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Semantic Business Process Management

Lecture 5 – Semantic Technologies I

OMG Ontology Definition Metamodel

Prof. Dr. Adrian Paschke

Corporate Semantic Web (AG-CSW)

Institute for Computer Science, Freie Universitaet Berlin

paschke@inf.fu-berlin.de

http://www.inf.fu-berlin.de/groups/ag-csw/

Arbeitsgruppe

Problem: Only Syntactic BPM Models

Lacks of Web Service Technology

Current BPM technologies allow usage of Web Services

But: only syntactical information descriptions

syntactic support for discovery, composition and execution

=> Web Service usability, usage, and integration needs to be inspected manually

no semantically marked up content / services

no support for the Semantic Web rules and ontologies

=> current Web Service Technology Stack failed to

realize the promise of Web Services

Overview

Overview Semantic Technologies

Ontologies

OMG Ontology Definition Metamodel

W3C Web Ontology Language

Rules

OMG SBVR

OMG PRR

W3C RIF

RuleML

Semantic Computing Technologies

4. Software Agents and Web-based Services Rule Responder, FIPA, Semantic Web Services, …

3. Rules and Event/Action Logic & Inference RIF, SBVR, PRR, RuleML, Logic Programming

Rule/Inference Engines,…

2. Ontologien ODM, CL, Topic Maps RDFS, OWL Lite|DL|Full, OWL 2,

…

1. Explicit Meta-data and Terminologies vCard, PICS, Dublin Core, RDF, RDFa, Micro Formats,

FOAF, SIOC …

1. Explicit Metadata on the Web

Metadata are data about data

Metadata on the Web: Machine processable information about information on the Web

Projects e.g., PICS, Dublin Core, RDF, FOAF, SIOC, …

Problem domains: Syntax:

Which representation and interchange format for metadata?

Semantics: Which metadata are allowed for resources (metadata vocabulary, schema)

Association problem: How to connect metadata with resources (who defines the metadata, are

metadata separated from the content, etc.)

2. Ontologies

“An ontology is an explicit specification of a conceptualization “ T. Gruber

Ontologies described the common knowledge of a domain (semantics): Semantics interoperability between (connected) vocabularies

Typical components:1. Classes (concepts) of the domain

2. Properties (roles) of the classes

3. Constraints

4. Individuals (instances) of classes

3. Rules (Logic and Inference)

Logic is a discipline concerned with the principles of inference and reasoning

Formal languages for the representation of knowledge with clear semantics Declarative knowledge representation:

express what is valid, the responsibility to interpret this and to decide on how to do it is delegated to an interpreter / reasoner

Automated reasoner, e.g., a rule engine, can derive conclusions from given knowledge (inference)

4. Software Agents and Semantic Web Services

Intelligent Software Agents act autonomously and pro-active They have an internal knowledge base with decision/reaction logic (e.g.

rule-based expert systems)

Examples: Personal agents (e.g. Rule Responder), search robots

Web Service In general: any IT service provided on the Web

“A 'Web service' (also Web Service) is defined by the W3C as "a software system designed to support interoperable Machine to Machine interaction over a network." Web services are frequently just Web APIs that can be accessed over a network, such as the Internet, and executed on a remote system hosting the requested services.” (Wikipedia)

=> no clear separation between web agents and web services (in the broad sense) but level of self-autonomous decisions is higher in web agents

Ontologies

Aristotle - Ontology

Before: study of the nature of being

Since Aristotle: study of knowledge representation and reasoning

Terminology: Genus: (Classes)

Species: (Subclasses)

Differentiae: (Characteristics which allow to group or distinguish objects from each other)

Syllogisms (Inference Rules)

[Aristotle] Science of Being, Methapysics, IV, 1

What is an Ontology? (in IT)

An Ontology is a

formal specification Executable, Discussable

of a shared Group of persons

conceptualization About concepts; abstract class

of a domain of interest e.g. an application, a specific area, the “world model”

[Gruber 1993] - T.R. Gruber, Toward Principles for the Design of Ontologies Used forKnowledge Sharing, Formal Analysis in Conceptual Analysis and KnowledgeRepresentation, Kluwer, 1993.

Requirements for Ontology Languages

Ontology languages allow users to write explicit, formal conceptualizations of domain models

The main requirements are:

a well-defined syntax

efficient reasoning support

a formal semantics

sufficient expressive power

convenience of expression

Concept - Instance

Concept / Class / Universal (Metaphysics)

an abstract or general idea inferred or derived from specific

instances

Instance / Individual / Particular (Metaphysics)

object in reality, a copy of a abstract concept with actual values for

properties

Person

Person

Name: Adrian Paschke

Teaches: Computer Science

LivesIn: Berlin

WorksAt: Freie Universität Berlin

Types of ontologies

[Guarino et al. 1999] - N. Guarino, C. Masolo, G. Vetere. OntoSeek: Content-BasedAccess to the Web. In: IEEE Intelligent Systems, 14(3), 70--80, 1999.

