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Urbana-Champaign, May12, 2012

INARC I3.2 Mid-Year ReportI3.2: Modeling and Mining of Text-Rich Information Networks

Dan Roth (Task Co-lead) Jiawei Han (Task Co-Lead)

Heng Ji (CUNY) Xifeng Yan (UCSB)

University of Illinois at Urbana-ChampaignNS-CTA: INARC

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I3.2: Modeling and Mining ofText-Rich Information Networks

Key Objectives:

Structurally model a text-rich info. network and investigatemethods for mining knowledge from such networks

Enhance keyword search and knowledge discovery capabilityby the text-rich info. network model

Deliverables:

Q1: Methodologies for modeling and construction of multi-dimensional, relatively structured info. networks byprogressive info. network analysis

Q2: Models for enhanced text data analysis using relativelystructured, heterogeneous info. networks

Q3: Methods for multi-facet search in text-rich info. networksQ4: System prototype demo of the approaches

Impact:

Modeling, principles, and methodologies developed for text-rich info. networks will lead to more relevant query results

Key Technical Innovations:

Exploitation of mostly unstructured data from reportsalong with some relatively structured metadata andthe links (e.g., hyperlinks) between reports to discover

key entities associated to a given query. Theexploitation builds upon semantic processing (e.g.,topic modeling), network analysis (e.g, iTopics) anddata mining (e.g., topic/text cubes) technologies

Efficient algorithms to enrich text mining techniqueswith the info. network topology

Information trustworthiness analysis in text-rich info.networks and other text-rich networks

Role Researchers

Lead D. Roth, UIUC (INARC)

Lead J. Han, UIUC (INARC)

Primary H. Ji, CUNY (INARC)

Primary X. Yan, UCSB (INARC)

Collaborators N. Chawla, Notre Dame (SCNARC) (linked with E2.3)

J. J. Garcia-Luna-Aceves, UCSC (CNARC)

M. Magdon-Ismail, RPI (SCNARC) (linked with S2.1)

Z. Wen, IBM (SCNARC)

Total $322K

2

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3

Text-Rich Information Networks: Combining contents & network

Focused on large heterogeneous information networks Collections ofnews articles from diverse resources, blogs and forums

Wikipedia, an information network consisting ofstructured and

unstructured data

Developed State-of-the-art algorithmic tools

Supporting knowledge acquisition, information extraction, text modeling

and integrated information structure discovery

Utilizing deep text analysis & large scale statistical models over the

content and the structure of the network

Make use of both explicit network structure and hidden ontological

structure (e.g., category structure)

Advanced our understanding of how to:

Acquire and extract information from heterogeneous information

networks when data is noisy, volatile, uncertain, and incomplete

3

Advancing the

State-of-the-Art ofNetwork Science

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Subtask3: Multi-Facet

Search

4

Subtask 1: Modeling and construction of multi-dimensional, relatively

structured information networks by integrated text and information analysis

4

Overall Task Organization

Subtask2: Topic

Modeling and

Discovery with

InfoNet

Subtask 3: Multi-facet search in

text-rich information networks

Subtask 2: Enhanced text

data analysis usingrelatively structured,

heterogeneous

information networks

Subtask1: Text-richInfoNet

Construction

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5

Subtask 1: Modeling and construction of multi-dimensional, relatively

structured information networks by integrated text and informationanalysis

Explicitly capture the interplay between textual topics and network

structure

Subtask 2: Enhanced text data analysis using relatively structured,heterogeneous information networks

Novel theories and methods to make text data and information network

mutually enhance each other in text understanding and information

analysis

Subtask 3: Multi-facet search in text-rich information networks

Exploring effective methods for search and mining in text-rich

information networks

5

Novelty Claims

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6

Modeling and construction of multi-dimensional, relatively structured

information networks by integrated text and information analysis

Data Fusion and Information Network Fusion: Web structure mining for

integration of web data with info. networks *WWW11, SIGMOD11 demo+

Wikification (integration of wikipedia for entity/concept resolution) *ACL11+

Enrichment & disambiguation of information network

Dynamic Acquisition of Taxonomic Relations Network *EMNLP10+

Leverage Semantic Information Network to Enhance Entity Co-reference

Resolution and Entity Identification [ACL-HLT11]

