The Model-Driven Semantic Web Emerging Technologies & Implementation Strategies Elisa Kendall...

The Model-Driven Semantic WebEmerging Technologies & Implementation Strategies

Elisa KendallSandpiper Software

September 8, 2005

2Copyright ©2005 Sandpiper Software, Inc.

Model Driven Architecture® (MDA®)

Insulates business applications from technology evolution, for

– Increased portability and platform independence– Cross-platform interoperability– Domain-relevant specificity

Consists of standards and best practices across a range of software engineering disciplines

– The Unified Modeling Language (UML®)– The Meta-Object Facility (MOF™)– The Common Warehouse Metamodel (CWM™)

MOF defines the metadata architecture for MDA

– Database schema, UML and ER models, business and manufacturing process models, business rules, API definitions, configuration and deployment descriptors, etc.

– Supports automation of physical management and integration of enterprise metadata

– MOF models of metadata are called metamodels


MOF-Based Metadata Management

MOF tools use metamodels to generate code that manages metadata, as XML documents, CORBA objects, Java objects

Generated code includes access mechanisms, APIs to– Read and manipulate– Serialize/transform– Abstract the details based on

access patterns Related standards:

– XML Metadata Interchange (XMI®)

– CORBA Metadata Interface (CMI) – Java Metadata Interface (JMI)

Metamodels are defined for– Relational and hierarchical

database modeling– Online analytical processing

(OLAP)– Business process definition,

business rules specification– XML, UML, and CORBA IDL

Model 1

Model 2

Metamodel A

Transformation Model

Metamodel B

language used

language used

transformation

source language

target language


The Semantic Web

"The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation."

-- Tim Berners-Lee

“the wedding cake”“the bus”


Level Setting

An ontology specifies a rich description of the Terminology, concepts, nomenclature Properties explicitly defining concepts Relations among concepts (hierarchical and lattice) Rules distinguishing concepts, refining definitions and

relations (constraints, restrictions, regular expressions)

relevant to a particular domain or area of interest.


Ontology-Based Technologies Ontologies provide a common vocabulary and definition of rules

for use by independently developed resources, processes, services

Agreements among companies, organizations sharing common services can be made with regard to their usage and the meaning of relevant concepts can be expressed unambiguously

By composing component ontologies, mapping ontologies to one another and mediating terminology among participating resources and services, independently developed systems, agents and services can work together to share information and processes consistently, accurately, and completely.

Ontologies also facilitate conversations among agents to collect, process, fuse, and exchange information.

Improve search accuracy by enabling contextual search using concept definitions and relations among them instead of/in addition to statistical relevance of keywords.


Semantic Web Evolution

Draft specifications for RDF/S & OWL became formal W3C Recommendations in February 2004

Increasingly attention is on applications and deployment strategies, moving from research to early adoption

Research from the DAML community and W3C is now focused on– Methodology & best practices – representing value spaces,

complex relations, engineering methods, applications with richly specified ontologies

– Query and rule languages– Tools and development resources

• Parsers and Validators• Authoring Tools for ontologies and mark-up• Ontologies, knowledge bases, and libraries

– Interaction with SOAP/WSDL to support Semantic Web Services (OWL-S, SWRL)


Guiding Principles

Historically, knowledge representation and reasoning systems have operated under closed world assumptions

Uncertainty is magnified under open-world, “wild, wild web” conditions, making reasoning more difficult

Semantic web languages are designed to support less certainty, provide “better” search results, informed answers to questions, not absolutes

Based on XML, they can assist businesses in leveraging mark-up, content, and data – To augment business intelligence/analysis and knowledge mining – To support knowledge sharing and collaboration, augment

enterprise information integration – Enrich web services and other applications – Support policy-based applications and ensure compliance with

policy

at a lower cost with higher potential ROI than traditional computing methods


Cost of Knowledge Capture Most tools are oriented towards taxonomy or limited ontology

formation vs. knowledge base development, terminology reconciliation, or alignment

They require significant knowledge of formal logic and domain analysis from an ontological perspective

Few are graphical and/or standards based

Vulcan estimates the cost of encoding 50 pages of basic high school chemistry textbook knowledge at $10,000 per page*

