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Maqbool Ali

KHU Member. UTAS Member. Maqbool Ali. Byeong Ho Kang. Agenda. Introduction Motivation Related Work Limitations Proposed Architecture Tools and Technologies Development Timeline Current Status. Introduction. Knowledge Management Key factor Evolution of knowledge

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Maqbool Ali

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  1. KHU Member UTAS Member Maqbool Ali Byeong Ho Kang

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

  3. Introduction • 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 enhanceknowledge maintenance capabilities http://www.journal.forces.gc.ca/vo4/no1/images/McIntyre-4-fig3-eng.gif

  4. Motivation High Quality of Contents • Data Inconsistency (updation, maintenance) CHALLENGES Change Management • Evolution of Knowledge (expert, feedback, learning)

  5. Related Work

  6. Limitations • Fixed machine learning method • Manual rules generation • Single-level maintenance • Manual maintenance • Manual rules tuning

  7. Proposed Architecture Broker Interface Knowledge Maintenance Engine Knowledge Builder Evolutionary Knowledge Maintenance Evidence Support Expert Authoring Interface HDFS data Access Interface Selector Evidence Searching Query Generation Concept Extractor Data and Algorithm Characterization Meta Features Computation ML Algorithm Performance Evaluation Intermediate Data Concept Repository Feedback Analysis Recommendation UI / UX Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder Data-Algorithm Training Data Editing Interface Schema Filtration Query Formulation Editor Mapper Algorithm Selection Model Creation Confidence Level Checker Query Validation Schema Validation Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management Learner Algorithm Selection Models Rule Tuning Rule Validation Mapping and logging Inconsistency Detection Meta-Features Computation Algorithm Selection Knowledge Bases Machine Learning Methods Human Expert Service KB Information KB Data KB

  8. Update Rules {x=john && y=normal, Walk} Query {Concept1 and Concept2,…, and Conceptn} Case-1 Model Creation Rules {x=john && y=normal, Run} Case-2 Filtered Data {Data1, Data2, Data3} Concepts {Jogging, Run, Normal} Case-3 Broker Interface Evidence List {Evidence1, Evidence2,…, Evidencen} Algorithm space {SVM, ANN, NB, …, J48} Filtered Data {John, Over Eaten, Walk} Structured Data {1,John,Normal,Chlos,Lunch,Walk} Knowledge Maintenance Engine 1 4 2 Knowledge Builder Evolutionary Knowledge Maintenance Evidence Support Expert Authoring Interface 5 5 4 HDFS data Access Interface Selector 1 1 Evidence Searching Query Generation Concept Extractor Data and Algorithm Characterization 6 Meta Features Computation ML Algorithm Performance Evaluation Intermediate Data 6 Concept Repository 4 Feedback Analysis 2 UI / UX 1 Recommendation Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder Data-Algorithm Training Data 3 3 7 Editing Interface Query Formulation Schema Filtration 6 5 3 3 1 3 Editor Mapper Algorithm Selection Model Creation Confidence Level Checker Schema Validation Query Validation 1 4 Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management 8 4 2 Learner Algorithm Selection Models Rule Tuning Rule Validation Mapping and logging Inconsistency Detection 4 5 1 Meta-Features Computation Algorithm Selection 6 5 7 9 Features {Entropy, Stand. Deviation, Mean} Knowledge Bases Machine Learning Methods Human Expert Selected Algorithm {J48} Provide Feedback {hConfidence, Rule} Service KB Information KB Data KB Knowledge Creation Knowledge Maintenance Learned Data {x=john && y=normal, Run} Update Rules {x=john && y=normal, Walk}

  9. Knowledge Creation Model Creation|Model Execution Broker Interface Knowledge Maintenance Engine Knowledge Builder Evolutionary Knowledge Maintenance Selector Evidence Support Expert Authoring Interface Data and Algorithm Characterization HDFS data Access Interface Meta Features Computation ML Algorithm Performance Evaluation Evidence Searching Query Generation Concept Extractor Intermediate Data Concept Repository Feedback Analysis Recommendation UI / UX Data-Algorithm Training Data Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder Editing Interface Query Formulation Schema Filtration Algorithm Selection Model Creation Editor Mapper Learner Confidence Level Checker Schema Validation Query Validation Algorithm Selection Models Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management Rule Tuning Rule Validation Mapping and logging Inconsistency Detection Meta-Features Computation Algorithm Selection Human Expert Knowledge Bases Machine Learning Methods Service KB Information KB Data KB

  10. MLMethods NB J48 SVM J48 ….. Broker Interface Knowledge Maintenance Engine Knowledge Builder 1 Evolutionary Knowledge Maintenance 2 Selector Evidence Support Expert Authoring Interface Data and Algorithm Characterization HDFS data Access Interface 4 4 Meta Features Computation ML Algorithm Performance Evaluation Evidence Searching Query Generation Concept Extractor Intermediate Data Concept Repository Feedback Analysis 5 Recommendation UI / UX Data-Algorithm Training Data Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder Editing Interface 3 Query Formulation Schema Filtration 6 Algorithm Selection Model Creation Editor Mapper Learner Confidence Level Checker Query Validation Schema Validation 7 Algorithm Selection Models Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management Rule Tuning Rule Validation Mapping and logging Inconsistency Detection Meta-Features Computation Algorithm Selection Human Expert Knowledge Bases Machine Learning Methods Service KB Information KB Data KB

  11. Knowledge Creation Model Creation |Model Execution Broker Interface Knowledge Maintenance Engine Knowledge Builder Evolutionary Knowledge Maintenance Selector Evidence Support Expert Authoring Interface Data and Algorithm Characterization HDFS data Access Interface Meta Features Computation ML Algorithm Performance Evaluation Evidence Searching Query Generation Concept Extractor Intermediate Data Concept Repository Feedback Analysis Recommendation UI / UX Data-Algorithm Training Data Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder Editing Interface Schema Filtration Query Formulation Algorithm Selection Model Creation Editor Mapper Learner Confidence Level Checker Schema Validation Query Validation Algorithm Selection Models Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management Rule Tuning Rule Validation Mapping and logging Inconsistency Detection Meta-Features Computation Algorithm Selection Human Expert Knowledge Bases Machine Learning Methods Service KB Information KB Data KB

  12. Person Age <= 30 Age > 30 Diet =Over Eaten =Normal Broker Interface Knowledge Maintenance Engine Knowledge Builder 1 Evolutionary Knowledge Maintenance 2 Selector Evidence Support Expert Authoring Interface Data and Algorithm Characterization HDFS data Access Interface Meta Features Computation ML Algorithm Performance Evaluation Evidence Searching Query Generation Concept Extractor Intermediate Data Concept Repository Feedback Analysis Recommendation UI / UX Data-Algorithm Training Data Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder Editing Interface 3 Query Formulation Schema Filtration 6 Algorithm Selection Model Creation Editor Mapper Learner Confidence Level Checker Query Validation Schema Validation Algorithm Selection Models Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management Rule Tuning Rule Validation Mapping and logging Inconsistency Detection 4 5 Meta-Features Computation Algorithm Selection Human Expert 7 Knowledge Bases Machine Learning Methods Health Condition Service KB Information KB Data KB Run Walk

