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Ontology Enabled Analysis and Interoperability

January 31, 2003

Elisa F. Kendall

CEO & Founder

[email protected]

(650) 960-2456

opportunity

The convergence of   Internet technologies, XML dramatic advances in compute power, storage and network availability and capacity   a return to core competencies increasing globalization have created an environment where collaboration within and across organizations is no longer optional.

Examples:  Mergers & acquisitions  Collaborative design and development, concurrent engineering    Just-in-time and outsourced manufacturing Service & operations Corporate business operations (HR and employee empowerment, 360º CRM)  Bio and medical informatics

One of the greatest challenges facing CPC is interoperability.

high tech manufacturing

Standards, Methods

Semiconductor Manufacturers

DSPs

Value Added IP Vendors

(A,,C,D,Z, (A,B,C,D,Z, ...

Customer Designs

...

New Block

next generation collaboration

 Solutions need to reflect both extensive domain knowledge and cross-domain, interdisciplinary interaction  Interaction may include both  dynamic and/or transitory relationships  stable, long-term participation by individuals, organizations, and resources  Mandates automation of complex interactions among interdependent activities  Requires an understanding of the differences in business rules and terminology and a mechanism for resolving ambiguity and conflicts

knowledge-based interoperability

Ontologies and ontology brokering technologies

 can resolve ambiguity and conflicts among information producers, consumers, and repositories  provide a rigorous, precise, and complete mechanism for describing and maintaining shared IP  enable understanding of the distinctions in complex data, business rules, and terminology  support communication that facilitates automation of complex interactions among interdependent activities

component-based methodology

   Need to move the bar forward on component-based vs. monolithic ontology development to address scalability issues Layering and aligning ontology components facilitates scalability  top-level, upper, fundamental meta knowledge definition layer  foundation definition layer (

e.g.,

industry standards)  domain or “classic ontology” definition layer  further refined (and distinct) interface, process, services, and role (user, application, resource) definition layers UML-based approach supports component modeling strategy  enables collaborative ontology development  provides capabilities for maintenance, configuration management  supports best practices in software engineering (documentation, test, reuse and ontology sharing)  begins to address scalability issues

product roadmap

   Visual Ontology Modeler – Import/export of DAML+OIL, OWL, RDFS ontologies – Port to Rational XDE – Model analysis, merging, alignment capabilities – FOL expression editor – Import/export of KIF/CG based ontologies Knowledge Base Development Environment – Automated generation/maintenance of object database for ontology management, instance data management – Interactive ontology browsing and analysis, merging, alignment, verification motivated in part by Chimaera – Instance data editor, with automated template construction from ontologies Reference Ontologies and Library Development – Semi-automated ontology development capabilities based on keywords from text mining tools, database schemas, XML schemas – Additional ontology development based on ISO, industry standards

product roadmap

  Context-driven Search – Automatically generate complex queries for Verity, other search engines from ontologies and instance data – Provide context for text mining applications (

e.g

., inxight, Mohomine) Ontology Brokering Environment – Concept resolution between ontologies, using techniques such as name matching, synonym lookups, description matching, structural analysis, statistical relevance and weighting algorithms that can be manipulated by the user, and other algorithms, based in part on Anchor-PROMPT, work done by USC/ISI, OBSERVER, and Chimaera – Automated generation of transformations for use by other interoperability components – Inference capabilities for long term, more sophisticated concept matching and resolution – Query decomposition and planning based on InfoSleuth – Results, explanation generation and justification through JTP, Vivomind analogy engine – Multi-agent framework for distributed ontology brokering – candidates include Fujitsu’s java-based implementation of their April framework

Medius® Visual Ontology Modeler

Rational Rose ® 2002 Rose Enterprise Edition Medius ® Visual Ontology Modeler Medius ® UML Ontology Library KB Development Environment RDFS / DAML+OIL / OWL Ontology Knowledge Base Ontology Modeling Environment



Top-level ontology components (e.g., IEEE SUO/SUMO)

 

Meta-knowledge representation components (concepts from KIF, OKBC) Scientific notation data types and rules for conversion (SI Units, ISO Standards)



Generates XML Schema, W3C Web Ontology Language (DAML+OIL, OWL)



Domain ontologies based on customer requirements

VOM capabilities

   

Features

A distributed, multi-user development environment in a rich, graphical notation A rich set of ontology authoring wizards that create and maintain the required UML model elements for the user, saving time and substantially reducing construction errors and inconsistencies.

