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Deep Learning for Noise-tolerant RDFS Reasoning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Deep Learning for Noise-tolerant RDFS Reasoning. |
| 개인저자 | Makni, Bassem. |
| 단체저자명 | Rensselaer Polytechnic Institute. Computer Science. |
| 발행사항 | [S.l.]: Rensselaer Polytechnic Institute., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 189 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438206342 |
| 학위논문주기 | Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018. |
| 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 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
