Abstract
Within the machine learning and data mining communities, there has been a growing interest in learning structured models from input data that is itself structured or semi-structured. Graph identification refers to methods that transform observational data described as a noisy input graph into an inferred, “clean” information graph. Examples include inferring social networks from online, noisy, communication data, identifying gene regulatory networks from protein-protein interactions, and extracting semantic graphs from noisy and ambiguous co-occurrence information. Some of the key processes in graph identification are: entity resolution, collective classification, and link prediction. I will overview algorithms for these tasks, discuss the need for integrating the methods to solve the overall problem jointly. Time permitting, I will also give quick overviews of some of the other research projects in my group.
Biography
Lise Getoor is an associate 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. She has also done work on social network analysis and visual analytics. She has published numerous articles in machine learning, data mining, database, and artificial intelligence forums. She was awarded an NSF Career Award, is an action editor for the Machine Learning Journal, is a JAIR associate editor, has been a member of AAAI Executive council, and has served on a variety of program committees including AAAI, ICML, IJCAI, ISWC, KDD, SIGMOD, UAI, VLDB, and WWW.