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PKDD
2010
Springer

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models

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Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models
Abstract. A new method is proposed for compiling causal independencies into Markov logic networks (MLNs). An MLN can be viewed as compactly representing a factorization of a joint probability into the product of a set of factors guided by logical formulas. We present a notion of causal independence that enables one to further factorize the factors into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The causal independence lets us specify the factor in terms of weighted, directed clauses and operators, such as “or”, “sum” or “max”, on the contribution of the variables involved in the factors, hence combining both undirected and directed knowledge. Our experimental evaluations shows that making use of the finer-grain factorization provided by causal independence can improve quality of parameter learning in MLNs.
Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasa
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PKDD
Authors Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
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