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020 ▼a 9780438206342
035 ▼a (MiAaPQ)AAI10749899
035 ▼a (MiAaPQ)rpi:11246
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Makni, Bassem.
24510 ▼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.
5021 ▼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
71020 ▼a Rensselaer Polytechnic Institute. ▼b Computer Science.
7730 ▼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
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997074 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자