Knowledge Management Systems: Development and ApplicationsPart III: Case Studies and Future

Hsinchun Chen, Ph.D.

McClelland Professor,

Director, Artificial Intelligence Lab and Hoffman E-Commerce Lab

The University of Arizona

Founder, Knowledge Computing Corporation

Acknowledgement: NSF DLI1, DLI2, NSDL, DG, ITR, IDM, CSS, NIH/NLM, NCI, NIJ, CIA, NCSA, HP, SAP

美國亞歷桑那大學 , 陳炘鈞 博士

Knowledge Management Systems:Knowledge Management Systems:Case StudiesCase Studies

Multi-lingual Knowledge Portal (1M):

Meta searching, post-retrieval analysis, summarization,

categorization, AI Lab tooolkits

• Knowledge Portals are online searching systems that provide large amount of information resources and services within a specific domain. – Providing frequently updated and highly domain-specific

information.– Providing efficient and precise searching service.– Providing advanced analysis functionalities which can help

users find the information needed among huge amount of data.

– Providing additional tools such as Personalization and Alerting System to facilitate the searching tasks.

NanoPort: Knowledge Portal for Nanotechnology Researchers• Goal:

– Providing information services to nanotechnology researchers.– The design of the content and function is based on the feedback of Nanoscale Science and

Engineering (NSSE) experts.• Content:

– 1,000,000 high quality nanotechnology-related webpages in database.– Meta-search 4 search engines, 5 online databases and 3 online journals

• Key Features:– Dynamic summarization– Folder display– Visualization using self-organizing map (SOM)– Patent nalysis

• Funding:– US National Science Foundation (NSF) Nano Initiative

• Demo:– http://nanoport.org/

Input keywords

Select search engines

Select online databases

Select online journals

　Folder displayVisualization using SOM

Folder display Visualization with SOM

Summary

The original page

Highlight the summary in the original page

with corresponding color

Click on the summary sentence and jump to

its position in the original page

Summarize result dynamically

MedTextus: English Medical Intelligence• Goal:

– Providing information services to researchers in medical domain.

• Content:– Meta-search 5 large medicine-related online databases and journals.

• Key Features:– Keyword suggester– Folder display– Visualization using SOM

• Funding:– US National Library of Medicine (NLM)

• Demo:– http://ai23.bpa.aizona.edu/medtextus/

Keyword suggested by the system

Result pageVisualization with SOM

Folder display

Select databases

Input keywords

Keyword suggester

Advanced search options

eBizPort: English Business Intelligence• Goal:

– Providing business, trading and financial information services to commercial users.

• Content:– 500,000 high quality webpages in database.– Meta-search 10 authoritative online business magazines.

• Key Features:– Search by date– Keyword suggester– Dynamic summarization– Folder display– Visualization using SOM

• Demo:– http://ai18.bpa.arizona.edu:8080/ebizport/

Date of the result page

Result page

Folder display and SOM

Keyword suggested by the system

Keyword suggester

Limit the date of the result pages

Chinese Medical Intelligence (CMI)• Goal:

– Providing medical and health information services to both researchers and public.

• Content:– 350,000 high quality medical-related webpages collected from mainland

China, Hong Kong and Taiwan.– Meta-search 3 large general Chinese search engines.

• Key Features:– Built-in Simplified/Traditional Chinese encoding conversion– Dynamic summarization for both Simplified and Traditional Chinese– Automatic categorization– Visualization using SOM

• Demo: – http:// 128.196.40.169:8000/gbmed/

Simplified Chinese summary

Traditional Chinese summary

Chinese folder displayChinese visualizationwith SOM

Simplified/Traditional Chinese summarization

Select websites from mainland China, Hong Kong and Taiwan

Select search engines from mainland China, Hong Kong and Taiwan

Results are from both Simplified and Traditional Chinese

Original encoding of the result

Traditional Chinese results haven been converted into simplified Chinese

Chinese Business Intelligence (CBI)• Goal:

– Providing business, trading and financial information services to Chinese commercial users.

