Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Relational Markov networks (RMNs) are a joint probabilistic model for an entire collection of related entities. The model is able to mine relational data effectively by integrating information from content attributes of individual entities as well as the links among them, yet the prediction accuracy is greatly affected by the definition of the relational clique templates. Maximum likelihood estimation (MLE) is used to estimate the model parameters, but this can be quite costly because multiple rounds of approximate inference are required over the entire dataset. In this paper, we propose constructing RMNs basing on the community structures of complex networks, and present a discriminative maximum pseudolikelihood estimation (DMPLE) approach for training RMNs. Experiments on the collective classification and link prediction tasks on some real-world datasets show that our approaches perform well in terms of accuracy and efficiency.