LDR | | 02233nmm uu200409 4500 |
001 | | 000000333620 |
005 | | 20240805173359 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438206342 |
035 | |
▼a (MiAaPQ)AAI10749899 |
035 | |
▼a (MiAaPQ)rpi:11246 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 004 |
100 | 1 |
▼a Makni, Bassem. |
245 | 10 |
▼a Deep Learning for Noise-tolerant RDFS Reasoning. |
260 | |
▼a [S.l.] :
▼b Rensselaer Polytechnic Institute.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 189 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
500 | |
▼a Adviser: James A. Hendler. |
502 | 1 |
▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018. |
520 | |
▼a 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 |
520 | |
▼a 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 |
520 | |
▼a 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 |
520 | |
▼a 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. |
590 | |
▼a School code: 0185. |
650 | 4 |
▼a Computer science. |
690 | |
▼a 0984 |
710 | 20 |
▼a Rensselaer Polytechnic Institute.
▼b Computer Science. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0185 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997074
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |