Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

Tom Hope; Aida Amini; David Wadden; Madeleine van Zuylen; Sravanthi Parasa; Eric Horvitz; Daniel Weld; Roy Schwartz; Hannaneh Hajishirzi

doi:10.18653/v1/2021.naacl-main.355

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

Tom Hope
, Aida Amini
, David Wadden
, Madeleine van Zuylen
, Sravanthi Parasa
, Eric Horvitz
, Daniel Weld
, Roy Schwartz
, Hannaneh Hajishirzi

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

Abstract

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.

Original language	English
Title of host publication	NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publication	Human Language Technologies, Proceedings of the Conference
Publisher	Association for Computational Linguistics (ACL)
Pages	4489-4503
Number of pages	15
ISBN (Electronic)	9781954085466
DOIs	https://doi.org/10.18653/v1/2021.naacl-main.355
State	Published - 2021
Event	2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021 - Virtual, Online Duration: 6 Jun 2021 → 11 Jun 2021

Publication series

Name	NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Conference

Conference	2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021
City	Virtual, Online
Period	6/06/21 → 11/06/21

Bibliographical note

Publisher Copyright:
© 2021 Association for Computational Linguistics.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.18653/v1/2021.naacl-main.355

Cite this

Hope, T., Amini, A., Wadden, D., van Zuylen, M., Parasa, S., Horvitz, E., Weld, D., Schwartz, R., & Hajishirzi, H. (2021). Extracting a Knowledge Base of Mechanisms from COVID-19 Papers. In NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 4489-4503). (NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.naacl-main.355

Hope, Tom ; Amini, Aida ; Wadden, David et al. / Extracting a Knowledge Base of Mechanisms from COVID-19 Papers. NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2021. pp. 4489-4503 (NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference).

@inproceedings{bf502fc57fb34776a521272fd12696aa,

title = "Extracting a Knowledge Base of Mechanisms from COVID-19 Papers",

abstract = "The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.",

author = "Tom Hope and Aida Amini and David Wadden and \{van Zuylen\}, Madeleine and Sravanthi Parasa and Eric Horvitz and Daniel Weld and Roy Schwartz and Hannaneh Hajishirzi",

note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021 ; Conference date: 06-06-2021 Through 11-06-2021",

year = "2021",

doi = "10.18653/v1/2021.naacl-main.355",

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series = "NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference",

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Hope, T, Amini, A, Wadden, D, van Zuylen, M, Parasa, S, Horvitz, E, Weld, D, Schwartz, R & Hajishirzi, H 2021, Extracting a Knowledge Base of Mechanisms from COVID-19 Papers. in NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference. NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, Association for Computational Linguistics (ACL), pp. 4489-4503, 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Virtual, Online, 6/06/21. https://doi.org/10.18653/v1/2021.naacl-main.355

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers. / Hope, Tom; Amini, Aida; Wadden, David et al.
NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2021. p. 4489-4503 (NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hope, Tom

AU - Amini, Aida

AU - Wadden, David

AU - van Zuylen, Madeleine

AU - Parasa, Sravanthi

AU - Horvitz, Eric

AU - Weld, Daniel

AU - Schwartz, Roy

AU - Hajishirzi, Hannaneh

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AB - The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.

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ER -

Hope T, Amini A, Wadden D, van Zuylen M, Parasa S, Horvitz E et al. Extracting a Knowledge Base of Mechanisms from COVID-19 Papers. In NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2021. p. 4489-4503. (NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference). doi: 10.18653/v1/2021.naacl-main.355

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

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