An ultimate goal of AI is to build end-to-end systems that interpret natural language, reason over the resulting logical forms, and perform actions based on that reasoning. This requires systems from separate fields be brought together, but often this exposes representational gaps between them. The logical forms from a language interpreter may mirror the surface forms of utterances too closely to be usable as-is, given a reasoner's requirements for knowledge representations. What is needed is a system that can match logical forms to background knowledge flexibly to acquire a rich semantic model of the speaker's goal. In this paper, we present such a ``matcher" that uses semantic transformations to overcome structural differences between the two representations. We evaluate this matcher in a MUC-like template-filling task and compare its performance to that of two similar systems.

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In Proceedings of Third International Conference on Knowledge Capture 2005.

@inproceedings{yeh:kc05, title={Matching Utterances to Rich Knowledge Structures to Acquire a Model of the Speaker's Goal}, author={Peter Yeh and Bruce Porter and Ken Barker}, booktitle={Proceedings of Third International Conference on Knowledge Capture}, url="http://www.cs.utexas.edu/users/ai-lab?yeh:kc05", year={2005} }