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Pragati’s Expoze ́ Tool Suite for Harmonization

Explore the Ontology Tool Suite by Pragati for better ontology design, quality assurance, mapping, and more in Digital Rights Management (DRM). Learn about the Expozé Contribution Areas for DRM. Discover how to effectively cluster knowledge bases for optimal results.

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Pragati’s Expoze ́ Tool Suite for Harmonization

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  1. Pragati’s Expozé Tool Suite for Harmonization Mala Mehrotra Dmitri Bobrovnikoff Pragati Synergetic Research, Inc. mm@pragati-inc.com Expedition Workshop/Designing the DRM NSF, Arlington, VA. August 16 2005

  2. Outline • Motivation • Ontology and Ontological Issues • Expozé Tool Suite • MVP-CA Technology Core • Expozé Contribution Areas for DRM • Knowledge Entry • Quality Assurance • Mapping • Candidate Examples from Preliminary Analysis of FEA • Conclusion

  3. What is an Ontology? An ontology is an explicit formal specification of the terms in the domain and relations among them • Concepts arise from the objects of interest in the environment, and the purpose to which they are subjected • Interrelationships between the concepts depends upon the behavioral characteristics of the objects, and the operational characteristics of the environment Why do we need an Ontology? • Formalized semantics of concepts allows automated reasoning with the concepts • Enabling enhanced functionalities in a system • A common lingua supports interoperability, collaboration and sharing across systems

  4. Ontological Design Principles Ontological engineers try to optimize the ontological design • Parsimonious design of concept classes • Crispness in the distinctions across concepts • Richness in the associations across concepts

  5. Ontological Concerns • Information overload is occurring in the creation of ontologies • Every organization “thinks” their “core ontology” will be the Holy Grail for ontologies • Reality #1: The notion of a canonical ontology is, at least at present, a myth • Reality #2: We currently have to live with a cloud of candidate ontologies which model a “real” concept from different perspectives Ontology Developer’s Dilemma: How can I effectively find and reuse concepts from that “cloud”?

  6. Ontological Issues Conceptual/Modeling Differences • Level of Abstraction • Concepts are too specialized Example: Ford Taurus, Toyota Camry, Honda Accord => Automobiles • Concept is too general: Example: Move => Move-Into, Move-To, Move-Out-Of, Move-Through • Placement in the ontological hierarchy Different choices on specifying ontological distinctions for orthogonal characteristics Example: An ontology for organizing clothes line is different for (a) department store layout for customers Gender (mens’, womens’) (b) ordering clothes from a manufacturer Clothes-type (pants, shirts)

  7. Ontological Issues Term Relationships • Vicinity Terms – Terms related via common usage patterns Example: Pour, Immerse, Permeate • Complementary/Inverse terms Example:Move-From & Move-To, Exit and Enter • Homonym Terms - Context determines the semantics Example: Contract -> physical change vs. legal document Culture -> societal issues vs. biological experiment • Overloaded Terms – Same semantics for very different contexts Example: ObjectFoundInLocation

  8. Ontological Issues • Lexically and semantically close terms Example: Move & Move-Into, Touches & TouchesDirectly Prevent & Prevents • Lexically distant but semantically close terms Example: providesCoverInCOA & providesConcealmentInCOA TaskTypeRequiresAgentType,opTypeRequiresAgentType • Lexically reversed but semantically close terms Example: ForwardPassageOfLines-MilitaryOperation & PassageOfLines-Forward-MilitaryTask

  9. Pragati’s Vision Provide tools for the Ontology developers: • Development – Knowledge Entry aids • Reuse – Knowledge Discovery aids • Interoperability – Mapping/Merging aids • Maintenance – Quality Assurance aids Representations supported: • Axiomatized ontologies • Knowledge Bases • Loosely structured text (reports, manuals, etc.) Basic Tenet: Clustering the information system into semantically-related groups facilitates a variety of software engineering tasks.

