ins require new data mining models and algorithms. Furthermore
with th

e introduction of links new predictive tasks come
to light. Examples i

nclude predicting the numbers of links
predicting the type of link bet

ween two objects inferring
the existence of a link inferring the iden

tity of an object
finding co-references and discovering subgraph patt

erns.

In this talk I will give an overview of this newly emerging <

br>research area. I will describe novel aspects of the modeling
learni

ng and inference tasks. Then as time permits I will
describe some of

my group''s recent work on link-based classification
and entity resolut

ion in linked data.

Speaker Bio:
Prof. Lise Getoor is an assist

ant professor in the Computer
Science Department at the University of M

aryland College
Park. She received her PhD from Stanford University in
2001.
Her current work includes research on link mining statistical <

br>relational learning and representing uncertainty in structured
and s

emi-structured data. Her work in these areas has been
supported by NSF
NGA KDD ARL and DARPA. In July 2004
she co-organized the third in a
series of successful workshops
on statistical relational learning htt

p://www.cs.umd/srl2004.
She has published numerous articles in machine

learning
data mining database and AI forums. She is a member of
A

AAI Executive council is on the editorial board of the
Machine Learnin

g Journal and JAIR and has served on numerous
program committees includ