Micro and Macro Collaborative Networks Ranking for Entity and EventCoreference Resolution *EMNLP2011SUB+

Markov Logic Networks and Learning-to-Rank to Enhance Open Domain Role

Discovery *TAC10+

6

Subtask 1: Text-Rich Network Modelingand Construction

G

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Growing Parallel Paths for WebStructure Mining

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Path

Result:

Tim Weninger, Fabio Fumarola, Cindy Xide Lin, Rick Barber, Jiawei Han, and Donato Malerba,

Growing Parallel Paths for Entity-Page Discovery, WWW'11, Mar. 2011 7

http://www.cs.uiuc.edu/homes/hanj/pdf/www11_twininger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/www11_twininger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/www11_twininger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/www11_twininger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/www11_twininger.pdf

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WinaCS: Web Information Network Analysisfor Computer Science

Web structure-guided information

extraction and integration Integration of DBLP information

networks

Integration of mined web structures

with DBLP networks for knowledge-

base construction

Supports intelligent querying & mining

Tim Weninger, Marina Danilevsky, et al., WinaCS: Construction and Analysis of Web-Based

Computer Science Information Networks", ACM SIGMOD'11 (system demo), Athens, Greece, June 2011. 8

http://www.cs.uiuc.edu/homes/hanj/pdf/sigmod11_tweninger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/sigmod11_tweninger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/sigmod11_tweninger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/sigmod11_tweninger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/sigmod11_tweninger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/sigmod11_tweninger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/sigmod11_tweninger.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/sigmod11_tweninger.pdf

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Wikification: Example: Entity Resolution & Tracking

Regi Blinker played three matchesfor Oranje. The left winger started

his pro carreer at Feyenoord andplayed 400 official matches forFeyenoord, Celtic and Sparta. He

retired from football in 2003.Where is he now?

9

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Wikification [ACL2011] Given:

An information networks consisting of news articles and blogs,

Wikipedia: Text, Structured Information, Network (hyperlink) Structureand Ontological (Category) Structure.

Goal:

Identify all entities and concepts mentioned in articles and blog

Disambiguate & map each entity and concept to its appropriate

Wikipedia page Entity (and Concept) Resolution

Associate with each concept a collection of semantic attributes

Progressively enrich the information network and enable betteraccess to it.

Approach:

A global optimization problem that accounts for Local, node-specific information,

Global, node and network structure information

Ontological network structure

Machine Learning algorithms determine candidates and rank nodes

10Lev Ratinov, Doug Downey, Mike Anderson, Dan Roth,Local and Global Algorithms for Disambiguation to Wikipedia ,ACL11,

Id ifi i f T i

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Identification of TaxonomicRelations [EMNLP2010]

The use of information networks to acquire Taxonomic Relations.

Given: An information networks consisting of news articles and blogs Pairs of Concepts or Entities

Make use of: WikipediaText, Structured Information, Network(hyperlink) Structure and Ontological (Category) Structure.

Goal:

Developing large ontologies is essential to progressively enrich theinformation network and enable better access to it.

Huge amount of work has been done on developing stationary networks

Suffers from low coverage, noise, and brittleness

A Machine Learning & Optimization based approach

Exploits the fact that data in heterogeneous information networks isnoisy, uncertain, and incomplete.

Considers multiple relations, makes use ofa global constraintoptimization process to leverage both Wikipedia and the web.

Significantly outperforms existing well-known taxonomical networks.

(Honda, Toyota) are Siblings

M1A2 is-a Tank is-a Vehicle

AK-47 is-a Gun

11Quang Do and Dan Roth,Constraints based Taxonomic Relation Classification, EMNLP10

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Leverage Semantic Information Network to EnhanceEntity Coreference Resolution / Entity Identification

