Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Zhang, Yue; Wang, Cong; Soukaseum, Mya; Vlachos, Dionisios G.; Fang, Hui

doi:10.1021/acs.jcim.2c00359

Title: Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Journal Article · Thu Jun 30 00:00:00 EDT 2022 · Journal of Chemical Information and Modeling

DOI:https://doi.org/10.1021/acs.jcim.2c00359· OSTI ID:1977907

^[1];

^[1]; Soukaseum, Mya ^[2];

^[1]; Fang, Hui ^[1]

University of Delaware, Newark, DE (United States)
Drexel University, Philadelphia, PA (United States)

Valuable knowledge of catalysis is often hidden in a large amount of scientific literature. There is an urgent need to extract useful knowledge to facilitate scientific discovery. Here this work takes the first step toward the goal in the field of catalysis. Specifically, we construct the first information extraction benchmark data set that covers the field of catalysis and also develop a general extraction framework that can accurately extract catalysis-related entities from scientific literature with 90% extraction accuracy. We further demonstrate the feasibility of leveraging the extracted knowledge to help users better access relevant information in catalysis through an entity-aware search engine and a correlation analysis system.

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Research Organization:: Univ. of Delaware, Newark, DE (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0021166

OSTI ID:: 1977907

Journal Information:: Journal of Chemical Information and Modeling, Vol. 62, Issue 14; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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