Tools to assist subject matter experts (SMEs) in encoding knowledge, including increasing automation in developing the starting points for ontology and KB development are essential

Combining MOF® and MDA® technologies with KR dramatically reduces cost, increases likelihood of success, increases availability to a much broader community of potential users

* See Vulcan, Inc. – Project Halo, at http://www.projecthalo.com/


MDA from the KR Perspective EII solutions rely on strict adherence to agreements based on

common information models that take weeks or months to build

Modifications to the interchange agreements are costly and time consuming

Today, the analysis and reasoning required to align multiple parties’ information models has to be done by people

Machines display only syntactic information models and informal text describing the semantics of the models

Without formal semantics, machines cannot aid the alignment process

Translations from each party’s syntactic format to the agreed-upon common format have to be hand-coded by programmers

MOF® and MDA® provide the basis for automating the syntactic transformations


MOF and KR Together

MOF technology streamlines the mechanics of managing models as XML documents, Java objects, CORBA objects

Knowledge Representation supports reasoning about resources– Supports semantic alignment among differing vocabularies and

nomenclatures– Enables consistency checking and model validation, business rule analysis – Allows us to ask questions over multiple resources that we could not

answer previously– Enables policy-driven applications to leverage existing knowledge and

policies to solve business problems• Detect inconsistent financial transactions• Support business policy enforcement• Facilitate next generation network management and security applications

while integrating with existing RDBMS and OLAP data stores MOF provides no help with reasoning KR is not focused on the mechanics of managing models or metadata Complementary technologies – despite some overlap


Metadata Management Scenarios


Classifying Ontologies

Level of Complexity

Level of

Expre

ssiv

ity

Simple Taxonomy

Glossary

Topic Map

Concept Map

Hierarchical Taxonomy

Entity – Relationship Model

Database Schema

OO Software Model

KR System

XML Schema

Classification techniques are as diverse

as conceptual models; and generally

include understanding

Methodology

Target Usage

Level of Expressivity

Level of Complexity

Reliability / Level of Authoritativeness

Relevance

Amount of Automation

Metrics Captured and/or Available


Model Dynamics

Model centric perspectives characterize the ontologies themselves and are concerned with their structure, formalism and dynamics.

Perspective

Level ofAuthoritativeness

Source of Structure

Degree of Formality

Model Dynamics

Instance Dynamics

One Extreme

Least authoritative, broader shallowly defined ontologies

Passive (Transcendent) - Structure originates outside the system

Informal or primarily taxonomic

Read-only, ontologies are static

Read-only, resource instances are static

Other Extreme

Most authoritative, narrower, more deeply defined ontologies

Active (Immanent) - Structure emerges from data or behavior

Formal, having rigorously defined types, relations, and theories or axioms

Volatile, ontologies are fluid and changing

Volatile, resource instances change continuously


Application Characteristics

Application centric perspectives are concerned with how applications use and manipulate ontologies.

Perspective

Control/Degree

of Manageability

ApplicationChangeability

Coupling

Integration Focus

Lifecycle Usage

One Extreme

Externally focused, public(little or no control)

Static (with periodic updates)

Loosely-coupled

Information integration

Design Time

Other Extreme

Internally focused, private(full control)

Dynamic

Tightly-coupled

Application integration

Run Time


Ontologies have well-defined, formal semantics:– To support reasoning– To represent shared semantics for intelligent agents or semantic

web services– To represent declarative policies (with legal implications, such as

regulatory policies)

Elements frequently cross meta-levels or apply at multiple levels (e.g., act as a class and as an individual at the same time)

Reuse of complex relationships and axioms is critical to successful development:– Relations are first class elements– Associations in UML2 are insufficient to meet this need

Individuals may be specified independently and may have properties that are not properties of the classes of which they are members (e.g., owl:sameAs, owl:differentFrom)

Knowledge Representation vs. UML


Forward & reverse engineering of source material and ontologies

Support ontologies expressed in existing description logics (e.g., OWL DL) and a limited set of higher order languages (e.g., OWL Full, Common Logic)

