mining minds knowledge maintenance engine maqbool ali maqbool ali khu memberkhu member byeong ho...

Mining MindsKnowledge Maintenance Engine

• Maqbool Ali

KHU Member

• Byeong Ho Kang

UTAS Member

/ 2Agenda

• Introduction• Motivation• Related Work• Limitations• Proposed Architecture• Tools and Technologies• Development Timeline• Current Status

/ 3Introduction

• Knowledge Management

• Key factor Evolution of knowledge

• Major challenge Knowledge maintenance

• To handle dynamic knowledge generation and maintenance

• Intelligent and effective system to provide better quality of service

• Feedback to enhance knowledge maintenance capabilitieshttp://www.journal.forces.gc.ca/vo4/no1/images/McIntyre-4-fig3-eng.gif

/ 4Motivation

CHAL

LEN

GES

High Quality of Contents

Change Management

Data Inconsistency (updation, maintenance)

Evolution of Knowledge (expert, feedback, learning)

/ 5Related Work

Phases System / Study / Prototype Features and Limitations

KME

Knowledge Creation Dimitriadis [1], Kwiatkowska [2], Bachman [3]

Features:• Handle noisy, highly variable data• Extract new knowledge • Create effective set of decision rules• Worked on Time and space domainLimitations:• Used fixed machine learning methods (ZeroR, NaiveBayes,

J48, SVM)• Human is involved

Knowledge MaintenanceBachman [3], S.Auer [4], K.Kaljurand [5], M.Afzal [6], R.Regier [7], J.Dinerstein [8]

Features:• Maintain rules• Semi-automatic maintenance• Evidence SupportLimitations:• Single-Level maintenance• Manual maintenance• Manual tuning

/ 6Limitations

• Fixed machine learning method

• Manual rules generation

• Single-level maintenance

• Manual maintenance

• Manual rules tuning

/ 7

Evolutionary Knowledge Maintenance

Change Management

Inconsistency Detection

Mapping and logging

Human Expert

Knowledge Maintenance Engine

Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation

Knowledge Data Broker

Schema Filtration

Schema Validation

Broker InterfaceKnowledge Builder

Learner

SelectorData and Algorithm Characterization

Meta-Features Computation Algorithm Selection

Machine Learning M

ethods

Data-Algorithm Training Data

ML Algorithm Performance Evaluation

Meta Features Computation

Algorithm Selection Model Creation

Algorithm Selection Models

Confidence Level Checker

Coverage Analysis

Satisfaction Analysis

Functional Evaluation

Rule ValidationRule Tuning

Expert Authoring Interface

Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation

Evidence Presentation

Intermediate Data

HDFS data Access Interface

Feedback Analysis

Recommendation

UI / UX

Proposed Architecture

/ 8

Intermediate Data


Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human Expert


Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation


Filtered Data {John, Over Eaten, Walk}

Features {Entropy, Stand. Deviation, Mean}

Learned Data {x=john && y=normal, Run}

Algorithm space {SVM, ANN, NB, …, J48}

Selected Algorithm {J48}Provide Feedback {hConfidence, Rule}

Update Rules {x=john && y=normal, Walk}

Structured Data {1,John,Normal,Chlos,Lunch,Walk}

Query {Concept1 and Concept2,…, and Conceptn}

Evidence List {Evidence1, Evidence2,…, Evidencen}

Concepts {Jogging, Run, Normal}

Rules {x=john && y=normal, Run}

Update Rules {x=john && y=normal, Walk}

Knowledge Maintenance Knowledge Creation

1

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4 5

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1 1

1

1

1

1

2

3

4

5

6

4 5

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7

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3

4

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3

4

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Model Creation Case-1 Case-2

Case-3

Filtered Data {Data1, Data2, Data3}

/Knowledge Creation 9

Intermediate Data


Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human Expert


Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation


Model Creation | Model Execution

/

User Id Name Health Condition ……… Activity

1 John Normal ……… Walk

2 Alice Abnormal ……… Run

… …. ….. …… ….. 10

Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human Expert


Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation


2

User ID Person Age

1 John 35

2 Alice 29

… …. …..

