This document describes the University of Texas at Austin 2014 system for the Knowledge Base Population (KBP) English Slot Filling (SF) task. The UT Austin system builds upon the output of an existing relation extractor by augmenting relations that are explicitly stated in the text with ones that are inferred from the stated relations using probabilistic rules that encode commonsense world knowledge. Such rules are learned from linked open data and are encoded in the form of Bayesian Logic Programs (BLPs), a statistical relational learning framework based on directed graphical models. In this document, we describe our methods for learning these rules, estimating their associated weights, and performing probabilistic and logical inference to infer unseen relations. Although our system was able to infer additional correct relations that were not extracted by our baseline relation extraction system, we were unable to significantly outperform a pure extraction baseline.

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In Proceedings of the Seventh Text Analysis Conference: Knowledge Base Population (TAC 2014) 2014.

@inproceedings{bentor:tac14, title={University of Texas at Austin KBP 2014 Slot Filling System: Bayesian Logic Programs for Textual Inference}, author={Yinon Bentor and Vidhoon Viswanathan and Raymond Mooney }, booktitle={Proceedings of the Seventh Text Analysis Conference: Knowledge Base Population (TAC 2014)}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127532", year={2014} }