Abstract:Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to firstorder formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods ar… Show more
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