3

User Disease Level Location

1 2 Suwon

2 1 Yongin

… …. …..

User ID Condition Activity

1 Normal Run

2 Abnormal Walk

… …. …..

4

Dataset Algorithm Performance

4

Dataset Algorithm

Data 1 J48

Dataset Entropy Std. Dev. Mean …….

Data 1 0.43 0.55 35.6 …..

Data 2 0.15 0.22 17.7 …..

Data 3 0.25 0.36 48.2 …..

…… …….. ……… ……. …..

Dataset Entropy Std. Dev. Mean ……. Algorithm

Data 1 0.43 0.55 35.6 ….. J48

Data 2 0.15 0.22 17.7 ….. SVM

Data 3 0.25 0.36 48.2 ….. NB

…… …….. ……… ……. ….. ……...

5


If (Entropy<=0.35 & Mean <50) then Algorithm=J48

If (Entropy>=0.35 & Mean >50) then Algorithm=SVM

………

6

7

Intermediate Data


1

ML MethodsSVM

Data 1 SVM 0.74

J48Data 1 J48 0.86

NB

Data 1 NB 0.80

…..

….. ….. …..

Data 1 J48 0.86J48

Data 2 SVM

Data 3 NB

…… ……..

/Knowledge Creation 11

Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human Expert


Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation


Model Creation | Model Execution

Intermediate Data


/ 12

Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human Expert


Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation


User Id Name Health Condition ……… Activity

1 John Normal ……… Walk

2 Alice Abnormal ……… Run

… …. ….. …… …..

2

Intermediate Data


1

User Id Name Activity

1 John Walk

2 Alice Run

… …. …..

3

4

Entropy Std. Dev. Mean …..

0.30 0.65 25.6 …..


If (Entropy>=0.35 & Mean >50) then Algorithm = SVM

If (Entropy<0.35 & Mean <=50) then Algorithm = J48

………

5

J48

6

Health Condition

Run Walk

Person

Age > 30

=Normal

Age <= 30

Diet

=Over Eaten

7

/ 13Knowledge MaintenanceCase-1 | Case-2 | Case-3

/ 14

Intermediate Data


Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human Expert


Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation

Evidence Presentation1

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4 5

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9

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4

5

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Knowledge MaintenanceCase-1 | Case-2 | Case-3

/ 15

Intermediate Data


Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human Expert


Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation


1

1

1

2

3


ID Rule Satisfaction

Rule-1 (Activity = Eating && Disease=Yes && Temp=low) => (Health_condition = Abnormal) 70%

Rule-4 (Activity = Jogging && Disease=No) => (Health_condition = Normal) 90%

...... ……… …….

Rule-n (Activity = BUs&& Diet= Imbalance && Temp=high) => (Health_condition = Dizziness) 85%

Rule-4 (Activity = Jogging || Disease=No) => (Health_condition = Normal)

4

Rule Rule Modified On

Rule-1 (Activity = Eating && Disease=Yes && Temp=low) => (Health_condition = Abnormal) 10-07-2014

Rule-4 (Activity = Jogging && Disease=No) => (Health_condition = Normal) 25-06-2014

Rule-4 (Activity = Jogging || Disease=No) => (Health_condition = Normal) 24-07-2014

….. ……. ……

5


/ 16

Intermediate Data


Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human Expert


Knowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation


1

1

1

2

3

4

5




Rule-4 (Activity = Jogging && Disease=No) => (Health_condition = Abnormal) 20%

Rule-4 (Activity = Jogging && Disease=No) => (Health_condition = Abnormal) 15%

...... ……… …….

6

/ 17

Intermediate Data


Feedback Analysis


Change Management


Mapping and logging

Recommendation

UI / UX

Human ExpertKnowledge Bases

ServiceKB

InformationKB

DataKB

Query Builder

Query Formulation

Query Validation


Schema Filtration

Schema Validation


Learner



Machine Learning M

ethods







Coverage Analysis





Rules Extractor

Concept Extractor

Editing Interface

Editor Mapper

Concept Repository

Evidence Support

Evidence Searching

Query Generation


1


1

1

1

2



4

Concepts Possible Values

Activity Jogging | Eating | Bus Traveling

Disease Yes | No | Diabetes …..

