Semantic Business Process Management Lecture 5 Semantic Technologies I OMG Ontology Definition Metamodel

Transcription

1 Arbeitsgruppe 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

2 Problem: Only Syntactic BPM Models

3 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

4 Overview Overview Semantic Technologies Ontologies OMG Ontology Definition Metamodel W3C Web Ontology Language Rules OMG SBVR OMG PRR W3C RIF RuleML

5 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

6 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.)

7 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

8 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)

9 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

10 Ontologies

11 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

12 What is an Ontology? (in IT) An Ontology is a formal specification of a shared conceptualization of a domain of interest Executable, Discussable Group of persons About concepts; abstract class e.g. an application, a specific area, the world model [Gruber 1993] - T.R. Gruber, Toward Principles for the Design of Ontologies Used for Knowledge Sharing, Formal Analysis in Conceptual Analysis and Knowledge Representation, Kluwer, 1993.

13 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

14 Concept - Instance Concept / Class / Universal (Metaphysics) an abstract or general idea inferred or derived from specific instances Person Instance / Individual / Particular (Metaphysics) object in reality, a copy of a abstract concept with actual values for properties Person Name: Teaches: LivesIn: Adrian Paschke Computer Science Berlin WorksAt: Freie Universität Berlin

15 Types of ontologies [Guarino et al. 1999] - N. Guarino, C. Masolo, G. Vetere. OntoSeek: Content-Based Access to the Web. In: IEEE Intelligent Systems, 14(3), , 1999.

16 Taxonomy Object Person Topic Document Student Researcher Letter Movie Doctoral Student PhD Student Taxonomy := Segmentation, classification and ordering of elements into a classification system according to their relationships

17 Thesaurus Object Person Topic related to Document Student Researcher Letter similar Doctoral Student PhD Student synonym Terminology for a specific domain Taxonomy plus fixed relationships (similar, synonym, related to) originate from bibliography

18 Topic Map Object Person knows Topic described_in Document related to writes Student Researcher Letter similar Doctoral Student PhD Student Tel synonym Affiliation Topics (nodes), relationships, and occurrences of documents ISO-Standard typically for navigation and visualisation

19 Ontology (in our sense) Object is_a -1 is_a -1 Person knows Topic described_in Document is_a -1 is_a -1 writes related_to is_a -1 is_a -1 Student Researcher Letter is_a -1 is_a -1 Doctoral Student PhD Student Affiliation instance_of -1 RULES, e.g.: is_similar_to described_in is_about T D T D Tel Affiliation Hans Muster P writes is_about knows D T P T Representation Languages: ODM, RDF(S); OWL; Predicate Logic; F-Logic, ISO CL, FUB

20 Formality of KR Languages

21 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

22 Ontologies and their relatives Based on AAAI 99 Ontologies Panel McGuiness, Welty, Ushold, Gruninger, Lehmann

23 Ontologies and their relatives (2)

24 Standards/Recommendations/Specifications for Semantic Computing Metadata Registry Structured Metadata ISO/IEC Metadata Registries Terminology Thesaurus Taxonomy Ontology Data Standards Terminology Refers To Referent CONCEPT Stands For Symbolizes Rose, ClipArt Rose Object Management MOF ODM PRR SBVR Graph Node Edge Node Semantic Web RDF(S) / OWL Subject Predicate Object RIF ISO/IEC JTC 1/SC 32 ISO TC 37 OMG W3C 24

25 Ontology Definition Metamodel OMG ODM

26 OMG Ontology Definition Metamodel (ODM) ODM is the OMG standard for model driven ontology development Adopted as an OMG standard in October 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

27 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

28 OMG MOF and OMG MDA Excurse

29 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.

30 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

31 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

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

33 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

34 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

35 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

36 back to OMG ODM

37 Model Driven Ontology Development: ODM Overview Five EMOF platform independent metamodels (PIMs), four normative Mappings (MOF QVT) <<metamodel>> CL <<metamodel>> RDF <<metamodel>> DL UML2 Profiles RDFS & OWL <<metamodel>> TM <<metamodel>> RDFS (from RDF) merge (non-normative) Topic Maps Collateral XMI Java APIs Proof-of-concepts <<metamodel>> RDFBase (from RDF) merge merge <<metamodel>> OWL <<metamodel>> RDFWeb (from RDF) merge Conformance <<metamodel>> OWLBase (from OWL) RDFS & OWL Multiple Options merge <<metamodel>> OWLDL (from OWL) merge <<metamodel>> OWLFull (from OWL) TM, CL Optional Informative Mappings