• Content:– 300,000 high quality webpages collected from Mainland China, Hong Kong

and Taiwan. • Key Feature:

– Built-in Simplified/Traditional Chinese encoding conversion– Dynamic summarization for both Simplified and Traditional Chinese– Folder display– Visualization using SOM

• Demo – http://ai14.bpa.arizona.edu:8081/nanoport/

The largest business, trading and financial websites in mainland China, Hong Kong and TaiwanBoth Simplified and Traditional

results are retured

Chinese summarizer

Simplified Chinese summary

Traditional Chinese summary

Chinese folder display

Chinese visualization with SOM

Spanish Business Intelligence Portal

Meta searches 7 major sources and provides searching of its own collection (PIN)

Supports boolean searching and allows the display of 10, 20, 30, 50, or 100 results per each meta searchers

Keyword suggestion from Scirus and Concept Space

Detailed directory of Spanish business resources on the Web

Keyword:

comercio electronico

Search, Organize, or Visualize resultsSearch, Organize, or Visualize resultsSearch, Organize, or Visualize results

Results organized by meta searchersSummarize in 3 or 5

sentences

Automatic keyword suggestion

Search Page Result PageSummarizer

A three-sentence summary on leftOriginal page

shown on right

Categorizer

Web pages grouped by key phrases extracted by mutual information algorithm (non-exclusive categorization)

Visualizer

Web pages visualized by self-organizing map (SOM) algorithm

Search Page Spanish Business Taxonomy

Web sites about the topic “Electronic Commerce” in Spanish speaking countries

Arabic Medical Intelligence Portal

Provides a virtual Arabic keyboard to facilitate input

Search Page Result Page

Categorizer

Visualizer

Lessons Learned• The content selection and functionality design of knowledge

portal should meet the need of real users.• Using meta-search together with other traditional data

collecting methods can improve the recall without sacrificing the precision of the knowledge portal.

• The structure of the webpage may introduce noise into the dynamic summary.

• The AI Lab toolkits support scalable multi-lingual spidering, indexing, searching, summarization, and categorization

• New Spanish and Arabic portals completed• New cross-lingual web retrieval engine completed

Biomedical Informatics (10M):

Biomedical content, biomedical ontologies,

linguistic phrasing, categorization, text mining

HelpfulMED Search of Medical Websites

HelpfulMED search of Evidence-based Databases

What does database cover?

Search which databases?

How many documents?

Enter search term

Consulting HelpfulMED Cancer Space (Thesaurus)

Enter search term

Select relevant search terms

New terms are posted

Search again...

Or find relevant webpages

1 Visual Site Browser

Browsing HelpfulMED Cancer Map

Top level map2

Diagnosis, Differential3

4 Brain Neoplasms

Brain Tumors5

Genescene Overview

Text MiningProcess Medline abstracts and extract gene relations automatically from the text

Data MiningProcess gene expression

data (and existing knowledge) and use

different algorithms to extract regulatory

networks Interface & Visualization

Allow searching for keywords, display a map of the relations extracted from the text and/or from

the microarray

Knowledge BaseIntegrate gene relations from

literature and outside databases and provide

knowledge for learning and evaluation in data mining

Genescene Overview

Medline

Titles & Abstracts

Feature Structures

Publications &

Meta Information

Publications

MicroArray DataUMLS

VisualizationInformation

RetrievalGeneSceneData Mart

GeneSceneText Mart

Text Mining GeneScene

ConceptSpace

Co-occurrence relations

Data Mining

Relation Parsers

Relations inflat files

XML Parser

UMLS

GO

HUGO

Ontologies

Relations inflat files

Spring Algorithm

BayesianNetworks

AssociationRule Mining

JIF

POS Tagging

FullParser

RelationGrammar

FSA

AZ NounPhraser

Adjuster & Tagger

Lexical lookup

External Databases

KnowledgeBase

Problem: Gene Pathway•Title Key roles for E2F1 in signaling p53-dependent apoptosis and in cell division within developing tumors.•Abstract: Apoptosis induced by the p53 tumor suppressor can attenuate cancer growth in preclinical animal models. Inactivation of the pRb proteins in mouse brain epithelium by the T121 oncogene induces aberrant proliferation and p53-dependent apoptosis. p53 inactivation causes aggressive tumor growth due to an 85% reduction in apoptosis. Here, we show that E2F1 signals p53-dependent apoptosis since E2F1 deficiency causes an 80% apoptosis reduction. E2F1 acts upstream of p53 since transcriptional activation of p53 target genes is also impaired. Yet, E2F1 deficiency does not accelerate tumor growth. Unlike normal cells, tumor cell proliferation is impaired without E2F1, counterbalancing the effect of apoptosis reduction. These studies may explain the apparent paradox that E2F1 can act as both an oncogene and a tumor suppressor in experimental systems

