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  <title><![CDATA[Interpretable Explanations for Probabilistic Inference in Markov Logic]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1035/Interpretable-Explanations-for-Probabilistic-Inference-in-Markov-Logic</link>
  <description><![CDATA[Markov Logic Networks (MLNs) represent relational knowledge using a combination of first-order logic and probabilistic models. In this paper, we develop an approach to explain the results of probabilistic inference in MLNs. Unlike approaches such as LIME and SHAP that explain black-box classifiers, e explaining MLN inference is harder since the data is interconnected. We develop an explanation framework that computes importance weights for MLN formulas based on their influence on the marginal...]]></description>
  <dc:date>2021-12-15</dc:date>
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