I read the paper “Unsupervised Semantic Parsing” by Hoifung Poon and Pedro Domingos appeared in EMNLP 2009. The paper talks about the learning the semantic representations in the unsupervised setting, utilizing Markov Logic. My problem with the paper is that it does not give enough examples to illustrate what is actually going on, under the hood of the Markov Logic rules which are templates of features. My guess about the inference is that given the QLF and the learned clusters, the QLF are assigned to the clusters, and during learning, the inference step is first find a possible clustering through search, and then evaluate the MAP assignment probability for the clustering, and use the assignment for the parameter estimation. The clusters which are represented as constants are the things being searched. The paper argues that it handles the variations in the syntactics given the same semantic representation.
About the focus paper, I feel it has the same problem as the related paper, it is not evaluating directly on the task that it claims to solve. It would be better if it could evaluate on some ontology test set. It gives me a feeling it is just a small extension of USP to make it do IE better.