38 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 UML Profiles DL Core, relatively unconstrained Description Logics based metamodel Identifier (keys) model extension to UML for ER RDFS/OWL Profile Topic Maps Profile Set of Mappings UML to OWL, Topic Maps to OWL RDFS/OWL to Common Logic

39 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

40 The ODM Architecture <<metamodel>> UML2 <<metamodel>> ER <<metamodel>> DL <<metamodel>> RDFS <<metamodel>> OWL UML Profiles for Ontology -- RDFS -- OWL -- TM <<metamodel>> SCL <<metamodel>> TM Ontology Modeling Languages Ontology Logic Languages Ontology Description Languages UML Notations extension mapping NOTE: UML2 metamodel is an existing OMG standard dependency

41 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 Data Model and XML Serialization ISO Query Language ISO Constraint Language

42 ODM TM Metamodel Overview Top Level Constructs TopicMapConstruct +sourcelocator Locator 0..n {set} Characteristic 0..n {set} +characteristic MapItem +content 0..n {set} /containment TopicMap 1 +parent 1 +parent /hasa +topics {set} 0..n Topic Association +associations 0..n {set} 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

43 ODM TM Overview Characteristics 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.

44 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.

45 ODM TM Scoping and Typing Scope_able Type_able +scope 0..n +type 0..1 Topic Characteristic Association Topics are class-like in that they can be used as types A Topics Characteristics and Associations may be limited to a specified scope.

46 Common Logic Metamodel Overview 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 NameForText +namedtext 0..* Text +commentedtext 0..1 Name name : String 0..* +excludedname ModuleBody +body 1 0..* +text PhraseForText +phrase 0..* Phrase CommentedText ExcludedName +exclusionset +moduleforbody 0..* 0..* ExclusionSet ExcludedSet Module Importation Sentence * +exclusionset +module 1 0..* +module +context ModuleName +localdomain 1 NameForImportation Identifier assertedcontent +identifierfortext +commentfortext 0..* Comment comment : String

47 Common Logic Phrases

48 CL Terms & Atoms

49 Sentences

50 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.

51 Quantified Sentences

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

53 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 RDF <<metamodel>> (from org.omg.odm) merge <<metamodel>> RDFS RDFS adds vocabulary related to RDF Schema <<metamodel>> RDFBase merge <<metamodel>> RDFWeb RDFWeb fits the model to the Web via document model

54 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

55 RDFS Package Classes & Utilities RDFS assists us in getting around MOF multiple classification limitations through rdf:type

56 RDFS Package Properties Note that rdf:domain and rdf:range are global properties limiting their usage enhances reusability of ontology components

57 RDFWebPackage Documents

58 Web Ontology Language (OWL) Metamodel Overview <<metamodel>> RDFBase (from RDF) <<metamodel>> RDF (from org.omg.odm) merge <<metamodel>> RDFS (from RDF) merge merge <<metamodel>> RDFWeb (from RDF) OWL metamodel components include: OWLBase: common abstract syntax & constraints OWLDL: OWL DL constraints OWLFull: OWL Full constraints <<metamodel>> OWL (from org.omg.odm) <<metamodel>> OWLBase merge merge merge Semantic MOF or SMOF spec, currently in work at OMG fills in the gaps for MOF multiple classification <<metamodel>> OWLDL <<metamodel>> OWLFull provides additional capabilities for KR applications, SBVR, domainspecific languages

59 Excerpt OWL Metamodel The OWL metamodel is implemented by extending the RDFS metamodel.

60 OWLBase Package OWL Ontology

61 OWLBase Package OWL Classes

62 OWLBase Package Restrictions

63 OWLBase Package OWL Properties

64 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

65 Key Features of the RDF Profile rdfs:resource is modeled as UML::InstanceSpecification Introduction of <<reifies>> stereotype of UML::Dependency to 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

66 RDF Property Subsetting Options

67 Example OWL Number, Value Constraints OWL Cardinality Restricted Mulitplicity in Subtype OWL allvaluesfrom Property Redefinition

68 OWL Property Redefinition (allvaluesfrom) Using Association Classes

69 OWL Intersection, Union, Complement

70 OWL Disjointness Options Simple binary disjoint relationship Disjointness, multiple participants, common parent Disjointness, multiple participants, no common parent

71 OWL Inverse Options Simple inverse relationship Inverse relationship among association classes

72 ODM UML-OWL Bridge

73 UML to OWL Transformation

74 Example: Museum UML Model

75 Example: UML2OWL Transformation

76 MDA-based Ontology Engineering with ODM

77 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

78 Questions? Literature OMG Ontology PSIG OMG ODM Eclipse ATL ODM mplementation/