AI Forum: Lise Getoor/University of Maryland College Park Link Mining in ACES 6.304

Contact Name: 
Jenna Whitney
Date: 
Feb 10, 2006 11:00am - 12:00pm


There is a signup schedule for this event.

Speake

r Name/Affiliation: Lise Getoor/University of Maryland College Park
Talk Title: Link Mining

Date/Time: February 10 2006 at 11:00 a.

m.

Location: ACES 6.304

Host: Ray Mooney

Talk Abstra

ct:
A key challenge for data mining is tackling the problem
of minin

g richly structured datasets where the objects
are linked in some way.
Links among the objects may demonstrate
certain patterns which can be
helpful for many data mining
tasks and are usually hard to capture wit

h traditional statistical
models. Recently there has been a surge of in

terest in this
area fueled largely by interest in web and hypertext mi

ning
but also by interest in mining social networks security
and

law enforcement data bibliographic citations and epidemiological
recor

ds.

Link mining includes both descriptive and predictive modeling of link data. Classification and clustering in linked relational
doma

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

ing AAAI ICML IJCAI KDD SIGMOD
UAI VLDB and WWW.