Disambiguation

Name Variant Clustering

9.4% absolute improvement in micro-averaged accuracy

(CUNY) Heng Ji and Ralph Grishman. "Knowledge Base Population: Successful Approaches

and Challenges". ACL-HLT2011. 12

https://agora.cs.illinois.edu/download/attachments/30425499/5.pdf?version=1&modificationDate=1300050634857https://agora.cs.illinois.edu/download/attachments/30425499/5.pdf?version=1&modificationDate=1300050634857https://agora.cs.illinois.edu/download/attachments/30425499/5.pdf?version=1&modificationDate=1300050634857https://agora.cs.illinois.edu/download/attachments/30425499/5.pdf?version=1&modificationDate=1300050634857https://agora.cs.illinois.edu/download/attachments/30425499/5.pdf?version=1&modificationDate=1300050634857https://agora.cs.illinois.edu/download/attachments/30425499/5.pdf?version=1&modificationDate=1300050634857

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13

1cq

2cq

3cq

4cq5cq

6cq7cq ( )q

Bo

( )q

Ao

q 0.70.4

q

0.30.6

correct rank :

Micro and Macro Collaborative Networks Ranking forEntity and Event Coreference Resolution

Previous methods only focused on thetarget node and one learning theory

itselfPropose a new collaborative networkranking theory which imitates humancollaborative learning

Leverage inter-connections amongcollaborative entities in information

networksAutomatic profiling for each node

Construct a collaborative network for eachentity based on graph-based clustering

Rank multiple decisions from collaborativeentities (micro) and algorithms (macro)based on global prediction

7% absolute improvement in micro-averaged accuracy

On-going CUNY+UIUC work: usingtopic modeling for entity clustering

(CUNY) Zheng Chen and Heng Ji. 2011. Collaborative Ranking: A Case Study in EntityLinking. Proc. EMNLP2011 [SUB] 13

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Khamis Mushait

14 14

Wail Al-Shehri

V3

Markov Logic Networks and Learning-to-Rank to EnhanceOpen Domain Role Discovery

Waleed Al-Shehri

Abdul Aziz Al-OmariAbdul Rahman Al-Omari

V4

V6 V7 V8

V9

V10

V11V12

Wail Al-Shehri

V3

Waleed Al-Shehri

Abdul Aziz Al-OmariAbdul Rahman Al-Omari

V4

911 SuspectTerrorist Network

V15

TerroristInformation Network

originmember

Al-Qaeda

V13

sibling

news pageweb blog

twitterforumBoston

V14residence

residence

Mohamed AttaMohamed AttaV16

pilot

pilotSaudi Arabian Airlines

Discovered 26 roles for persons, 16 roles for organizations and 13 roles for locations

Markov Logic Networks for Cross-slot and Cross-query reasoning based on InfoNet andtextual linkages to resolve conflictions and predict missing links

Weight=15:

Weight=100:

Maximum Entropy based Learning-to-rank model to re-rank candidate answers

13%-22% absolute F-measure improvement

(CUNY) Chen et al. "CUNY-BLENDER TAC-KBP2010 Entity Linking and Slot Filling SystemDescription". Proc. TAC2010 and Lecture Notes in Computer Science, 2010

, , ( , ) ( , ) ( ) ( )x y z Ambiguous X Y Textual Linkage Y Z Pilot X Pilot Z Remove X

, ,( , ) ( , ) ( , )

x y zSibling X Y Origin Y Z Origin X Z

https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556https://agora.cs.illinois.edu/download/attachments/30425499/hji_app_16.pdf?version=1&modificationDate=1300051108556

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15

Enhanced text data analysis using relatively structured,

heterogeneous information networks

Progressive Dynamic Information Network Analysis[EMNLP11, ACL-HLT2011 (sub)]

Integration of Heterogeneous Info. Network and TopicModeling (Biased Propagation) [KDD11 (sub)]

Topic Modeling for Active Learning and Inference in EventNetwork Construction [ACL-HLT2011 (sub)]

Geographical Topic Discovery & Comparison [WWW11]

Latent Association Analysis of Document Pairs [KDD11

(sub)]

15

Subtask 2. Network-Enhanced Text Analysis

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Ali Larijani

IranSupreme NationalSecurity Council

Islamic Republic ofIran Broadcasting

FaridehMotahari

TehranUniversity Hassan

Rowhani

Progressive Dynamic Information Network Analysis

Motivations

Most information obtained on text-rich

InfoNet construction so far is viewed asstatic, ignoring the temporal dimensionof many types of attributes

