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Overview of Relational Markov Networks Colin Evans.

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Presentation on theme: "Overview of Relational Markov Networks Colin Evans."— Presentation transcript:

1 Overview of Relational Markov Networks Colin Evans

2 Relational Schema

3 Relational Data – an Instantiation

4 Problem How do we find an optimal assignment of labels I.y to a set of variables I.x in a specific instantiation I? We need an objective function p(I.y|I.x) that indicates the quality of an assignment of labels. The objective function should take into account the relational structure of the data.

5 Relational Clique Templates A template C is an algorithm for selecting a subset of nodes requiring labels – essentially a logical query. –“Find all label attributes A, B with pages C, D where C hasLabel A and D hasLabel B and C linksTo D” A specific subset is called a clique, I.x c. An potential function f c (I.y c |I.x c ) where I.y c is a subset of I.y is associated with the template. An example potential function for the above template: –A=B → 1 –A≠B → 0

6 Markov Networks Given a set of templates C and a set of cliques C(I), we can construct a Markov Network by connecting each of the cliques.

7 Markov Networks Given a Markov Network, we have the following JPD:

8 Potential Functions A weight w c associated with each clique template C is inserted to balance the contribution of each potential function. These weights need to be learned.

9 How do we learn the weights? Gradient Descent Perceptron Learning Other optimization methods

10 How do we find an optimal labeling? Modeling the relationships of the cliques as a Markov Network and use the sum-product algorithm. –Problem: sum-product is only proven to converge on trees.

11 Other Issues How does this method work if you have a large body of “correctly” labeled relational data and only wish to apply a small number of labels? –Email classification is a good example of this. –Complexity of assigning a label goes down, but we still use relationships to determine the label. Could one template “dominate” in a specific data set? Does there need to be a factor which normalizes the contribution of a template if it produces too many cliques? How do you test the “usefulness” of a template?


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