  13. Knowledge Maintenance Case-1 | Case-2 | Case-3

  14. Knowledge Maintenance Case-1 | Case-2 | Case-3 Broker Interface Knowledge Maintenance Engine 4 Knowledge Builder Evolutionary Knowledge Maintenance Evidence Support Expert Authoring Interface 5 5 4 HDFS data Access Interface Selector Evidence Searching Query Generation Concept Extractor Data and Algorithm Characterization 6 Meta Features Computation ML Algorithm Performance Evaluation Intermediate Data 6 Concept Repository 4 Feedback Analysis UI / UX 1 Recommendation Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder Data-Algorithm Training Data 3 3 7 Editing Interface Schema Filtration Query Formulation 5 3 1 Editor Mapper Algorithm Selection Model Creation Confidence Level Checker Query Validation Schema Validation 1 Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management 8 4 2 Learner Algorithm Selection Models Rule Tuning Rule Validation Mapping and logging Inconsistency Detection 1 Meta-Features Computation Algorithm Selection 6 5 9 Knowledge Bases Machine Learning Methods Human Expert Service KB Information KB Data KB

  15. Knowledge Maintenance Case-1| Case-2 | Case-3 Broker Interface Knowledge Maintenance Engine Knowledge Builder Evolutionary Knowledge Maintenance Selector Evidence Support Expert Authoring Interface Data and Algorithm Characterization HDFS data Access Interface Meta Features Computation ML Algorithm Performance Evaluation Evidence Searching Query Generation Concept Extractor Intermediate Data Concept Repository Feedback Analysis UI / UX 1 Recommendation Data-Algorithm Training Data Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder 3 Editing Interface Query Formulation Schema Filtration 1 Algorithm Selection Model Creation Editor Mapper Learner Confidence Level Checker Schema Validation Query Validation 1 Algorithm Selection Models Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management 4 2 Rule Tuning Rule Validation Mapping and logging Inconsistency Detection Meta-Features Computation Algorithm Selection 1 Human Expert 5 Knowledge Bases Machine Learning Methods Service KB Information KB Data KB

  16. Knowledge Maintenance Case-1| Case-2 | Case-3 Broker Interface Knowledge Maintenance Engine Knowledge Builder Evolutionary Knowledge Maintenance 4 Selector Evidence Support Expert Authoring Interface Data and Algorithm Characterization HDFS data Access Interface Meta Features Computation ML Algorithm Performance Evaluation Evidence Searching Query Generation Concept Extractor Intermediate Data Concept Repository Feedback Analysis UI / UX 1 Recommendation Data-Algorithm Training Data Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder Editing Interface Query Formulation Schema Filtration 5 3 1 Algorithm Selection Model Creation Editor Mapper Learner Confidence Level Checker Query Validation Schema Validation 1 Algorithm Selection Models Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management 2 Rule Tuning Rule Validation Mapping and logging Inconsistency Detection Meta-Features Computation Algorithm Selection 1 Human Expert 6 Knowledge Bases Machine Learning Methods Service KB Information KB Data KB

  17. Knowledge Maintenance If Activity = and Disease = Case-1 | Case-2 | Case-3 Broker Interface Knowledge Builder Evolutionary Knowledge Maintenance Selector Evidence Support Expert Authoring Interface Data and Algorithm Characterization 5 5 4 HDFS data Access Interface Meta Features Computation ML Algorithm Performance Evaluation Evidence Searching Query Generation Concept Extractor 6 Intermediate Data 6 Concept Repository 4 Feedback Analysis UI / UX 1 Recommendation Data-Algorithm Training Data Evidence Presentation Rules Extractor Knowledge Data Broker Query Builder 3 7 Editing Interface Query Formulation Schema Filtration 1 Algorithm Selection Model Creation Editor Mapper Learner Confidence Level Checker Query Validation Schema Validation 1 Algorithm Selection Models Coverage Analysis Satisfaction Analysis Functional Evaluation Change Management 8 2 Rule Tuning Rule Validation Mapping and logging Inconsistency Detection Meta-Features Computation Algorithm Selection 1 Human Expert 9 Knowledge Bases Machine Learning Methods Service KB Information KB Data KB

  18. Tools and Technologies • Java • Weka • IBM SPSS Statistics • JSP • JavaScript

  19. Development Timeline 1st Year 2ndYear 3rd Year 4th Year

  20. 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

  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.

  22. Thank You! maqbool.ali@oslab.khu.ac.kr

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