Machine Learning
Efficient inference with cardinality-based clique potentials
Proceedings of the 24th international conference on Machine learning
Extending Markov Logic to Model Probability Distributions in Relational Domains
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Towards efficient sampling: exploiting random walk strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
P-CLASSIC: a tractable probablistic description logic
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Combining subjective probabilities and data in training markov logic networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects belonging to particular classes. Models are intended to be independent of the set of objects to which these principles can be applied, and it is assumed that the principles will soundly generalize across arbitrary sets of objects. In this paper, we point out limitations of models that seek to represent the corresponding principles with a fixed set of parameters and discuss the conditions under which the soundness of fixed parameters is indeed questionable. We propose a novel representation formalism called adaptive Markov logic networks to allow more flexible representations of relational domains, which involve parameters that are dynamically adjusted to fit the properties of an instantiation by phrasing the model's parameters as functions over attributes of the instantiation at hand. We empirically demonstrate the value of our learning and representation system on a simple but well-motivated example domain.