The Semantic Web for Enterprise Architecture James Lapalme

Me, Myself and I Working on semantics and modeling problems since 2001 (E-learning, SoC) Enterprise architect at PSP Investments with a focus on Information PhD candidate at UdeM (MPSoC) IEEE/ACM author and presenter

Working on semantics and modeling problems since 2001 (E-learning, SoC)

Enterprise architect at PSP Investments with a focus on Information

PhD candidate at UdeM (MPSoC)

IEEE/ACM author and presenter

Objectives Introduction to the Semantic Web Application to Enterprise Architecture Discussion on Possible Trends

Introduction to the Semantic Web

Application to Enterprise Architecture

Discussion on Possible Trends

Agenda Enterprise Goals and Challenges The Semantic Web Information Modeling The Semantic Web in the Context of EA Future Applications

Enterprise Goals and Challenges

The Semantic Web

Information Modeling

The Semantic Web in the Context of EA

Future Applications

Scary Words Semantics Ontology Meaning Conceptualization Model Formal Metadata

Semantics

Ontology

Meaning

Conceptualization

Model

Formal

Metadata

Enterprise Architecture Goals Process Adaptation Rapid Time-to-Market Process Optimization Lower operational costs Knowledge Discovery Higher Return Data Quality Lower Risk

Process Adaptation

Rapid Time-to-Market

Process Optimization

Lower operational costs

Knowledge Discovery

Higher Return

Data Quality

Lower Risk

Information Challenges Ambiguous Semantics Communication Multiple Technologies Consistency Partially Known Value-Chain Operations Low Data Quality Decisions Poor Data Specification Expectations

Ambiguous Semantics

Communication

Multiple Technologies

Consistency

Partially Known Value-Chain

Operations

Low Data Quality

Decisions

Poor Data Specification

Expectations

Modern Solutions SOA Process Adaptation Complex-Event Processing Process Optimization Data Quality Program Data Quality Knowledge Mining Knowledge Discovery

SOA

Process Adaptation

Complex-Event Processing

Process Optimization

Data Quality Program

Data Quality

Knowledge Mining

Knowledge Discovery

Success Factors Entities and Events Well Defined Clear Expectations Precise Relations

Entities and Events

Well Defined

Clear Expectations

Precise Relations

Semantic Web

The Web Created for Document Sharing Focused on Presentation Adapted for Human to Human

Created for Document Sharing

Focused on Presentation

Adapted for Human to Human

The Semantic Web Scientific American (2001) Focused On Meaning Knowledge Representation Machine Consumption Metadata « Anybody can say Anything about Anything Anywhere »

Scientific American (2001)

Focused On

Meaning

Knowledge Representation

Machine Consumption

Metadata

« Anybody can say Anything about Anything Anywhere »

Syntax vs Semantic HTML and XML are syntax Machine cannot extract “meaning” from the current Web.

HTML and XML are syntax

Machine cannot extract “meaning” from the current Web.

Evoluation

Just Little Theory

Set theory

Function/Relation

Ressource Description Framework URI URI URI

Ressource Description Framework URIs are Surrogates for Things Simple Statements Subject, Verb, Object (triple) Literals based in XSD types Type is a standard Verb Uri and meaning XML and N3 are sterilization

URIs are Surrogates for Things

Simple Statements

Subject, Verb, Object (triple)

Literals based in XSD types

Type is a standard Verb

Uri and meaning

XML and N3 are sterilization

RDF Schema Permits Information Schema Definitions Based on Set Theory and First-Order Logic Adds Subjects/Objects Resource, Class, Property Adds Verbs SubClassOf, Domain, Range Defines Entailment Rules

Permits Information Schema Definitions

Based on Set Theory and First-Order Logic

Adds Subjects/Objects

Resource, Class, Property

Adds Verbs

SubClassOf, Domain, Range

Defines Entailment Rules

Web Ontology Language (OWL) Allows Schema Definitions (Description Logic) Information Schema Alignment Adds Subjects/Objects Restriction Adds Verbs subProperty, Inverse, Transitive, etc. Defines Entailment Rules OWL Lite, DL and Full

Allows

Schema Definitions (Description Logic)

Information Schema Alignment

Adds Subjects/Objects

Restriction

Adds Verbs

subProperty, Inverse, Transitive, etc.

Defines Entailment Rules

OWL Lite, DL and Full

Example

Semantic Web Stack

Information Modeling

Meaning Natural Language is Ambiguous Ambiguity can eliminated with Contextualization Contextualization can be define through Relations

Natural Language is Ambiguous

Ambiguity can eliminated with Contextualization

Contextualization can be define through Relations

Perception One Reality, Multiple Views of It Meaning is Relative to a Perception Perception is Contextualization

One Reality, Multiple Views of It

Meaning is Relative to a Perception

Perception is Contextualization

Glossary vs Taxonomy

Ontology Hyper-taxonomy Multiple intersecting taxonomies Meaning is define with rich and complex relations

Hyper-taxonomy

Multiple intersecting taxonomies

Meaning is define with rich and complex relations

Modeling Technologies (key differences) Generalization by Restriction Disjoint Sub-Classing Multiple Inheritance XSD Schema Conceptual ER UML

Applications to EA

Modeling Language OWL is a (quasi) superset of traditional model languages Non-Propriety file format Offer Formal Verification Offer Test-Driven Development Analysis (SPARQL)

OWL is a (quasi) superset of traditional model languages

Non-Propriety file format

Offer Formal Verification

Offer Test-Driven Development

Analysis (SPARQL)

Model-Driven Data Specification Definitions (Glossary) Natural Language Ontology (OWL) Relation and Context Rules Expectation Alignment (CWM) Mapping

Definitions (Glossary)

Natural Language

Ontology (OWL)

Relation and Context

Rules

Expectation

Alignment (CWM)

Mapping

Data Specification Governance “ Medium is the Message” Format is key Must be owned by the Business

“ Medium is the Message”

Format is key

Must be owned by the Business

Derived Artifacts Databases Schemas XSD Schemas OO Models Cleansing Rules Event Models Knowledge Domain Models

Databases Schemas

XSD Schemas

OO Models

Cleansing Rules

Event Models

Knowledge Domain Models

Available Tools Editor : TopBraid Composer, Semanticworks Storage : Oracle 10G Relational To RDF : Virtuoso Code : Jena, Linq To RDF IA : Pellet, Racer

Editor : TopBraid Composer, Semanticworks

Storage : Oracle 10G

Relational To RDF : Virtuoso

Code : Jena, Linq To RDF

IA : Pellet, Racer

Back to Goals SOA Semantically Unambiguous XSD Schemas which are aligned with Relational Schemas Complex-Event Processing Semantically Unambiguous Event Models Support of Event Inferencing Data Quality Program Governed Semantically Unambiguous Data Specification (Structure and Rules) Knowledge Mining Corporate Ontology which allow Knowledge Discovery

SOA

Semantically Unambiguous XSD Schemas which are aligned with Relational Schemas

Complex-Event Processing

Semantically Unambiguous Event Models

Support of Event Inferencing

Data Quality Program

Governed Semantically Unambiguous Data Specification (Structure and Rules)

Knowledge Mining

Corporate Ontology which allow Knowledge Discovery

Future Trends Semantic Databases RDF based Enterprise Application Integration Semantic Complex-Event Processing Semantic Business Intelligence Semantic Enterprise Information Integration Enterprise Information Management Unified Model

Semantic Databases

RDF based Enterprise Application Integration

Semantic Complex-Event Processing

Semantic Business Intelligence

Semantic Enterprise Information Integration

Enterprise Information Management

Unified Model

Questions