Approaches Temporal Role Representation

[T1 T2 T3 T4] =

New Evaluation Metric

Local temporal role discovery usingnew kernel methods based ondependency paths

Global inference and aggregation toresolve conflicts using Integer LinearProgramming (ongoing collaborationwith Dan Roth at UIUC)

Results

State-of-the-art temporal roleclassification accuracy and lowestvagueness/over-constraining

(CUNY) Javier Artiles, Qi Li, Enrique Amigo and Heng Ji. 2011. Leveraging Cross-documentRedundancy for Temporal Information Extraction. EMNLP2011, ACL-HLT2011 [SUB] 16

Baseline

Aggregation over 2 tuples Aggregation over 10 tuples

Our Approach on Information Networks

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Probabilistic Topic Models with Biased Propagation onHeterogeneous Information Networks [KDD11 (sub)]

Problem and Motivation: Discover latent topics & identify clusters

of multi-typed objects simultaneously Treat multi-typed objects differently (e.g.,

D w. rich text & U w.o. explicit text) Solution and Contribution:

Basic idea: biased topic propagation Propose a novel TMBP algorithm to

directly incorporate heter. infornetinstead of homog. InforNet with topicmodeling (improve 20%-40% over PLSA)

17

Topic modeling with heterogeneous InforNet

Topic modelBiased propagation

(UIUC) Hongbo Deng, Jiawei Han, Bo Zhao, Yintao Yu, and Cindy Xide Lin, "Probabilistic TopicModels with Biased Propagation on Heterogeneous Information Networks", KDD'11 (sub)

T i M d li f A i L i d I f i

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Topic Modeling for Active Learning and Inference in

Event Network Construction

Topic Modeling can enhanceinformation network construction by

grouping similar event typestogether and converginginformation distributions

Using Topic modeling, with only1/4 training data we can achievecomparable performance as

passive learningCross-document inference withintopic clusters provided 10%improvement over state-of-the-artevent extraction, significant gainsover IR based clustering

Ongoing work: apply new entity-driven and biased propagationbased topic modeling methods

(CUNY + UIUC) Hao Li, Heng Ji, Hongbo Deng and Jiawei Han. 2011. Topically RelatedData is Better Data: Topic Modeling for Event Extraction. ACL-HLT2011[sub]

Putin

weapons

nuclear

talks

forces

troops

army

militaryBritish

AFPmillion

government

dollars

convicted

billion company

court

sentence

Event Type: "Contact"

Trigger: talk, meet etc.

Arguments: "Entity"

"Instrument" "Place"

"Time-Within"

Event Type: "Business"

Trigger: form, dissolve

Arguments:

"Org""Place" "Time-

Within" "Agent"

Event Type: "Attack"

Trigger: blew, attackArguments: "Attacker"

"Target" "Place" "Time-

Within"

EventType:"Transaction"

Trigger: Borrow, Launch

Arguments: "Giver"

"Recipient""Money""Sell

er""Artifact""Buyer"

Pyong

yang

China

officials

Washington

north

southKorea

program

United

States

Saddam

control

fighting

city

Baghdad

Iraqi

regime

Kurdish

York

case

media

Event Type: "Justice"

Trigger: Arrest, Jail

Arguments:"Defendant"

"Time-Within"

"Adjudicator" "Place"

Doc 1

Doc 3

Doc 4

Doc 6

Doc N

Doc 2

Doc 5

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Geographical Topic Discovery & Comparison

Motivation: Analyze GPS-associated documents,e.g., geo-tagged photos and tweets sent fromiphones

Problem: Given a collection of GPS-associateddocuments and # of topics K, discover K geo-topics along with the topic distribution indifferent geo. locations

Latent Geographical Topic Analysis Combine text and GPS location info

Words that are close to each other are morelikely to be in the same region. Words thatare in the same regions are more likely to be

in the same topic Regions are not known beforehand. Our

framework adopts the region discoveryprocess according to the dataset

19Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas Huang, Geographical

Topic Discovery and Comparison, WWW'11, Mar. 2011

http://www.cs.uiuc.edu/homes/hanj/pdf/www11_zyin.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/www11_zyin.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/www11_zyin.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/www11_zyin.pdfhttp://www.cs.uiuc.edu/homes/hanj/pdf/www11_zyin.pdf

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Latent Association Analysis of Document Pairs

Latent Association Analysis (LAA) mines the topics of two documentsets simultaneously, taking the bipartite network between twodocument sets into consideration

One of the first attempts to analyze the topic structures of twoconnected document sets, aiming to infer their mapping networkmodel

LAA significantly outperforms existing algorithms with 70% accuracyimprovement

Topic Simplex for Corpus 1

?