Taxonomy

Taxonomy := Segmentation, classification and ordering of elements into a classification systemaccording to their relationships

Object

Person DocumentTopic

Student LetterResearcher Movie

Doctoral Student PhD Student

Thesaurus

Terminology for a specific domain

Taxonomy plus fixed relationships (similar, synonym, related to)

originate from bibliography

Object

Person DocumentTopic

Student LetterResearcher Email

similar

Doctoral Student PhD Student

synonym

related to

Topic Map

Topics (nodes), relationships, and occurrences of documents

ISO-Standard

typically for navigation and visualisation

Object

Person DocumentTopic

Student Letter

Doctoral Student

Researcher Email

PhD Student

synonym

similar

writes

knows described_in

Tel Affiliation

related to

Ontology (in our sense)

Representation Languages: ODM, RDF(S); OWL; Predicate Logic; F-Logic, ISO CL,…

Object

Person DocumentTopic

Student LetterResearcher Email

is_similar_to

knows described_in

Doctoral StudentPhD Student

Tel

Affiliation

Affiliation

is_a-1

is_a-1

is_a-1

is_a-1

is_a-1

is_a-1

instance_of-1

is_a-1

Hans Muster

is_a-1

FUB+49 030 608 ….

T D T D

D T P T

described_in

is_about knows

is_about

Pwrites

RULES, e.g.:

writes

related_to

Formality of KR Languages

Many Ontology Languages

Entity Relationship Modell

UML with OCL

Frames

Predicate Logic

Common Logic

Description Logic (formal Semantics, Reasoning)

SHOE, XOL, OML, SKOS, OBO

RDFS, DAML+OIL -> OWL

ODM

…

No special ontolgy languages,

but might be used to describe

ontologies

Ontologies and their relatives

Based on AAAI’99 Ontologies Panel – McGuiness, Welty, Ushold, Gruninger, Lehmann

Ontologies and their relatives (2)

24

Standards/Recommendations/Specificationsfor Semantic Computing

ISO/IEC JTC 1/SC 32

ISO/IEC

11179

Metadata

Registries

Metadata Registry

TerminologyThesaurusTaxonomy

Data

Standards

Ontology

Structured

Metadata

Terminology

CONCEPT

Referent

Refers To Symbolizes

Stands For

“Rose”,

“ClipArt

Rose”

ISO TC 37

Semantic

Web

W3C

Object

Management

MOF

ODM

PRR

SBVR

OMG

Node

Node

Edge

Subject

Predicate

Object

Graph RDF(S) / OWL

RIF

Ontology Definition Metamodel

OMG ODM

OMGOntology Definition Metamodel (ODM)

ODM is the OMG standard for model driven ontology

development

Adopted as an OMG standard in October 2006

http://www.omg.org/cgi-bin/doc?ptc/2007-09-09

Not one model, but a family of metamodels

Supports exchange of independently developed models

Provides standard profiles for ontology development in UML

Enables consistency checking and validation of models in

general

Ontology Definition Metamodel

ODM brings together the communities by providing:

Broad interoperation within Model Driven Architecture

MDA tool access to ontology based reasoning capability

UML notation for ontologies and ontological interpretation

of UML

OMG MOF and OMG MDA

Excurse

OMG MOF

The Meta-Object Facility (MOF) is an Object Management Group (OMG) standard for model-driven engineering.

M0 Layer Concrete representation of data.

M1 Layer Models, e.g. knowledge models, process modes, UML / object

models, which define the data on the M0 layer.

M2 Layer Meta-Models. Define the structure and architecture of models.

M3 Layer Meta-Meta-Models (MOF layer). Abstract layer, which is used to

define the M2 layer.

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

MOF

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 ID

OMG Model-driven Architecture (MDA) is a kind of domain engineering, and supports model-driven engineering (MDE)

1. Computation Independent Model (CIM)

2. Platform Independent Model (PIM)

3. Platform Specific Model (PSM)

Insulates business applications from technology evolution, for Increased portability and platform

independence

Cross-platform interoperability

Domain-relevant specificity

OMG MDA

OMG MDA - MOF 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

MDA tools take models (e.g. MOF M1-3 models) as input and generate models as output

MDA principles can also apply to other areas such as business rules / ontologies modeling and business process modeling

MDA from a Knowledge Representation Perspective

Enterprise integration 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 business rules / processes driven applications to leverage existing knowledge, rules, processes 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

back to OMG ODM …

Five EMOF platform independent metamodels (PIMs), four

normative

Mappings (MOF QVT)

