Statistical Relational Learning: Entity Resolution and Link Prediction

A key challenge for machine learning is mining richly structured datasets describing objects, their properties, and links among the objects. We'd like to be able to learn models which can capture both the underlying uncertainty and the logical relationships in the domain. Links among the objects may demonstrate certain patterns, which can be helpful for many practical inference tasks and are usually hard to capture with traditional statistical models. Recently there has been a surge of interest in this area, fueled in part by interest in mining social networks, web collections, security and law enforcement data and biological data.

Statistical Relational Learning (SRL) is a newly emerging research area which attempts to represent, reason and learn in domains with complex relational and rich probabilistic structure. In this talk, I'll begin with a short SRL overview. Then, I'll describe some of my group's recent work, focusing on our work on entity resolution and link prediction in relational domains.

Joint work with students: Indrajit Bhattacharya, Mustafa Bilgic, Rezarta Islamaj, Louis Licamele, Galileo Namata, Vivek Sehgal, Prithviraj Sen and Elena Zheleva.

Speaker Bio

Prof. Lise Getoor is an assistant professor in the Computer Science Department at the University of Maryland, College Park. She received her PhD from Stanford University in 2001. Her current work includes research on link mining, statistical relational learning and representing uncertainty in structured and semi-structured data. Her work in these areas has been supported by NSF, NGA, KDD, ARL and DARPA. In June 2006, she co-organized the fourth in a series of successful workshops on statistical relational learning, She has published numerous articles in machine learning, data mining, database and AI forums. She was one of 11 finalists choosen nationally for the 2005 Microsoft New Faculty Award. She is a member of AAAI Executive council, is on the editorial board of the Machine Learning Journal, is a JAIR associate editor and has served on numerous program committees including AAAI, ICML, IJCAI, KDD, SIGMOD, UAI, VLDB, and WWW.