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020 ▼a 9780438277939
035 ▼a (MiAaPQ)AAI10842170
035 ▼a (MiAaPQ)columbia:14834
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 660
1001 ▼a Remolona, Miguel Francisco Miravite.
24510 ▼a HOLMES: A Hybrid Ontology-Learning Materials Engineering System.
260 ▼a [S.l.]: ▼b Columbia University., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 194 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Venkat Venkatasubramanian.
5021 ▼a Thesis (Ph.D.)--Columbia University, 2018.
520 ▼a Designing and discovering novel materials is challenging problem in many domains such as fuel additives, composites, pharmaceuticals, and so on. At the core of all this are models that capture how the different domain-specific data, information,
520 ▼a The HOLMES framework starts with journal articles that are in the Portable Document Format (PDF) and ends with the assignment of the entries in the journal articles into ontologies. While this might seem to be a simple task of information extrac
520 ▼a In the development of the information extraction tasks, we note that there are new problems that have not arisen in previous information extraction work in the literature. The first is the necessity to extract auxiliary information in the form o
520 ▼a In this work, the HOLMES framework is presented as a whole, describing our successful progress as well as unsolved problems, which might help future research on this topic. The ontology is then presented to help in the identification of the rele
590 ▼a School code: 0054.
650 4 ▼a Chemical engineering.
650 4 ▼a Computer science.
690 ▼a 0542
690 ▼a 0984
71020 ▼a Columbia University. ▼b Chemical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0054
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999830 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033