Topic Simplex for Corpus 2

0 1

1

?

Correlation Factor

Document Pairs

Gengxin Miao, et al., Latent Association Analysis of Document Pairs, KDD11 (sub)

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21

Information Network-Based Trustworthiness Analysis *COLING10,

Army Sci10 (Best Paper Award), WWW11+

Progressive Network Analysis for Expert Search (Diffusion through

Co-occurrence Relationships for Expert Search on the Web)

*SIGIR11 sub+

Modeling and Exploiting Heterogeneous Sources for Expertise

Ranking *SIGIR11 sub+

Personalized Recommendation on Information Networks

*SIGIR11 sub+

Multi-facet Search in Self-Boosting Information Networks

(Demo: Terrorism Network Search and Browsing) *SIGIR11 sub+

21

Subtask 3: Multi-Facet Search and Mining

Information Network Based Trustworthiness

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Information Network-Based TrustworthinessAnalysis

Given: Multiple Information networks: websites, blogs, forums, sensor networks

Some claims, e.g., [Person A travelled to France], [There is a fire indowntown Chicago] Prior beliefs and background knowledge

Our goal is to: Score trustworthiness of Claims based on support across multiple (trusted) sources in the network source characteristics:

reputation, interest-group, verifiability of information, etc. Prior Beliefs and Background knowledge

Rate databases/sources as more/less trustworthy Track how the trustworthiness of fact / database varies with time as the text

corpus grows over time New framework for incorporating prior knowledge into anyfact-finding

algorithm Done via a Linear Programming approach Highly expressive declarative constraints Tractable (polynomial time)

Prior knowledge improve results Absolutely essential when the users judgment varies from the norm

22Dan Roth et al, COLING10, Army Sci10 (Best Paper Award), WWW11

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Progressive Network Analysis for Expert Search

Goal: find and rank people who have expertise described by user query

Web pages are more noisy, contain spam compared to corpus in anenterprise. Both relevance and reputation should be considered

Use a heterogeneous hypergraph to model the co-occurrencerelationships among people and words and devise a heat diffusion

model on the hyerpgraph Applied to 0.5B web pages

Accuracy: 50%-200% improvement than the leading language modelmethods. Significantly overcome noises in the Web.

Ziyu Guan, et al., Diffusion through Co-occurrence Relationships for Expert Search on the Web, SIGIR11 (sub)23

M d li d E l iti H t

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Modeling and Exploiting HeterogeneousSources for Expertise Ranking

24

Coauthor graphCitation graph

Problem: How to leverage both heterogeneous network anddocuments to identify the relevant experts for a given query?

Baseline: The expertise of a person could be characterizedbased on his/her associated documents (doc-based method)

Intuitions:

Citation graph: Similar documents are likely to have

similar relevance to a given queryCoauthor graph: Two authors are most likely to sharesimilar expertise if they coauthor many papers.

Document-author bipartite graph: mutually reinforcedbetween documents (x) and authors (y)

Top-10 experts for query:Information retrieval

Solution: We formulate a joint regularization framework toincorporate several hypotheses to capture the information ofdifferent graphs together with textual documents

Hongbo Deng, et al., Modeling and Exploiting Heterogeneous Sources for Expertise Ranking, SIGIR11 (sub)

Result: Using DBLP with 2M nodes and 10M edges.Significant improvements over the baseline.

