Name
Abstract | ||
|---|---|---|
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. 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1021/acs.jcim.2c00359 | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
DocType | Volume | Issue |
Journal | 62 | 14 |
ISSN | Citations | PageRank |
1549-9596 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zhang | 1 | 0 | 0.34 |
| Cong Wang | 2 | 0 | 0.34 |
| Mya Soukaseum | 3 | 0 | 0.34 |
| Dionisios G. Vlachos | 4 | 0 | 1.01 |
| Hui Fang | 5 | 918 | 63.03 |