UML2 Profiles

RDFS & OWL

Topic Maps

Collateral

XMI

Java APIs

Proof-of-concepts

Conformance

RDFS & OWL

Multiple Options

TM, CL Optional

Informative Mappings

CL

<<metamodel>>

TM

<<metamodel>> RDFS

<<metamodel>>

(from RDF)

RDFWeb

<<metamodel>>

(from RDF)

OWLBase

<<metamodel>>

(from OWL)

merge

DL

<<metamodel>>

RDFBase

<<metamodel>>

(from RDF)merge

merge

RDF

<<metamodel>>

OWLDL

<<metamodel>>

(from OWL)

merge

OWLFull

<<metamodel>>

(from OWL)

merge

merge

OWL

<<metamodel>>

(non-normative)

Model Driven Ontology Development: ODM Overview

ODM defines … Platform Independent (Normative) Metamodels (PIMs) include

RDFS & OWL – abstract syntax, constraints for OWL DL & OWL Full, several

compliance options

ISO Common Logic (CL)

ISO Topic Maps (TM)

Informative Models DL Core, relatively unconstrained Description Logics based metamodel

Identifier (keys) model extension to UML for ER

UML Profiles

RDFS/OWL Profile

Topic Maps Profile

Set of Mappings

UML to OWL,

Topic Maps to OWL

RDFS/OWL to Common Logic

ODM UML Profiles and Metamodels

Metamodels

To “precisely” represent the abstract syntax of target

ontology definition languages

UML mappings

To leverage existing UML models and ontologies

UML profiles

To facilitate the use of UML notation (and tools) for

ontology modeling

The ODM Architecture

<<metamodel>>

UML2

<<metamodel>>

DL

<<metamodel>>

SCL

<<metamodel>>

TM

<<metamodel>>

OWL

<<metamodel>>

RDFS

<<metamodel>>

ER

extension mapping

Ontology

Modeling

Languages

Ontology Description

Languages

NOTE: UML2 metamodel is an existing OMG standard

UML

Profiles for

Ontology

-- RDFS

-- OWL

-- TM

UML

NotationsOntology

Logic

Languages

dependency

Topic Maps

Topic Maps represent another XML Schema based

approach for conceptual knowledge representation

Topic Maps are collections of topics, each of which

represent a single subject, related to one another by

associations

Similar to ER in some respects

Originally based on the notion of a publications index

Used primarily in Europe, increasing interest in US

Recently standardized by ISO

ISO 13250 – Data Model and XML Serialization

ISO 18024 – Query Language

ISO 19756 – Constraint Language

ODM TM Metamodel OverviewTop Level Constructs

TopicMapConstructs are the basic element in the ODM TM

TopicMap is a collection of MapItem that are it‟s Topics

and Associations

Topics may, and typically do, have a set of Characteristics

Characteristic MapItem

Association

TopicMapConstruct

Topic

0..n +characteristic

0..n {set}

/hasA

Locator 0..n

+sourceLocator

{set} 0..n

TopicMap 0..n +content

0..n {set}

/containment

1

0..n

+parent 1

+topics 0..n {set}

1

0..n

+parent 1

+associations 0..n {set}

ODM TM OverviewCharacteristics

AssociationRoles connect

Topics together into

Associations

similar to UML Association

Ends, or UML Properties in

UML 2.0

Occurrences define

attributes of Topics

similar to UML Attributes

Names represent human

readable labels or

descriptions

they are not identifying.

ODM TM Topic Identifiers and Locators

TM distinguishes two types of Locators

Identifiers – The entity is about the subject.

Locators –The entity located is the subject.

ODM TM Scoping and Typing

Topics are class-like in that they can be used as „types‟

A Topics Characteristics and Associations may be limited to a specified scope.

AssociationCharacteristic

Scope_able

Topic

0..n

+scope

0..n

Type_able

0..1

+type

0..1

Common Logic Metamodel Overview

Sentence

Name

name : String

ExclusionSet

0..*

0..*

+excludedName0..*

+exclusionSet

0..*

ExcludedName

Importation

Phrase

Module

0..1 0..*+exclusionSet

0..1

+module0..*

ExcludedSet

Identifier

1

1

+localDomain1

+module1

ModuleName

1

0..*

+assertedContent1

+context0..*

NameForImportation

Comment

comment : String

Text

0..*

0..*

+phrase

0..*

+text0..*

PhraseForText

1

0..*

+body1

+moduleForBody

0..*

ModuleBody

0..1

0..*

+identifierForText0..1

+namedText0..*NameForText

0..*

0..1

+commentForText

0..*

+commentedText0..1

CommentedText

Provides a first-order, more expressive logic metamodel for ODM

– Next generation KIF, designed for the Semantic Web

– In use by DoD, intelligence community, researchers world-wide

– Needed to support complex process, service semantics

– Grounds the logical formulations of SBVR

Metamodel developed synergistically with ISO Common Logic

Common Logic Phrases

CL Terms & Atoms

Sentences

Boolean Sentences

There are no explicit 'true' and 'false' elements in the metamodel. These are empty cases of Conjunction (true) and Disjunction (false). That is why a Disjunction or Conjunction of zero sentences is allowed.

