AAAI Publications, The Twenty-Ninth International Flairs Conference

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Propositionalization for Unsupervised Outlier Detection in Multi-Relational Data
Fatemeh Riahi, Oliver Schulte

Last modified: 2016-03-30

Abstract


We develop a novel propositionalization approach to unsupervised outlier detection for multi-relational data. Propositionalization summarizes the information from multi-relational data, that are typically stored in multiple tables, in a single data table. The columns in the data table represent conjunctive relational features that are learned from the data. An advantage of propositionalization is that it facilitates applying the many previous outlier detection methods that were designed for single-table data. We show that conjunctive features for outlier detection can be learned from data using statistical-relational methods. Specifically, we apply Markov Logic Network structure learning. Compared to baseline propositionalization methods, Markov Logic propositionalization produces the most compact data tables, whose attributes capture the most complex multi-relational correlations. We apply three representative outlier detection methods LOF, KNN, OutRank to the data tables constructed by propositionalization.

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