"E2F1 signals p53-dependentapoptosis"

p53

E2F1

apoptosis

infers So, I'm assuming... a straightline pathway...

reads "E2F1 acts upstream of p53"

p53

E2F1

apoptosis

"E2F1 deficiency does notaccelerate tumor growth"

E2F1

p53

apoptosis

tumor growth

reads

E2F1

p53

apoptosis

Action Protocols

reads

GraphicRepresentation

Expert errs and corrects

Final graph

Prepositions: OF/BY/IN

q0

q3

q1

q2

q7

q4

q5

q6

NP

NP, 5: str1

OF

OF

Negation

NP

Adjective,Noun,verb (-ed)

Adjective,noun,verb (-ed)

Nominalization (-ion)

Nominalization (-ion)

OF

Nominalization (-ion)

Adjective, noun,verb (-ed)

Nominalization (-ion)

Nominalization (-ion)

q8

q10

OF

NP

q9

OF BY

q11

q13

q14

q12

BYBY

aux verb

NP

q15Negation

verb

verb

Aux, 1: tr13

BY q16

IN

q17

NP

IN

q18

mod

IN

IN

mod

mod

verb

IN

NP

NP

Aux

q0

q3q3

q1

q2

q7

q4

q5

q6

NP

NP, 5: str1

OF

OF

Negation

NP

Adjective,Noun,verb (-ed)

Adjective,noun,verb (-ed)

Nominalization (-ion)

Nominalization (-ion)

OF

Nominalization (-ion)

Adjective, noun,verb (-ed)

Nominalization (-ion)

Nominalization (-ion)

q8

q10q10

OF

NP

q9

OF BY

q11

q13

q14

q12q12

BYBY

aux verb

NP

q15Negation

verb

verb

Aux, 1: tr13

BY q16

IN

q17q17

NP

IN

q18

mod

IN

IN

mod

mod

verb

IN

NP

NP

Aux

Example Map (one abstract)

Select interesting relations to visualize

Double click to expand

Overview

Expanded node

Finding the truth: p38 acts as a negative feedback for Ras

signaling

Lessons Learned:• Biomedical information is precise but terminologies

fluid• SOM performance for medical documents = 80%• Biomedical professionals need search and analysis

help• Biomedical linguistic parsing and ontologies are

promising for biomedical text mining• The need for integrated biomedical data (gene

microarray) and text mining (literature)• New testbeds completed: p53, AP1, and yeast

COPLINK Crime Data Mining (10M):

Intelligence and security informatics, crime association,

crime network analysis and visualization

COPLINK Connect

Consolidating & Sharing Information promotes problem solving and collaboration Records

Management Systems (RMS)

Mugshots Database

Gang Database

COPLINK Connect Functionality• Generic, common XML based criminal elements representation• Data migration (batch and incremental) and mapping for all major databases and legacy systems• Database independent: ODBC compliance data warehouse• Multi-layered Web-based architecture: database server, Web server, browser• Powerful and flexible search tools for various reports, e.g., incidents, warrants, pawns, etc.• Graphical browser-based GUI interface for ease of use, training and maintenance

H. Chen, J. Schroeder, R. V. Hauck, L. Ridgeway, H. Atabakhsh, H. Gupta, C. Boarman, K. Rasmussen, and A. W. Clements, “COPLINK Connect: Information and Knowledge Management for Law Enforcement,” Decision Support Systems, Special Issue on Digital Government, 2003.

COPLINK DetectConsolidated information enables targeted problem solving via powerful investigative criminal association analysis

COPLINK Detect Functionality• Simple association rule mining applied to criminal elements relationships• Generic, common XML based representation for criminal relationships• Incremental data migration and association analysis on databases• Support powerful, multi-attribute queries using partial crime information• Graphical browser-based GUI interface for simple crime relationship analysis and case retrieval H. Chen, D. Zeng, H. Atabakhsh, W. Wyzga, J. Schroeder, “COPLINK: Managing Law Enforcement Data and Knowledge,” Communications of the ACM, 2003.

