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Deep Learning for Noise-tolerant RDFS Reasoning
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Makni, Bassem. |
| 단체저자명 | Rensselaer Polytechnic Institute. Computer Science. |
| 서명/저자사항 | Deep Learning for Noise-tolerant RDFS Reasoning. |
| 발행사항 | [S.l.] : Rensselaer Polytechnic Institute., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 189 p. |
| 소장본 주기 | School code: 0185. |
| ISBN | 9780438206342 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: James A. Hendler. |
| 요약 | Since the introduction of the Semantic Web vision in 2001 as an extension to the Web, where machines can reason about the Web content, the main research focus in semantic reasoning was on the soundness and completeness of the reasoners. While th |
| 요약 | Recent research work on semantic reasoning with noise-tolerance focuses on type inference and does not aim for full RDF Schema (RDFS) reasoning. This thesis documents a novel approach that takes previous research efforts in noise-tolerance in th |
| 요약 | This thesis aims to provide a stepping stone towards bridging the Neural-Symbolic gap, specifically targeting the Semantic Web field and RDFS reasoning in particular. This is accomplished through layering Resource Description Framework (RDF) gr |
| 요약 | The evaluation confirms that deep learning can in fact be used to learn RDFS rules from both synthetic as well as real-world Semantic Web data while showing noise-tolerance capabilities as opposed to rule-based reasoners. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14997074 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00027947 | 004 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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