42
%U https://aclanthology.org/2023.emnlp-demo.42
%U https://doi.org/10.18653/v1/2023.emnlp-demo.42
%P 462-470
Markdown (Informal)
[Descriptive Knowledge Graph in Biomedical Domain](https://aclanthology.org/2023.emnlp-demo.42) (Zhu et al., EMNLP 2023)
ACL
- Kerui Zhu, Jie Huang, and Kevin Chen-Chuan Chang. 2023. Descriptive Knowledge Graph in Biomedical Domain. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 462–470, Singapore. Association for Computational Linguistics.