1. A Researcher’s Workbench in 2020: Intelligent Information Systems for Knowledge Synthesis and Discovery
ChengXiang (“Cheng”) Zhai
Department of Computer Science
Institute for Genomic Biology
Graduate School of Library and Information Science
Department of Statistics
University of Illinois at Urbana-Champaign
DOE Institute for Computing in Science (ICiS) Workshop on “Integrating, Representing, and Reasoning over Human Knowledge: A Computational Grand Challenge for the 21st Century”, SnowBird, Utah (Aug, 2010).

2. What are the key computational challenges?
Assuming data sharing isn’t a problem, what kind of systems are needed to effectively support representing, integrating, and reasoning over human knowledge?
What are the key computational challenges?

3. Computer-Aided Research (CAR) in 2020
Public
data/Info/
knowledge
Public
data/Info/
knowledge …
Network …
1. Multi-level integration of data/info/knowledge
2. Multimode info access
5. Collaborative research
3. Research task support
4. Personalized CAR
Personal
data/info/
knowledge
Personal
data/info/
knowledge

4. 1. We need multiple levels of integration

5. Five Levels of Integration
Level 1: “Syntactic” integration of multiple sources
Scalable, robust, but minimum support for discovery
Level 2: Semantic integration (ontology)
Scalable, less robust, better support for discovery
Level 3: Synthesis of knowledge (entities, relations)
Less scalable, not robust, support for interactive discovery
Level 4: Synthesis of knowledge + Inference rules
Only applicable to a limited domain, but potentially support automatic discovery
Level 5: Specialized discovery model
Automatic hypothesis testing, but limited to a special discovery/prediction task

6. Multi-level support is needed because…
Knowledge extraction is far from 100% accurate (NLP is difficult)
Interpretation of knowledge is inherently context-sensitive and low-level support is needed for context and provenance
Automation-scalability tradeoff will not disappear (soon) …

7. Automation-Scalability Tradeoff
Automation of discovery
Goal
Specialized statistical
prediction models
“Beyond ontology”
integration
Logic-based
Inference systems
ER graph
analysis engine
Ontology-based
semantic integration
Federated search
engines
“Ontology-Free” integration
Scalability/Generality

8. Interactive ER Graph Analysis
The extracted entities and relations form a weighted graph
Need to develop techniques to mine the graph for knowledge
Store graphs
Index graphs
Mining algorithms (neighbor finding, path finding, entity comparison, outlier detection, frequent subgraphs,….)
Mining language

9. Example of Interactive Graph Mining
Behavior B2
isa
isa
Co-occur-fly
X= PathBetween({A4,A4’}, B4, {co-occur, reg,isa})
Co-occur-bee
Behavior B1
Gene A1
Behavior B3
Behavior B4
Orth-mos
Co-occur-mos
Co-occur-fly
Gene A1’
Gene A2
Gene A3
Reg
Reg
Reg
orth
Reg
Gene A4’
Gene A4
Gene A5
1.X=NeighborOf(B4, Behavior, {co-occur,isa}) {B1,B2,B3}
2. Y=NeighborOf(X, Gene, {c-occur, orth} {A1,A1’,A2,A3}
3. Y=Y + {A5, A6} {A1,A1’, A2, A3,A5,A6}
4. Z=NeighborOf(Y, Gene, {reg}) {A4, A4’}

10. Inference-Based Discovery
Encode all kinds of knowledge in the same knowledge representation language
Perform logic inferences
Example
Regulate (GeneA, GeneB, ContextC). [Text mining]
SeqSimilar(GeneA,GeneA’) [Sequence mining]
Regulate(X,Y,C) Regulate(Z,Y,C) & SeqSimilar(X,Z) [Human knowledge]
 Regulate(GeneA’,GeneB,ContextC)
ADD: InPathway(GeneB, P1)
InPathway(X,P) Regulate(X,Y,C) & InPathway(Y,P) [Human knowledge]
 InvolvedInPathway(GeneA’,P1)

11. Integration of Expert Knowledge
How can we combine expert knowledge with knowledge extracted from literature?
Possible strategies:
Interactive mining (human knowledge is used to guide the next step of mining)
Inference-based integration
Trainable programs (focused miner, targeting at certain kind of knowledge)

12. 2. We need multiple-mode information access
Querying/Browsing
Researcher
Recommendation
How can we connect the right information
with the right user at the right time?

