- Abstract:
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Chemists not only produce a significant amount of data-rich scholarly communication artifacts, but have also adopted a highly formulaic style of writing. The literature of this discipline is an attractive target for automated data extraction. In previous work, we have demonstrated the identification and extraction of chemical entities from scientific papers.[1][2] However, we have not addressed the extraction of the relationships linking the chemical entities to both each other as well as to ...
Expand abstract - Publication status:
- Not Published
- Peer review status:
- Peer reviewed
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- Author's Original
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(pdf, 611.7kb)
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- URN:
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uuid:400e170b-98d3-41ee-afcf-fff1918b0452
- Local pid:
- ora:3120
- Language:
- English
- Keywords:
- Subjects:
- Copyright holder:
- L.Hawizy et al.
- Copyright date:
- 2009
- Notes:
-
References
[1] S. E. Adams, J. M. Goodman, R. J. Kidd, A. D. McNaught, P. Murray-Rust, F. R. Norton, J. A. Townsend, and C. A. Waudby, “Experimental data checker: Better information for organic chemists,” Organic and Biomolecular Chemistry,
vol. 2, pp. 3067 –3070, 2004.
[2] P. Corbett and P. Murray-Rust, “High-throughput identification of chemistry in life science texts,” 2006, pp. 107–118. [Online]. Available: http://dx.doi.org/10.1007/11875741 11
[3] W. Consortium, “Rdf primer,” http://www.w3.org/TR/rdf-primer/ , last accessed: 07/08/09.
[4] ——, “Sparql query language for rdf,” http://www.w3.org/TR/rdf-sparql-query/ , last accessed: 07/08/09.
Journal article
Information extraction and linked open data in chemistry
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