Automated export of XML schema, RDFS / DAML+OIL Integration with a commercial, scalable object database that supports efficient evaluation, comparison, validation, and management of ontologies with OKBC and CORBA access    

Benefits

Makes ontology modeling accessible to subject matter (domain) experts and others less skilled in knowledge representation Supports model configuration management using industry standard tools (

e.g.,

ClearCase) for team development of large ontologies Automated model construction and code generation saves time and substantially reduces errors and inconsistencies Beta user productivity gain in DAML generation and maint. was higher than anticipated (

e.g.,

ontology restructuring, dependency mgmt., reuse, ontology use across multiple KR paradigms)

ontology broker capabilities

Deliver

– – – –

Relevant results From the user’s perspective In the user’s vernacular C++, XML, KIF M E D I U S ® Knowledge Brokering Suite Intelligent Agents Resolution Engine Ontologies Resolve

–

Ambiguity and conflicts in terms

–

Across applications and organizations

–

Cross-references, complex relationships Support Services solution planning, design, & implementation

Enhance

–

Workflow Automation

–

Customizable Agents subscription / notification, data fusion, vocabulary enhancement Bridge and Aggregate

– –

Complex data and rich metadata Context and Content 11

phase I broker environment

Medius Ontology Modeler Ontology Generation External Ontologies (DAML, OWL) Ontology Analyzer Ontology Server

Information Broker

Reasoning Engine Ontology Storage

Query and Results

Phase 1 - Interactive Ontology Diagnosis and Reconciliation Phase 1 Features



Ontology Manager – Object-Oriented Database, CORBA Interface



Ontology Analyzer – Ontology Rationalization and Term Resolution



Ability to read generic DAML+OIL or XML from the Ontology Modeler

phase II broker environment

Client Domain Manager Agent-based Communication Service & Interface Manager Ontology Manager

Information Broker

Ontologies & Maps Data Sources & Problem Solving Methods

Phase 2 – Static Ontology Alignment and Initial Service Layers Phase 2 Features



Enhancements to the term resolution functionality



Generation of Static Ontology Alignment Maps

  

Domain Manager for resolution of incoming messages Service / Interface Manager for interacting with Data Sources Initial integration with Agent based communication

phase III broker environment

Client Agent Communication Process Manage r Agent Communication Domain Manager Agent Communication

Information Broker

Ontologies & Maps Ontology Manager Interface Manager Service Manager Data Sources & Problem Solving Methods

Phase 3 – Figure 6. Near Real-Time Ontology Reconciliation Phase 3 Features



Further enhancements to the term resolution functionality



Dynamic term resolution in the Domain Manager

 

Process Manager for handling process driven interactions with PSL Split Service and Interface Managers for improved interactions with Data Sources



Further usage of Agent based communication

collaborative design

Subsystem Design Systems Integration Engineering IT

Specialty Tools Design and Modeling

(CASE, MCAD, ECAD, etc.)

Productivity & Communication Engineering Business Applications Foundation Infrastructure

Component Design

M E D I U S



Knowledge Brokering Suite Intelligent Agents Resolution Engine Ontologies

–

Prioritize & monitor product portfolio

–

Quicker decision making due to availability of relevant information

–

Maximize reuse

–

Leverage capabilities of outsource partners, joint ventures Support Services solution planning, design, & implementation

–

Engineering discipline-specific

–

Process-related

–

Company-specific

–

Organizational

–

Role / Function

–

Language-specific 15

service & operations

M E D I U S



Knowledge Brokering Suite

Hangar Maintenance Line Maintenance

Intelligent Agents Resolution Engine Ontologies

Component Shops

Support Services solution planning, design, & implementation

Maintenance Execution Planning & Scheduling

–

Device-independence

–

Subscription / notification

–

Goal-directed searching

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Timely, relevant, accurate

–

Aviation-specific

–

FAA related

–

Airline-specific

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Organizational

–

Role / Function

–

Language-specific

unique capabilities

        

Addresses the need for people and applications to meaningfully share information Bridges incompatible resources, reducing integration development time and limiting need for migration or intermediate storage Delivers information dynamically as the user wants it Aggregates information for content richness Enhances vocabulary as context and usage changes Platform, interface, and device independent – scaleable, flexible, standards-based architecture Leverages existing investments in UML Focuses on content by creating multidimensional relationships and interaction rules across critical business information Enables development and deployment of intelligent agents capable of subscription/notification, data fusion, application integration and other complex interactions through an intelligent agent framework

Document

Transcript Document

Ontology Enabled Analysis and Interoperability

opportunity

One of the greatest challenges facing CPC is interoperability.

high tech manufacturing

next generation collaboration

knowledge-based interoperability

Ontologies and ontology brokering technologies

component-based methodology

product roadmap

product roadmap

Medius® Visual Ontology Modeler

VOM capabilities

Features

Benefits

ontology broker capabilities

phase I broker environment

phase II broker environment

phase III broker environment

collaborative design

service & operations

unique capabilities

Directory