COPLINK Detect 2.0/2.5

COPLINK Connect/Detect Status• Systems stable and shown useful. Commercialized and supported by KCC• Systems deployed at: TPD, UAPD, PPD, Phoenix, Huntsville (TX), Des Moines (Iowa), Ann Arbor (Michigan), Boston (Massachusetts), Montgomery county (sniper investigation)• Systems under deployment: Salt River (AZ), Cambridge (Massachusetts), Redmond (Washington), many others• COPLINK acclaims at LA Times and New York Times, Newsweek (sniper investigation)

COPLINK Visual Data Mining Research

COPLINK Criminal Network Analysis: Association Tree, Association Network Analysis, Temporal-Spatial Visualization

• P1000: A Picture is worth 1000 words.• Use visual representations and effective HCI to assist in more

efficient and effective crime analysis• Leverage different representations and algorithms: hyperbolic

trees, network placement algorithms, structural analysis, geo-spatial mapping, time visualization

H. Chen, D. Zeng, H. Atabakhsh, W. Wyzga, J. Schroeder, “COPLINK: Managing Law Enforcement Data and Knowledge,” Communications of the ACM, 2003.

A 9/11 Terrorist Network

Figure 1a: Relations among multiple criminal elements are shown on both a hyperbolic tree (right) and a hierarchical list (left).

Figure 1b: A hyperbolic tree with multiple levels of investigative leads.

Figure 2a: The initial layout of a criminal network before analysis.

Figure 2b: The network is analyzed and automatically adjusted to reflect subgroups and central criminal figures.

Figure 2c: A user may choose only the type that is of interest (e.g., person) and view crime associations (e.g., person name, address).

COPLINK Association Tree and Network (2nd generation)

COPLINK Criminal Structural Analysis (3rd generation)

• Criminal association identification– Using shortest-path algorithms to find the

strongest associations between two or more criminals in a network

• SNA (Social Network Analysis)– Using blockmodel analysis to detect subgroups

and patterns of interactions between groups– Identifying leaders, gatekeepers, and outliers

from a criminal networkJ. Xu & H. Chen, “Criminal Network Analysis: A Data Mining Perspective,” Decision Support Systems, 2004, forthcoming.

The proposed framework

COPLINK SNA Experiment• Data Sets

– TPD incident summaries • Time period—Narcotics: 2000-present; Gangs: 1995-present• Size

– Two testing networks• Narcotics (60 individuals)• Gang (24 individuals)

Total # individuals

# sub-networks

Size of sub-newtorks

Narcotics 12,842 2,628 1-10: 2,587

11-20: 31

21-100: 9

502: 1

Gangs 4,376 289 1-10: 264

11-20: 20

21-100: 4

2,595: 1

A narcotic network example

Switch between narcotic network and gang network

Show network and reset network

Adjust level of details

A point represents an individual labeled by his name

A line represents a link between two persons

A bubble represents a subgroup labeled by its leaders name

A line implies that some individuals in one group interact with some individuals in the other group. The thicker the link, the more individual interactions between the two groups

The size of a bubble is proportional to the number of individuals in the group

The rankings of the members of a selected group (green).

A gang network example

The leader

A clique

A gatekeeper

The reduced network structure

Patterns Found• The chain structure of the

narcotic network

• Implications: disrupt the network by breaking the chain

• The star structure of the gang network

• Implications: disrupt the network by removing the leader

White gangs who involved in murders and shootings

White gangs who sold crack cocaine

A group of black gangs

Expert Validation

“Yes, these two groups are together very often”

“(211) and (173) are best friends”

“He is very important. He has a lot of money and sells drugs. His girl friend brings a lot of dancers in the city and buy drugs.”

Lessons Learned:• Data warehousing and gateway approaches are

needed for information consolidation• XML and data normalization are critical• Co-occurrence analysis and link analysis are

extremely useful for crime investigation• Visual data mining is essential for criminal network

analysis• Wireless (laptop, PDA, cell phone) application is

essential• KM techniques may create unintended cultural and

practice side effects

GetSmart Concept Maps:

Knowledge creation, transfer and mapping

Meaningful Learning• Substantive synthesis

• Relate to experiences

• Intentionally connect to prior knowledge

• Memorization

• Unrelated to experience

• No effort to link to existing knowledge

• Practice, rehearsal and thoughtful replication contribute to meaningful learning.