13. Collaborative Surfing [Want et al. 09]
Browsing and querying are tightly integrated
Search log organized as a topic map
A sustained way of collaborative surfing

14. News Recommender for Facebook [Gupta et al. 09]
Recommendation of research papers?

15. 3. We need to go beyond information access to support tasks
Research topic identification
“hot topic” retrieval, interdisciplinary topic retrieval, topic recommendation
Literature review
automatic survey generation
Collaborator recommendation
To work on an emerging interdisciplinary topic
To work on a joint grant proposal
Hypothesis generation & testing (question answering)

16. Topical Trends in KDD [Mei & Zhai 05]
gene expressions probability microarray …
marketing customer
model business …
rules association
support …

17. Theme Evolution Graph [Mei & Zhai 05]
1999
2000
2001
2002
2003
2004 T
web classifica –tion features0.006 topic …
SVM criteria classifica – tion linear …
mixture random cluster clustering
variables …
topic mixture LDA semantic …
decision tree classifier class Bayes … …
Classifica - tion text unlabeled document labeled learning …
Informa - tion web social retrieval distance networks 0.004 … … …

18. Imagine we can compare literature in two related areas…
Comparing News Articles [Zhai et al. 04] Iraq War (30 articles) vs. Afghan War (26 articles)
The common theme indicates that “United Nations” is involved in both wars
Cluster 1
Cluster 2
Cluster 3
Common
Theme
united
nations …
killed
month
deaths
Iraq n
Weapons
Inspections
troops
hoon
sanches
Afghan
Northern
alliance
kabul
taleban
aid
taleban
rumsfeld
hotel
front
Collection-specific themes indicate different roles of “United Nations” in the two wars
Imagine we can compare literature in two related areas…

19. Task support + ER Question answering
BeeSpace System [He et al. 10]
Task support + ER Question answering

20. 4. Personalization & Workflow Management
Different users have different tasks  personalization
Tracking a user’s history and learning a user’s preferences
Exploiting the preferences to customize/optimize the support
Allowing a user to define/build special function modules
Workflow management

21. UCAIR: User-Centered Adaptive IR [Shen et al. 05]
When a user clicks on the “back” button after viewing a document,
UCAIR reranks unseen results to
pull up documents similar to the one the user has viewed

22. 5. Collaborative Research Information/Knowledge/Workflow Sharing
Different users may perform similar tasks  Information/Knowledge/workflow sharing
Capturing user intentions
Recommend information/knowledge/workflow
How do we solve the problem of privacy?
Massive collaborations?
Each user contributes a small amount of knowledge
All the knowledge can be combined to infer new knowledge
An ESP-like online game for discovery?

23. Knowledge Synthesis & Discovery Game (inspired by the ESP game)
Which of the following genes is
likely associated with
foraging behavior?
Bonus score
based on validation
in publication
Hypothesis
Selection
Ontology
Mapping …
Hypothesis
Selection
Ontology
Mapping …
Immediate Scoring
based on Consensus
Which of the following
concepts can also
describe “car”? …

24. Big Challenges
What’s the right system architecture (= sharing model?)?
centralized vs. distributed, client vs. server
2. How can we sustain sharing and massive collaboration?
open system, “plug and play”, KSD game …
3. How can we seamlessly support multiple-level integration?
4. Specific computational challenges:
-- Large-scale NLP, particularly information extraction
( Large-scale machine learning and knowledge base?)
-- Large-scale semantic mapping (ontology)
-- Interactive fuzzy ER graph mining
-- Scalable inference engines (probabilistic datalog) …
Public
data/Info/
knowledge
Public
data/Info/
knowledge …
Network …
1. Multi-level integration of data/info/knowledge
2. Multimode info access
5. Collaborative research
3. Research task support
4. Personalized CAR
Personal
data/info/
knowledge
Personal
data/info/
knowledge

25. A Possible System Architecture
User
User Interface/ Workflow Manager
Inference
Engine
User Modeling & Personalization
Special
Search
Analysis Engine
Hypothesis
Knowledge
Base
Search & Navigation
NLP
Machine
Learning
Expert
Knowledge
ER Graph
Mining …
InformationRetrieval
NCBI
Genome
Databases
Information
Extraction
Entities
Relations
Data/Info +
Ontology

26. References
[1] Xuanhui Wang, Bin Tan, Azadeh Shakery, ChengXiang Zhai, Beyond Hyperlinks: Organizing Information Footprints in Search Logs to Support Effective Browsing, Proceedings of the 18th ACM International Conference on Information and Knowledge Management ( CIKM'09), pp , [2] Manish Agrawal, Maryam Karimzadehgan, and ChengXiang Zhai. An Online News Recommender System for Social Networks. In Proceedings of ACM SIGIR 2009 workshop on Search in Social Media, [3] Qiaozhu Mei, ChengXiang Zhai, Discovering Evolutionary Theme Patterns from Text -- An Exploration of Temporal Text Mining, Proceedings of the 2005 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , (KDD'05 ), pages , [4] ChengXiang Zhai, Atulya Velivelli, Bei Yu, A cross-collection mixture model for comparative text mining, Proceedings of ACM KDD 2004 ( KDD'04 ), pages , [5] Xin He, Yanen Li, Radhika Khetani, Barry Sanders, Yue Lu, Xu Ling, ChengXiang Zhai, Bruce Schatz. BSQA: integrated text mining using entity relation semantics extracted from biological literature of insects, Nucleic Acids Research, (Web Server issue):W175-W [6] Xuehua Shen, Bin Tan, and ChengXiang Zhai, Implicit User Modeling for Personalized Search , In Proceedings of the 14th ACM International Conference on Information and Knowledge Management ( CIKM'05), pages , [7] Qiaozhu Mei, ChengXiang Zhai. Generating Impact-Based Summaries for Scientific Literature , Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies ( ACL-08:HLT), pages ,