Foundations of statistical natural language processing
Foundations of statistical natural language processing
Ideal Refinement of Datalog Programs
LOPSTR '95 Proceedings of the 5th International Workshop on Logic Programming Synthesis and Transformation
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
Journal of Artificial Intelligence Research
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Relational Transformation-based Tagging for Activity Recognition
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Probabilistic inductive logic programming
Trading expressivity for efficiency in statistical relational learning: Ph.D. thesis abstract
ACM SIGKDD Explorations Newsletter
Relational Transformation-based Tagging for Activity Recognition
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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We introduce relational grams (r-grams). They upgrade n-grams for modeling relational sequences of atoms. As n-grams, r-grams are based on smoothed n-th order Markov chains. Smoothed distributions can be obtained by decreasing the order of the Markov chain as well as by relational generalization of the r-gram. To avoid sampling object identifiers in sequences, r-grams are generative models at the level of variablized sequences with local object identity constraints. These sequences define equivalence classes of ground sequences, in which elements are identical up to local identifier renaming. The proposed technique is evaluated in several domains, including mobile phone communication logs, Unix shell user modeling, and protein fold prediction based on secondary protein structure.