Diet Balance | Imbalance5

6

Concept Values

Activity Jogging

Eating

Bus Traveling

If Activity = and Disease = Jogging

Eating

Travelling

Diabetes

No

Yes

ID Rule

Rule-2 (Activity = Eating && Disease=Yes && Temp=low) => (Health_condition = Abnormal)


…… ………

Rule-n (Activity = BUs&& Diet= Imbalance && Temp=high) => (Health_condition = Dizziness)

Query (Jogging and Diabetes …. and condition)

4

Search(Online Sources, Query) Example: Pubmed

5

Title Date Journal Type

Dietary and lifestyle factors in relation to pla 2010 Med J Exercise

Novel approaches to obesity prevention 2012 J Diabetes Food

6

Rule-1 (Activity = Eating || Disease=No) => (Health_condition = Normal)

3 7

8

9

/Tools and Technologies

• Java• Weka• IBM SPSS Statistics• JSP• JavaScript

18

/Development Timeline 19

Finding Service Issue

KME Refinement (1st Service)

Broker Interface GUI Development

(1st Service)

Model Creation for Knowledge Builder

(1st Service)

Model Execution for Knowledge Builder

(1st Service)

Implementation of Maintenance cases

(1st Service)

KME Refinement based on Results

Report -1st Service

Report – 2nd Service



(2nd Service)


(2nd Service)


(2nd Service)


(2nd Service)

KME Refinement (2nd Service)



KME Refinement (3rd Service)


(3rd Service)


(3rd Service)


(3rd Service)


(3rd Service)


Report- 3rd Service

Final Report

Final checking and supplements of overall project

KME Adjustments reflecting whole service checking result

Checking whole service based on survey result

Satisfactory Survey based on Prototype for Service Refinement

1st Year

2nd Year

3rd Year

4th Year

/Current Status

• Literature survey of the existing systems on knowledge creation and maintenance• Had meeting with consortium member on 27th June 2014.• Redesign of the Architecture based on comments from consortium

member• Redesign of Selector module (Knowledge Generation)• Redesign of Confidence Level Checker (Knowledge Maintenance)

• Study on Integration/interfacing with other MM Modules• Write initial draft of SRS document• Designing UML Diagrams

20

21/References

• [1] Dimitriadis, S.; Goumopoulos, C., "Applying Machine Learning to Extract New Knowledge in Precision Agriculture Applications," Informatics. PCI '08. pp.100-104, 2008

• [2] Kwiatkowska, E.J.; Fargion, G.S., "Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data," Geoscience and Remote Sensing, IEEE Transactions on , vol.41, no.12, pp.2844-2860, Dec. 2003.

• [3] Bachman, R. E.; Hoffman, R. D.; Johnson, V. M.; McDavid, D. W.; Mazina, D. I., "Search engine facility with automated knowledge retrieval, generation and maintenance." U.S. Patent No. 7,216,121. 8 May 2007.

• [4] Auer, S.; Lehmann, J., "Creating knowledge out of interlinked data."Semantic Web 1.1, 2010, 97-104.

• [5] Kaljurand, K., "ACE View---an Ontology and Rule Editor based on Attempto Controlled English." OWLED. 2008.

• [6] Afzal, M.; Hussain, M.; Khan, W.A.; Ali, T.; Lee, S.; Kang, B.H., “KnowledgeButton: An Evidence Adaptive Tool for CDSS and Clinical Research.” INISTA14, 2014.

• [7] Regier, R.; Gurjar, R.; Rocha, R. A., "A clinical rule editor in an electronic medical record setting: development, design, and implementation." AMIA Annual Symposium Proceedings. Vol. 2009.

• [8] Dinerstein, J.; Dinerstein, S.; Egbert, P.K.; Clyde, S.W., "Learning-Based Fusion for Data Deduplication," Machine Learning and Applications, 2008. ICMLA '08. , pp.66-71, Dec. 2008.

QuestionsThank You!

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mining minds knowledge maintenance engine maqbool ali maqbool ali khu memberkhu member byeong ho...

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knowledge maintenancebachman

dynamic knowledge generation

maintenance intelligent

set of slides

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