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HOLMES: A Hybrid Ontology-Learning Materials Engineering System
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | HOLMES: A Hybrid Ontology-Learning Materials Engineering System. |
| 개인저자 | Remolona, Miguel Francisco Miravite. |
| 단체저자명 | Columbia University. Chemical Engineering. |
| 발행사항 | [S.l.]: Columbia University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 194 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438277939 |
| 학위논문주기 | Thesis (Ph.D.)--Columbia University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Venkat Venkatasubramanian. |
| 요약 | 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, |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Chemical engineering. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
