# Exploring Markov Logic Networks for Question Answering

@inproceedings{Khot2015ExploringML, title={Exploring Markov Logic Networks for Question Answering}, author={Tushar Khot and Niranjan Balasubramanian and Eric Gribkoff and Ashish Sabharwal and Peter Clark and Oren Etzioni}, booktitle={Conference on Empirical Methods in Natural Language Processing}, year={2015} }

Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. [] Key Method First, we simply use the extracted science rules directly as MLN clauses and exploit the structure present in hard constraints to improve tractability. Second, we interpret science rules as describing prototypical entities, resulting in a drastically simplified but brittle network.

## 38 Citations

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Ground-explanations are extracted from importance weights defined over the MLN formulas that encode the contribution of formulas towards the final inference results and are richer than state-of-the-art non-relational explainers such as LIME.

### A Study of Automatically Acquiring Explanatory Inference Patterns from Corpora of Explanations: Lessons from Elementary Science Exams

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