On the Beaten Path: Exploitation of Entities Interactions For Predicting Potential Link

Young-Woo Seo and Katia Sycara
Tech. Report, CMU-RI-TR-06-36, Robotics Institute, Carnegie Mellon University, August, 2006

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We propose a new non-parametric link analysis algorithm that predicts a potential link between entities given a set of different relational patterns. The proposed method first represents different types of relations among entities by constructing the corresponding number of factorized matrices from the original entity-by-relation matrices. The prediction of a possible link between entities is done by linearly summing the weighted distances in the latent spaces. A logistic regression is used to estimate regression coefficients of distances in the latent spaces. From the experimental comparisons with various algorithms, our algorithm performs best in precision and second-best in recall measure.

author = {Young-Woo Seo and Katia Sycara},
title = {On the Beaten Path: Exploitation of Entities Interactions For Predicting Potential Link},
year = {2006},
month = {August},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-06-36},
keywords = {link analysis, subgroup identification, machine learning},
} 2017-09-13T10:42:35-04:00