UML Profiles vs. Metamodels– Both are required due to structural and semantic distinctions

between KR languages and UML– Metamodels precisely represent the abstract syntax– Profiles facilitate use of UML notation and “code” generation

Extending the UML2 metamodel vs. new, distinct metamodels– Extension would introduce unnecessary complexity, unintended

semantics

Should there be a core ODM metamodel that the others extend?– No obvious commonalities– Goal was to be true to the abstract syntax and formal semantics of

the selected KR languages – without unnecessary complexity

Design Rationale


Towards a Model Driven Semantic Web – Ontology Definition Metamodel Six EMOF platform independent metamodels,

(PIMs), five normative Mappings (MOF QVT Relations Language

planned) UML2 Profiles

– RDFS & OWL– TM

Collateral– XMI– Java APIs– Proof-of-concepts

Conformance– RDFS & OWL– All else optional

TM<<metamodel>>

DL<<metamodel>>

ER<<metamodel>>

SCL<<metamodel>>

RDFS<<metamodel>>

OWL<<metamodel>>

Ontology Definition Metamodel

Completed/Included Mapping Planned Mapping

UML<<metamodel>>

(nonnormative)

CL<<metamodel>>


Subclassing vs. Mapping– OWL metamodel depends on RDFS metamodels– All others are related through mapping (non-normative)

Naming– Namespaces are defined at the package level in UML & MOF– Namespaces are global in RDFS/OWL, CL, & TM

Associations– Relations and Properties are “first class entities” in RDFS/OWL & CL– AssociationEnds must be defined in UML (requires model library)– Inheritance of features, attributes (slots), axioms, other relevant

production rules on associations is poorly supported in UML2, not at all previously (requires use of UML2 properties, association classes)

Packaging– Discussions underway to resolve RDF/S packaging structure– RDF Schema & RDF Syntax, or possibly 3 packages

OCL is insufficiently rich to support rules, axioms

Impedance Mismatches & Issues


Provide standard UML notation for each of the languages

RDFS/OWL was highest priority based on RFP

Topic Maps was high priority based on user community input

What about ER and CL?– ER has an existing graphical notation, though no “standard”

notation – a profile will likely be done under a broader data modeling standardization effort (forthcoming – CWM2)

– CL was originally included for constraint/rule representation; profile requested due to its utility for BSBR and interoperability with other ISO efforts; will follow through an RFC/RFP process

Essentially a one-to-one mapping from concrete meta-model element to stereotype

Abstract meta-classes contribute to base classes for stereotypes

ODM Profiles


OWL Full was selected as the core ODM metamodel to limit NxN mapping requirements

Final specification will include – Two-way mappings from UML2, TM, & ER to RDFS/OWL– One-way mapping from RDFS/OWL to CL– Reverse (lossy) mapping from CL to RDFS/OWL may be

done through RFP/RFC– Two-way mapping from UML2 to CL (lossy) may be done

through RFP/RFC

Current mapping representations include tables, abstract syntax, an interim QVT notation (i.e., inconsistent)

Plans include migration to emerging MOF Query / View / Transformation (QVT) Relations Language

ODM Mappings


Bridging KR and MDA


Technology Architecture


ODM Status

Several revision cycles on the specification to date

Informative discussions of usage scenarios, differences between UML & OWL

Platform Independent Metamodels (PIMs) include– Resource Description Framework and Web Ontology

Language (covers abstract syntax, common concrete syntactic elements from both)

– Common Logic (CL), based on draft ISO CD 24707– Topic Maps (TM), based on draft ISO FCD 13250-2

specification– ER – based on de facto industry standards– DL Core – high-level, relatively unconstrained Description

Logics based metamodel (non-normative, informational)

Revised submission (next iteration) will be posted by 11/14/05 to the OMG web site

Presentation on 8/22 revision planned for OMG Atlanta meeting (September 12-16 – see http://www.omg.org/docs/ad/05-08-01.pdf)

Plans for recommendation / vote for adoption December meeting


Implementation Strategies

Native OWL Tool UML Tool with ODM Plug-In

Ontology Analysis & Validation

Java Theorem Prover(JTP)

