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In this work, we present a novel approach to ontology reasoning that is based on deep learning rather than logic-based formal reasoning.
Recursive distributed representations
Jordan B. Pollack · 1990
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Learning Task-Dependent Distributed Representations by Backpropagation Through Structure
Christoph Goller and Andreas Küchler · 1996
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LUBM: A benchmark for OWL knowledge base systems
Yuanbo Guo, Zhengxiang Pan, and Jeff Heflin · 2005
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Towards a Complete OWL Ontology Benchmark
Li Ma, Yang Yang, Zhaoming Qiu, Guotong Xie, Yue Pan, and Shengping Liu · 2006
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The Description Logic Handbook: Theory, Implementation, and Applications
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Introduction to Statistical Relational Learning
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Can recursive neural tensor networks learn logical reasoning?
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Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems
Boris Motik, Yavor Nenov, Robert Piro, Ian Horrocks, and Dan Olteanu · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches
Evgeniy Gabrilovoch, Ramanathan Guha, Andrew McCallum, and Kevin Murphy, editors · 2015
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Yavor Nenov, Robert Piro, Boris Motik, Ian Horrocks, Zhe Wu, and Jay Banerjee · 2015
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A Review of Relational Machine Learning for Knowledge Graphs
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Richard Socher, Danqi Chen, Christopher D. Manning, and Andrew Y. Ng · 2013
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Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich · 2016
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Logic tensor networks: Deep learning and logical reasoning from data and knowledge
Luciano Serafini and Artur d’Avila Garcez · 2016
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