Ontology Reasoning with Deep Neural Networks (Extended Abstract)

The ability to conduct logical reasoning is a fundamental aspect of intelligent human behavior, and thus an important problem along the way to humanlevel artificial intelligence. Traditionally, logic-based symbolic methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human logical reasoning qualities. More recently, however, there has been an increasing interest in using machine learning rather than logic-based symbolic formalisms to tackle these tasks. In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform logical reasoning in the form of basic ontology reasoning.

[1]  B. Motik,et al.  RDFox: A Highly-Scalable RDF Store , 2015, SEMWEB.

[2]  Kees Middelburg,et al.  A Survey of Paraconsistent Logics , 2011, ArXiv.

[3]  Philip D. Butcher,et al.  Comparative and Functional Genomics , 2002, Comparative and Functional Genomics.

[4]  Tim Rocktäschel,et al.  End-to-end Differentiable Proving , 2017, NIPS.

[5]  Kaile Su,et al.  Symbolic manipulation based on deep neural networks and its application to axiom discovery , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[6]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[7]  Jason Weston,et al.  End-To-End Memory Networks , 2015, NIPS.

[8]  Jens Lehmann,et al.  DBpedia - A crystallization point for the Web of Data , 2009, J. Web Semant..

[9]  Yang Yu,et al.  Tunneling Neural Perception and Logic Reasoning through Abductive Learning , 2018, ArXiv.

[10]  Zhendong Mao,et al.  Knowledge Graph Embedding: A Survey of Approaches and Applications , 2017, IEEE Transactions on Knowledge and Data Engineering.

[11]  Alexa T. McCray,et al.  An Upper-Level Ontology for the Biomedical Domain , 2003, Comparative and functional genomics.

[12]  Peter A. Flach,et al.  Advances in Neural Information Processing Systems 28 , 2015 .

[13]  Jason Weston,et al.  Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks , 2015, ICLR.

[14]  A. Sayed,et al.  Foundations and Trends ® in Machine Learning > Vol 7 > Issue 4-5 Ordering Info About Us Alerts Contact Help Log in Adaptation , Learning , and Optimization over Networks , 2011 .

[15]  Maurizio Lenzerini,et al.  Inconsistency-Tolerant Semantics for Description Logics , 2010, RR.

[16]  James Hendler,et al.  Deep learning for noise-tolerant RDFS reasoning , 2019, Semantic Web.

[17]  Guillaume Bouchard,et al.  On Approximate Reasoning Capabilities of Low-Rank Vector Spaces , 2015, AAAI Spring Symposia.

[18]  Plamen Angelov,et al.  Proceedings of the 2013 International Joint Conference on Neural Networks , 2013 .

[19]  Artur S. d'Avila Garcez,et al.  Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge , 2016, NeSy@HLAI.

[20]  Richard Evans,et al.  Can Neural Networks Understand Logical Entailment? , 2018, ICLR.

[21]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[22]  Thomas G. Dietterich,et al.  In Advances in Neural Information Processing Systems 12 , 1991, NIPS 1991.

[23]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[24]  Alessandra Russo,et al.  DeepLogic: End-to-End Logical Reasoning , 2018, ArXiv.

[25]  Umberto Straccia,et al.  Proceedings of the 6th international conference on Web Reasoning and Rule Systems , 2012 .

[26]  Zoubin Ghahramani,et al.  Proceedings of the 24th international conference on Machine learning , 2007, ICML 2007.

[27]  Luc De Raedt,et al.  DeepProbLog: Neural Probabilistic Logic Programming , 2018, BNAIC/BENELEARN.

[28]  Pascal Hitzler,et al.  Reasoning over RDF Knowledge Bases using Deep Learning , 2018, ArXiv.