Bottom-up learning of Markov logic network structure
Proceedings of the 24th international conference on Machine learning
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
Bayesian knowledge corroboration with logical rules and user feedback
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Learning statistical models from relational data
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
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Statistical machine learning is in the midst of a “relational revolution”. After many decades of focusing on independent and identically-distributed (iid) examples, many researchers are now studying problems in which the examples are linked together into complex networks. These networks ca be a simple as sequences and 2-D meshes (such as those arising in part-of-speech tagging and remote sensing) or as complex as citation graphs, the world wide web, and relational data bases. Statistical relational learning raises many new challenges and opportunities. Because the statistical model depends on the domain's relational structure, parameters in the model are often tied. This has advantages for making parameter estimation feasible, but complicates the model search. Because the “features” involve relationships among multiple objects, there is often a need to intelligently construct aggregates and other relational features. Problems that arise from linkage and autocorrelation among objects must be taken into account. Because instances are linked together, classification typically involves complex inference to arrive at “collective classification” in which the labels predicted for the test instances are determined jointly rather than individually. Unlike iid problems, where the result of learning is a single classifier, relational learning often involves instances that are heterogeneous, where the result of learning is a set of multiple components (classifiers, probability distributions, etc.) that predict labels of objects and logical relationships between objects.