Hybrid Reasoning System

Use Reasoning to Find Inconsistencies

Validate Logic, Policies

MOF RepositoryTools

XMI Representation Enables Use in Eclipse/EMF, MOF-Based Tools

Link ODM Models (conceptual / semantic) To logical (ERWIN, Rose) and physical models (ERWIN, Rose,

Power Designer)

MOF/ODM –OWL Bridge

Use UML to Refine Linkage Ontologies

Use Links / Mappings to Find & Eliminate Redundancies

thru Reasoning

Use Links / Mappings For Query Planning, Business

Intelligence

RDFS/OWL


Related OMG Standards Work

Business Semantics for Business Rules – Emerging standard, Semantics for Business Vocabularies &

Rules (SBVR) – Depends on the ODM CL Metamodel for logical grounding

Production Rules Representation– Emerging standard, preliminary presentation in Atlanta 9/12– Discussions in progress to relate PRR to CL

CL will provide the logical foundation for all OMG ontology and rules related standards

Mappings between ODM & SBVR, ODM & PRR, ODM & EXPRESS are planned

Relationship to emerging W3C standards for rules, semantic web services will depend on how the standards evolve

Plan in Ontology PSIG is to extend or build on ODM to support both rules and semantic web services vocabularies, potentially other vocabularies as they are developed


OMG Standards & Zachman Framework

Pro

du

cti

on

Ru

les

RF

P

Se

ma

nti

cs

fo

r B

us

ine

ss

Vo

ca

bu

lari

es

& R

ule

s

Ontology Definition Metamodel


Related ISO Metadata Standards

ISO/IEC 11179 – Metadata Registries– Part 2: Classification for Administered Items– Part 3: Registry Metamodel & Basic Attributes

ISO/IEC 19763 – Framework for Metamodel Interoperability– Part 3: Metamodel Framework for Ontology Registration

(depends on ODM Metamodels, ISO 11179 classification)– Part 4: Metamodel Framework for Mapping (depends on

MOF QVT specification)

XMDR Program – Extended Metadata Registration– OWL ontologies for ISO 11179 Part 3, related MMF

elements– discussions bridging the standards are underway– Liaison meeting (ODM, ISO, XMDR) planned for 9/22-23

in Toronto prior to ISO WG meeting


Business Integration

Semantic Web Services standards are converging (OWL-S and SWSL)

OMG RFP forthcoming for extensions to ODM to support Semantic Web Services, EXPRESS, eventually SWRL (when a rule language is selected/formalized)

Business Semantics for Business Rules joint revised submission, called “Semantics for Business Vocabularies & Rules (SBVR)” is logically grounded in Common Logic / ODM CL Metamodel

Potential mapping to forthcoming Production Rule specification

Leverage mapping from UML for BPEL to ODM extensions

Strategy:– Link business process models through MOF environment– Generate OWL for the linkage– Use linkage as basis for mediating business process semantics


A Framework for Next Generation Interoperability MOF’s model management facilities and KR capabilities for

machine interpretable semantics and reasoning are separate, complementary concerns

The ability of reasoners to find discrepancies in invariant rules, preconditions, and post conditions, can add scalability to MDA’s use of Design-by-Contract (DBC)

UML profiles can serve as graphical notations for Semantic Web languages, dramatically increasing ease of use

The combination of MDA and SW technologies promises to – Address the missing link in business process automation– Enable true information interoperability and continuity– Support next generation policy-based applications development


The Model-Driven Semantic Web

Knowledge acquisition, developing the semantics is the bottleneck

Leveraging existing assets breaks that bottleneck

Correlation through reasoning provides the utility– Multi-dimensional, cross organizational tailored

semantic views– “Virtual” repository approach enables elimination of

redundancy – Reasoning supports quality initiatives through

inconsistency discovery, model and content validation

MDA and MOF coupled with Semantic Web technologies are the key


Questions / Discussion


John Sowa’s Top-Level Categories


Physical Quantities & Dimensions


Constant Quantities & Units


Standard International Units


Example Measurement Class Definition


Example Corrosion Rate Definition

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