M lti Facet Search in Self Boosting Information Net orks

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Multi-Facet Search in Self-Boosting Information Networks(Example: Terrorism Network Search and Browsing)

Demo: http://blender2.cs.qc.cuny.edu/BlenderGraph/

Video: http://nlp.cs.qc.cuny.edu/terrorism.m4v

(CUNY + UIUC) Sam Anzaroot, Javier Artiles, Heng Ji, Hongbo Deng and Jiawei Han. 2011. Search andBrowsing Self-Boosting Information Networks. SIGIR2011 [SUB]

Facilitate a military analyst in expert finding and terrorist information search gathering,control and analysis for any given query Entity-topic analyzer for self-expansion and self-boosting: Terrorism organization members status of members (die, arrest,...) and information networks associatedwith each member

http://blender2.cs.qc.cuny.edu/BlenderGraph/http://nlp.cs.qc.cuny.edu/terrorism.m4vhttp://nlp.cs.qc.cuny.edu/terrorism.m4vhttp://blender2.cs.qc.cuny.edu/BlenderGraph/

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Subtask 1: Text-Rich Network Modeling and Construction

Object search task enhanced by entity disambiguation and rolediscovery can provide methods for finding groups of soldiers and

identifying terrorists with certain expertise

Subtask 2: Network-Enhanced Text Analysis

Asymmetric wars and counter-terrorism need understand text-rich net

Text mining for monitoring potential threats and detecting terrorism

with entity-topic modeling and event detection and tracking

Subtask 3: Multi-Facet Search and Mining in Text-Rich Networks

Most military applications need to search in multi-facets on text and

unstructured data, including emails, reports, telecommunicationmessages, military-related news and blogs

Our multi-facet multi-dimensional information network search and

browsing tool has rich functions and provide intelligent network

expansions

26

Military Relevance

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I3.2s Collaboration Network

27

I3.2: Han, Ji,Roth, Yan

weekly teleconsfrequent emails5 joint papers

I1.2

Tarek, Charu

I1.1

RothHuang

ARLCole

Winkler

I3.1Han, Yan

T1.4

Parsons

E2.3Han

Logic Reasoning

for InformationValidation

IRC LeungData &

Experiments

Military Data forTopic Analysis

T1.1 Adali

T1.5Lin, Wen

S1.1 Lin

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Next Six Months and Path Ahead to 2012

Continue research on mining text-intensive information networks

Research in three frontiers: (1) integrated classification and clustering innetwork mining, (2) build up a theory on link/relationship analysis inheterogeneous networks, and (3) explore military applications

Collaborations with researchers in other networks

Work with Nitesh Chawla, who has done much work on link prediction,on evaluation of mining methods for clustering and classification of

heterogeneous networks Work with SCNARC (Boleslaw Szymanski et al.) on using the method

developed here to mine social and cognitive networks

Next year research planned if funded

Effective theory and methods for mining heterogeneous networks

involving social and communication networks Network classification and clustering modeling in heterogeneous

information, social, and communication networks

Application of role discovery, network classification, and anomalydetection methods in military applications

28

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Research Papers (Accepted/Published, 2011)

1. Tim Weninger, Marina Danilevsky, Fabio Fumarola, Joshua Hailpern, Jiawei Han, et al., WinaCS:Construction and Analysis of Web-Based Computer Science Information Networks", Proc. of 2011

ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'11), (system demo paper), Athens,Greece, June 2011.

2. Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas Huang, Geographical TopicDiscovery and Comparison, Proc. of 2011 Int. World Wide Web Conf. (WWW'11), Hyderabad,India, Mar. 2011 (Full paper).

3. Tim Weninger, Fabio Fumarola, Cindy Xide Lin, Rick Barber, Jiawei Han, and Donato Malerba,Growing Parallel Paths for Entity-Page Discovery, Proc. of 2011 Int. World Wide Web Conf.

(WWW'11), Hyderabad, India, Mar. 2011 (Poster paper)4. Heng Ji and Ralph Grishman. "Knowledge Base Population: Successful Approaches andChallenges". Accepted by Proc. the 49th Annual Meeting of the Association for ComputationalLinguistics: Human Language Technologies (ACL-HLT2011), 2011.

5. Heng Ji, Adam Lee and Wen-Pin Lin. "Information Network Construction and Alignment fromAutomatically Acquired Comparable Corpora". Invited book chapter for Building and UsingComparable Corpora. Springer, 2011.

