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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Abstract
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.

Keywords
link analysis, subgroup identification, machine learning

Notes
Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems
Associated Lab(s) / Group(s): Advanced Agent - Robotics Technology Lab

Text Reference
Young-Woo Seo and Katia 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

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