A C

ontin

uum

Meaningful Learning

Rote Learning

Creative Production

Most School Learning

* Adapted from Novak’s model of meaningful learning

Six Steps of Information Search: A Constructivist Approach

Learners are actively involved in building on what they already know to come to a new understanding of the subject under study.

Exploration

Initiation

Presentation

Formulation

Collection

Selection

Introduce a problem.Identify a general area

for investigation.

Explore information to form a focus.

Summarize the topic and prepare to present to the intended audience.

Gather information that defines/supports the focus.

GetSmart Learning Tools

Digital Library CurriculumKnowledge

Representation

Keyword SuggestionFiltered Material

A Place to Store Work

AssignmentsAnnouncements

Linked Resources

Concept MapCustomized Resources

A Concept Map about Concept Maps

Navigation bar

Concept mapmanagementtools

Search tools

Meta search options

GetSmart Interface

By right clicking on a node you can delete the node, change the properties of the node, or add a resource to the node. Resources can be URLs, Maps, or Notes.1 2

3

You can either type a URL, or click the

“Add From URL Clipboard” button.

This is the clipboard. Simply highlight the URL you would like to add to a node and Click OK. Your URL will appear in the window,

click the Done button to add it to your map.

4

Printing

Choosing the Print option will cause a new window to open. This map will show your map, the title of the map, and any URL’s, notes, or maps you have linked to your map.

Usage: Overall at UA and VT

• 114 student users – all UA students (54) turned in all assignments (VT assignments still pending)

• 4,000+ user sessions• 1,000+ maps created for homework and presentations• 600+ searches performed• 50+ maps created as a group• 40,000+ relationships represented in the maps

Results (1)• 120 cue phrases were used to extract 37,674 links, which

accounted for 93% of the pool.

• These cue phrases were categorized into the proposed link types:– About 50 cue phrases map to the five previously

determined link types: hierarchical, componential, comparative, influential, and procedural.

– Over 50% of cue phrases expressed hierarchical and componential relationships.

– Descriptive relationships accounted for a large portion (30%), which were analyzed further.

21.30%

32.67%

3.86%

9.65%

2.91%

29.60%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

Hierarchical Componential Comparative Influential Procedural Descriptive

Over 50% of the links expressed hierarchical or componential relationships

Descriptive relationships accounted for a large portion at 30%, so we further analyze this link type

Link Type Distribution

* The number of links which had those identified cue phrases in them

Link types Number* Percentage Representative cue phrases

Hierarchical 8,026 21.30% example, such as, case, type, member, is a

Componential 12,307 32.67% consist, contain, include, compose, part, made of

Comparative 1,455 3.86% like, compare, similar, differ, alternative

Influential 3,635 9.65% lead to, cause, result, influence, determine

Procedural 1,097 2.91% next, go to, procedureDescriptive 11,153 29.60% use/implement/present/advantages/feature

Sum 37,673 100.00%

Lessons Learned:• Digital library and concepts maps support

meaningful learning• Digital library systems provide support for

community knowledge creation.• Semi-open link systems are useful for

capturing knowledge and learning process• NSDL is not a “library.” It should be a

learning or knowledge creation environment.

Knowledge Management Systems:Knowledge Management Systems:FutureFuture

Other Emerging Categorization Challenges/Opportunities:

• Multilingual terminology and semantic issues• Web analysis and categorization issues• E-Commerce information (transactions) classification

issues• Multimedia content and wireless delivery issues• Future: semantic web, multilingual web,

multimedia web, wireless web!

The Road Ahead

• The Semantic Web: XML, RDF, Ontologies

• The Wireless Web: WML, WIFI, display

• The Multimedia Web: content indexing and

analysis

• The Multilingual Web: cross-lingual MT and IR

Requirements For Successful KMS Implementation (General)

• Sponsor for the application• Business case for the application clearly

understood and measurable• High likelihood of having a significant impact

on the business• Good quality, relevant data in sufficient

quantities• The right people – business domain, data

management, and data mining experts

Requirements For Successful KMS Implementation (KM Specific)

• Information overload is more than anyone can handle

• Productivity gained and decision improvements evident among knowledge workers

• Organization’s IT infrastructure ready• Need to integrate with consulting, process,

content, and policy considerations

For Project Information at AI Lab:

• http://ai.bpa.arizona.edu

• hchen@bpa.arizona.edu