6. Heng Ji, Benoit Favre, Wen-Pin Lin, Dan Gillick, Dilek Hakkani-Tur and Ralph Grishman. 2011.

"Open-domain Multi-document Summarization via Information Extraction: Challenges andProspects". Invited book chapter for Multi-source, Multilingual Information Extraction andSummarisation. Springer.

7. Lev Ratinov, Doug Downey, Mike Anderson, Dan Roth, Local and Global Algorithms forDisambiguation to Wikipedia , ACL11

8. Y. Chan and D. Roth, Exploiting Syntactico-Semantic Structures for Relation Extraction, ACL11

29

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Research Papers (Published, Sept.-Dec. 2010)

1. Manish Gupta, Rui Li, Zhijun Yin, and Jiawei Han, Survey on Social Tagging Techniques", SIGKDD

Explorations, 12(1):58-72, 2010.

2. Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, Shiqiang Yang, Mining Topic-Level Influence in

Heterogeneous Networks", Proc. 2010 ACM Int. Conf. on Information and Knowledge

Management (CIKM'10), Toronto, Canada, Oct. 2010

3. Tim Weninger, Fabio Fumarola, Jiawei Han, Donato Malerba, Mapping Web Pages to Database

Records via Link Paths", Proc. 2010 ACM Int. Conf. on Information and Knowledge Management

(CIKM'10), Toronto, Canada, Oct. 2010.

4. Xin Jin, Andrew Gallagher, Liangliang Cao, Jiebo Luo, and Jiawei Han, The Wisdom of SocialMultimedia: Using Flickr for Prediction and Forecast", Proc. 2010 ACM Multimedia Int. Conf.

(ACM-Multimedia10), Florence, Italy, Oct. 2010

5. Zheng Chen, Suzanne Tamang, Adam Lee, Xiang Li, Wen-Pin Lin, Javier Artiles, Matthew Snover,

Marissa Passantino and Heng Ji. "CUNY-BLENDER TAC-KBP2010 Entity Linking and Slot Filling

System Description". Proc. Text Analytics Conference (TAC2010), 2010

6. Hao Li, Xiang Li, Heng Ji and Yuval Marton. 2010. "Domain-Independent Novel Event Discoveryand Semi-Automatic Event Annotation". Proc. the 23rd Pacific Asia Conference on Language,

Information and Computation (PACLIC 2010)

7. J. Pasternack and Dan Roth, Comprehensive Trust Metrics for Information Networks , Army

Science Conf.10 (Best Paper Award), Dec. 2010.

8. Q. Do and D. Roth, Constraints based Taxonomic Relation Classification, EMNLP10, Oct. 2010

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Research Papers (Submitted, 2011)1. (UIUC + U. Michigan) Cindy Xide Lin, Qiaozhu Mei, Yunliang Jiang, and Jiawei Han, "Inferring the Diffusion and Evolution of

Topics in Social Communities", KDD'11 (sub)

2. (UIUC) Hongbo Deng, Jiawei Han, Bo Zhao, Yintao Yu, Cindy Xide Lin, "Probabilistic Topic Models with Biased Propagation on

Heterogeneous Information Networks", KDD'11 (sub)3. (UIUC) Zhijun Yin (UIUC), Liangliang Cao (UIUC), Jiawei Han (UIUC), Chengxiang Zhai (UIUC), Thomas Huang (UIUC), "LPTA: A

Probabilistic Model for Latent Periodic Topic Analysis", KDD'11 (sub)

4. (CUNY + UIUC) Heng Ji and Jiawei Han. 2011. Web-Scale Knowledge Discovery and Information Extraction. Invited Paper for

IEEE Special Issue on Web-Scale Multimedia Processing and Applications. In Preparation.

