Non-Parametric Learning of Gaifman Models

We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base. These relational features are first-order rules that are then partially grounded and counted over local neighborhoods of a Gaifman model to obtain the feature representations. We propose a method for learning these relational features for a Gaifman model by using relational tree distances. Our empirical evaluation on real data sets demonstrates the superiority of our approach over classical rule-learning.

[1]  Ben Taskar,et al.  Introduction to statistical relational learning , 2007 .

[2]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[3]  Martin Grohe,et al.  An existential locality theorem , 2004, Ann. Pure Appl. Log..

[4]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[5]  Xinlei Chen,et al.  Never-Ending Learning , 2012, ECAI.

[6]  Kevin Chen-Chuan Chang,et al.  A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications , 2017, IEEE Transactions on Knowledge and Data Engineering.

[7]  H. Gaifman.  On Local and Non-Local Properties , 1982 .

[8]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[9]  Christopher Ré,et al.  Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS , 2011, Proc. VLDB Endow..

[10]  Tommi S. Jaakkola,et al.  Learning Bayesian Network Structure using LP Relaxations , 2010, AISTATS.

[11]  Sriraam Natarajan,et al.  Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities. , 2018, Smart health.

[12]  Tom M. Mitchell,et al.  Random Walk Inference and Learning in A Large Scale Knowledge Base , 2011, EMNLP.

[13]  Ben Taskar,et al.  Link Prediction in Relational Data , 2003, NIPS.

[14]  Yoshihiro Yamanishi,et al.  Dual Convolutional Neural Network for Graph of Graphs Link Prediction , 2018, ArXiv.

[15]  Qiang Ji,et al.  Structure learning of Bayesian networks using constraints , 2009, ICML '09.

[16]  Fernando Berzal Galiano,et al.  A Survey of Link Prediction in Complex Networks , 2016, ACM Comput. Surv..

[17]  Pedro Larrañaga,et al.  Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Mathias Niepert.  Discriminative Gaifman Models , 2016, NIPS.

[19]  Luc De Raedt,et al.  ProbLog: A Probabilistic Prolog and its Application in Link Discovery , 2007, IJCAI.

[20]  Evgeniy Gabrilovich,et al.  A Review of Relational Machine Learning for Knowledge Graphs , 2015, Proceedings of the IEEE.

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

[22]  Luc De Raedt,et al.  Statistical Relational Artificial Intelligence: Logic, Probability, and Computation , 2016, Statistical Relational Artificial Intelligence.

[23]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[24]  Gautam Kunapuli,et al.  ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem , 2009 .

[25]  Matthew Richardson,et al.  The Alchemy System for Statistical Relational AI: User Manual , 2007 .

[26]  Lise Getoor,et al.  Probabilistic Similarity Logic , 2010, UAI.

[27]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[28]  Kristian Kersting,et al.  Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach , 2017, ILP.

[29]  Nilanjan Dey,et al.  Link prediction in co-authorship networks based on hybrid content similarity metric , 2017, Applied Intelligence.

[30]  Ni Lao,et al.  Relational retrieval using a combination of path-constrained random walks , 2010, Machine Learning.

[31]  Sriraam Natarajan,et al.  Relational One-Class Classification: A Non-Parametric Approach , 2014, AAAI.

[32]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[33]  Kristian Kersting,et al.  Learning Markov Logic Networks via Functional Gradient Boosting , 2011, 2011 IEEE 11th International Conference on Data Mining.

[34]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.