  10. Expozé: Pragati’s Tool Suite Clustering Engine Analysis Engine Vicinity Concepts Generator QA Engine Clustered Artifacts Repository Artifact adaptor Relationship Extractor Mapping Engine Query Engine Import/Export Plugins S-S-Text OWL MELD/CycL Template Extractor Repository Manager XMDR SCL CLIPS XMI ….. SemanticWeb Ontologies/ KBs/ Semi-Structured Systems COE with MVP-CA backend MVP-CA: Cluster Analysis Tool IOD: Iterative Ontology Development Tool OSRT: Ontology Search and Reuse Tool

  11. Core-Technology: Multi-ViewPoint Clustering Analysis Approach: Cluster a knowledge base from multiple perspectives • Clustering of knowledge bases into groups of semantically-related rules/axioms reveals • Relationship of terms in the context of their usage • Prototypical patterns of usage for the terms in the axioms • Multiple ways of clustering (based on different objective criteria) aid in understanding and analyzing KBs from different perspectives

  12. Utilizing Expozé Tool Suite for DRM 2 2 Registry 3 3 1 1 DOS DHS • Expoze Tool Suite Contribution Areas • Knowledge Discovery & Entry • Quality Assurance • Mapping & Harmonizing Data Models Screening COI Diagram from M. Daconta’s presentation

  13. MVP-CA Interface: Navigating FEA Ontology* Understanding the ontology: MVP-CA tool supports exploration and browsing of concept clusters (dendrogram representing cluster formation shown here) * FEA Ontology developed by TopQuadrant

  14. Some Concept Clusters in FEA Ontology component composition value transfer measurement area siblings reference models measurement indicators business / service Some high-level concepts extracted from a preliminary analysis of the FEA ontology (BRM, PRM, SRM & TRM)

  15. COE – Expozé Interface:Searching for a concept Two complementary viewpoints: definitional view of FEA OWL axioms (left) and vicinity concepts view across ontologies given the search term “component” (right)

  16. FEA Ontology Template Possible deviation from naming convention? Template: common patterns abstracted for knowledge entry

  17. Structural Harmonization Cyc’s BioChemistry Mt. - - K Q M N D R A C G T A C G U N A G T U C D D D R R R D R • A nucleotide molecule can be represented by • holding the sugars constant at first level and varying the base (left figure) or • holding the base constant at first level and varying the sugar (right figure) • The left representation good for chain type of reasoning for the molecule that is at the nucleotide level. • The right representation good for the matching base pair type of level of reasoning. • Clustering brought to attention both these representations. Sugar-dependent representation Base-dependent representation

  18. Axiom Clusters for NucleotidesCyc’s BioChemistry Mt. - - K Q M (#$genls #$Thymine-Deoxyribonucleotide #$Deoxyribonucleotide) (#$genls #$Adenine-Deoxyribonucleotide #$Deoxyribonucleotide) (#$genls #$Cytosine-Deoxyribonucleotide #$Deoxyribonucleotide) (#$genls #$Guanine-Deoxyribonucleotide #$Deoxyribonucleotide) Clusters showing multiple legitimate representations of Nucleotides shown graphically in the last slide (#$genls #$Uracil-Ribonucleotide #$Ribonucleotide) (#$genls #$Adenine-Ribonucleotide #$Ribonucleotide) (#$genls #$Cytosine-Ribonucleotide #$Ribonucleotide) (#$genls #$Guanine-Ribonucleotide #$Ribonucleotide) Sugar-dependent representation (#$genls #$Deoxyribonucleotide #$Nucleotide) (#$genls #$Ribonucleotide #$Nucleotide) (#$genls #$Nucleotide #$Molecule) (#$genls #$AdenineNucleotide #$Nucleotide) (#$genls #$CytosineNucleotide #$Nucleotide) (#$genls #$GuanineNucleotide #$Nucleotide) (#$genls #$Adenine-Ribonucleotide #$AdenineNucleotide) (#$genls #$Adenine-Deoxyribonucleotide #$AdenineNucleotide) (#$genls #$Cytosine-Deoxyribonucleotide #$CytosineNucleotide) (#$genls #$Cytosine-Ribonucleotide #$CytosineNucleotide) (#$genls #$Guanine-Deoxyribonucleotide #$GuanineNucleotide) (#$genls #$Guanine-Ribonucleotide #$GuanineNucleotide) Base-dependent representation

  19. Mapping Opportunity in FEA:ServiceComponent(SRM) & ServiceCategory(TRM) - - K Q M

  20. Future Work • In-Depth FEA analysis • Extend the FEA OWL axioms analysis to DRM Instances Analysis across COIs • Semi-Automatic Extraction of Ontologies from XML Data Model Instances • Build Mapping Aids for Interoperating across various DRM Models

  21. ROI for DRM Effort • Cost-Effective Solution for Building and Organizing Data Models & Ontologies • Less time needed • Less personnel needed • Effective reuse of existing systems • Quality Solution enabling high-end analysis for • Development • Maintenance • Interoperability • Adaptive Solution to Changing Demands • In time as data models & ontologies evolve across applications • In perspective for different types of needs from various COIs

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