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NMT4RDFS reviews and mentions
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GDlog: A GPU-Accelerated Deductive Engine
https://www.researchgate.net/figure/Parameter-Settings-of-th...
"Deep learning for noise-tolerant RDFS reasoning" (2018) > NMT4RDFS: http://www.semantic-web-journal.net/content/deep-learning-no... :
> This paper documents a novel approach that extends noise-tolerance in the SW to full RDFS reasoning. Our embedding technique— that is tailored for RDFS reasoning— consists of layering RDF graphs and encoding them in the form of 3D adjacency matrices where each layer layout forms a graph word. Each input graph and its entailments are then represented as sequences of graph words, and RDFS inference can be formulated as translation of these graph words sequences, achieved through neural machine translation. Our evaluation on LUBM1 synthetic dataset shows 97% validation accuracy and 87.76% on a subset of DBpedia while demonstrating a noise-tolerance unavailable with rule-based reasoners.
NMT4RDFS: https://github.com/Bassem-Makni/NMT4RDFS
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A human-generated review article with an emphasis on standards; with citations to summarize:
"Why do we need SWRL and RIF in an OWL2 world?" [with SPARQL CONSTRUCT, SPIN, and now SHACL]
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Bassem-Makni/NMT4RDFS is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of NMT4RDFS is Python.
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