Quantified Sentences

Description Logics Metamodel

Many variations on DLs, depending on application requirements and reasoning capabilities (OWL represents a commonly used subset)

Resource Description Framework (RDF) Metamodel Overview

RDFBase – primary

package

Reflects basic abstract

syntax from RDF Concepts

Minimal implementation

requirements, e.g., for RDF

triple/quad store

RDFS – adds vocabulary

related to RDF Schema

RDFWeb – fits the model

to the Web via document

model

RDFS

<<metamodel>>

RDFBase

<<metamodel>>

RDFWeb

<<metamodel>>

merge

merge

RDF

<<metamodel>>

(from org.omg.odm)

RDFBase Package - Statements

Supports named graphs (e.g., per SPARQL), reification, blank node identifiers, essentially RDF basics

Limited coverage to RDF Concepts document rather than along namespace boundaries, which didn‟t work from a UML perspective

Promotion of the blank node identifier to RDFSResource addresses MOF multiple classification, non-normative work-around

RDFS Package –Classes & Utilities

RDFS assists us in “getting around”MOF multiple

classification limitations through rdf:type

RDFS Package –Properties

Note that rdf:domain and rdf:range are global properties – limiting their

usage enhances reusability of ontology components

RDFWebPackage –Documents

Web Ontology Language (OWL) Metamodel Overview

OWL metamodel components

include:

OWLBase: common abstract

syntax & constraints

OWLDL: OWL DL constraints

OWLFull: OWL Full constraints

“Semantic MOF” or SMOF spec,

currently in work at OMG

fills in the gaps for MOF multiple

classification

provides additional capabilities for

KR applications, SBVR, domain-

specific languages

OWLBase

<<metamodel>>

OWL

<<metamodel>>

(from org.omg.odm)

RDFBase

<<metamodel>>

(from RDF)

RDFS

<<metamodel>>

(from RDF)

merge

RDFWeb

<<metamodel>>

(from RDF)merge

RDF

<<metamodel>>

(from org.omg.odm)

OWLDL

<<metamodel>>

OWLFull

<<metamodel>>

mergemerge

merge

merge

Excerpt OWL Metamodel

The OWL metamodel is implemented by extending the RDFS metamodel.

OWLBase Package –OWL Ontology

OWLBase Package –OWL Classes

OWLBase Package –Restrictions

OWLBase Package –OWL Properties

UML Profile for RDF & OWL

Intended to be highly intuitive for UML users

Reuses UML constructs when they have the same semantics as OWL When this is not possible, stereotypes UML constructs

that are consistent and as close as possible to OWL semantics

Uses standard UML 2 notation In the few cases where this is not possible, follows the

clarifications and elaborations of stereotype notation defined in UML 2.1

Key Features of the RDF Profile

rdfs:Resource is modeled as UML::InstanceSpecification

Introduction of <<reifies>> stereotype of UML::Dependencyto allow such instance specifications to reify classes, properties, individuals, statements, etc.

rdf:Property is modeled as UML::AssociationClass and UML::Property, to provide greatest possible flexibility

Several possible representations of various aspects of rdf:Property

RDF Property Subsetting Options

Example OWL Number, Value Constraints

OWL Cardinality –Restricted Mulitplicity in Subtype

OWL allValuesFrom –Property Redefinition

OWL Property Redefinition (allValuesFrom) Using Association Classes

OWL Intersection, Union, Complement

OWL Disjointness Options

Simple binary disjoint relationship

Disjointness, multiple participants,

common parent

Disjointness, multiple participants, no

common parent

OWL Inverse Options

Simple inverse relationship

Inverse relationship among association classes

ODM UML-OWL Bridge

UML to OWL Transformation

Example: Museum UML Model

Example: UML2OWL Transformation

MDA-based Ontology Engineering with ODM

ODM Summary

Standard for model driven ontology development ODM brings together the Software Engineering and Knowledge

Representation communities

Platform Independent (Normative) Metamodels (PIMs) include –RDF & OWL – abstract syntax, constraints for OWL DL & OWL

Full, several compliance options

–ISO Common Logic (CL)

–ISO Topic Maps (TM)

Informative Models –DL Core –high-level, relatively unconstrained Description Logics

based metamodel (non-normative, informational)

Identifier (keys) model extension to UML for ER

Adopted as an OMG standard in October 2006

Questions ?

Literature

OMG Ontology PSIG http://www.omg.org/ontology/

OMG ODM 1.0http://www.omg.org/spec/ODM/1.0/

Eclipse ATL ODM http://www.eclipse.org/m2m/atl/usecases/ODMImplementation/