5. (CUNY + UIUC) Hao Li, Heng Ji, Hongbo Deng and Jiawei Han. 2011. Topically Related Data is Better Data: Topic Modeling for

Event Extraction. Submitted to the 49th Annual Meeting of the Association for Computational Linguistics: Human Language

Technologies (ACL-HLT2011)

6. (CUNY + UIUC) Sam Anzaroot, Javier Artiles, Heng Ji, Hongbo Deng and Jiawei Han. 2011. Search and Browsing Self-BoostingInformation Networks. Submitted to the 34th Annual International ACM SIGIR Conference (SIGIR2011)

7. (CUNY) Javier Artiles, Qi Li, Enrique Amigo and Heng Ji. 2011. Leveraging Cross-document Redundancy for Temporal

Information Extraction. Submitted to Empirical Methods in Natural Language Processing (EMNLP2011)

8. (CUNY) Javier Artiles, Enrique Amigo, Qi Li and Heng Ji. 2011. Evaluating Temporal Information Extraction. Submitted to ACL-

HLT2011

9. (CUNY) Zheng Chen and Heng Ji . 2011. Collaborative Ranking: A Case Study in Entity Linking. Submitted to Conference on

Empirical Methods in Natural Language Processing (EMNLP2011)

10. (CUNY) Qi Li, Javier Artiles and Heng Ji. 2011. Dependency Paths Kernel for Temporal Relation Classification. Submitted to 49thAnnual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT2011).

11. (CUNY) Suzanne Tamang and Heng Ji. 2011. Learning-to-Rank for Slot Filling System Combination and Assessment. Submitted

to Conference on Empirical Methods in Natural Language Processing (EMNLP2011)

12. (CUNY) Zheng Chen, Suzanne Tamang, Adam Lee and Heng Ji. 2011. A Toolkit for Knowledge Base Population. Submitted to

the 34th Annual International ACM SIGIR Conference (SIGIR2011)

13. (CUNY) Xiang Li and Heng Ji. 2011. Comment-guided Reinforcement Learning for Slot Filling. Submitted to Conference on

Empirical Methods in Natural Language Processing (EMNLP2011)

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Other Technical Contributions (Book: UIC + UIUC + CMU) Philip S. Yu, Jiawei Han, and Christos Faloutsos (Editors), LINK MINING: MODELS,

ALGORITHMS AND APPLICATIONS, Springer, 2010.

(UIUC) Jiawei Han has received Daniel C. Drucker Eminent Faculty Award at UIUC (UCSB) Ms. Gengxin Miao, who was supported by the INARC program, has received IBM Ph.D. Fellowship for

2011-2012. Gengxin Miao is co-supervised by Xifeng Yan at INARC.

(CUNY) Heng Ji. CUNY Chancellor's "Salute to Scholar" Award, November 2010.

(CUNY) Heng Ji. National Science Foundation Research Experiences for Undergraduates, March 2011

Jiawei Han, Towards Integrated Mining of Multiple Social and Information Networks (keynote speech) The

2011 Int. Conf. on Advances in Social Network Analysis and Mining (ASONAM11), July 2011.

Jiawei Han, Exploring the Power of Heterogeneous Information Networks in Data Mining (keynote speech)The 2011 Int. SIAM Data Mining Conf. (SDM11), April 2011.

Jiawei Han, Construction and Analysis of Web-Based Computer Science Information Networks (keynote

speech) The 2011 Int. Conf. on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, June 2011.

Latifur Khan, Wei Fan, Jiawei Han, Jing Gao, Mohammad Mehedy Masud, Data Stream Mining: Challenges

and Techniques, (tutorial), The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD

2011), May 2011

Jiawei Han, Web Structure Mining and Information Network Analysis: An Integrated Approach, invited speechat the Third International Workshop on Network Theory: Web Science Meets Network Science, March 2011.

Heng Ji, Web-Scale Knowledge Discovery and Population from Unstructured Data, Keynote Speech ACLCLP

2010 Information Retrieval Conference, December 2010.

Heng Ji. Overview of the TAC2010 Knowledge Base Population Track, Keynote Speech at Web People Search

(WePS-3) Conference, September 2010.

32

Personalized Recommendation on

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Personalized Recommendation onInformation Networks

Concept extractionText Concept

Combine text & links inheterogeneous

networks

Find good conceptualassociations of userinterests; distinguishclean sources andnoisy sources

(UIUC) Chi Wang, et al., Learning Relevance in a Heterogeneous Social Network and Its