TY - CONF T1 - On the Capabilities of Logic Tensor Networks for Deductive Reasoning T2 - AAAI Spring Symposium 2019 Y1 - 2019 A1 - Bianchi, Federico A1 - Pascal Hitzler JF - AAAI Spring Symposium 2019 ER - TY - JOUR T1 - Reasoning over RDF Knowledge Bases using Deep Learning JF - arXiv preprint arXiv:1811.04132 Y1 - 2018 A1 - Ebrahimi, Monireh A1 - Md Kamruzzaman Sarker A1 - Bianchi, Federico A1 - Xie, Ning A1 - Doran, Derek A1 - Pascal Hitzler AB -

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.

UR - https://arxiv.org/abs/1811.04132 ER -