Document (#41554)

Author

Monireh, E.
Sarker, M.K.
Bianchi, F.
Hitzler, P.
Doran, D.
Xie, N.

Title

Reasoning over RDF knowledge bases using deep learning

Issue

Preprint.

Source

https://www.researchgate.net/publication/328899484_Reasoning_over_RDF_Knowledge_Bases_using_Deep_Learning

Year

2018

Abstract

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.

Content

Vgl.: arXiv:1811.04132v1 [cs.LG] 9 Nov 2018.

Theme

Wissensrepräsentation
Semantic Web

Object

RDF

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5. Eklund. P.W.: Logic-based networks : concept graphs and conceptual structures (2000) 0.13

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