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On the Beaten Path: Exploitation of Entities Interactions For Predicting Potential Link
Y. Seo and K. 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.

Associated center: CIMDS
Associated lab/group: Intelligent Software Agents

Y. Seo and K. Sycara, On the Beaten Path: Exploitation of Entities Interactions For Predicting Potential Link, tech. report CMU-RI-TR-06-36, Robotics Institute, Carnegie Mellon University, August, 2006.

@techreport{Seo_2006_5513,
   author = "Young-Woo Seo and Katia Sycara",
   title = "On the Beaten Path: Exploitation of Entities Interactions For Predicting Potential Link",
   institution = "Robotics Institute, Carnegie Mellon University",
   month = "August",
   year = "2006",
   number = "CMU-RI-TR-06-36",
   address = "Pittsburgh, PA"
}