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The Model-Driven Semantic Web

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Presentation on theme: "The Model-Driven Semantic Web"— Presentation transcript:

1 The Model-Driven Semantic Web
Emerging Technologies & Implementation Strategies Elisa Kendall Sandpiper Software September 8, 2005

2 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

3 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 transformation source language target language

4 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”

5 Level Setting relevant to a particular domain or area of interest.
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.

6 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.

7 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)

8 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

9 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

10 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

11 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

12 Metadata Management Scenarios

13 Classifying Ontologies
Level of Complexity Level of Expressivity 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

14 Model Dynamics Model centric perspectives characterize the ontologies themselves and are concerned with their structure, formalism and dynamics. Perspective Level of Authoritativeness 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

15 Application Characteristics
Application centric perspectives are concerned with how applications use and manipulate ontologies. Perspective Control/Degree of Manageability Application Changeability 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

16 Knowledge Representation vs. UML
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)

17 Design Rationale 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

18 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 All else optional Ontology Definition Metamodel Completed/Included Mapping Planned Mapping (nonnormative)

19 Impedance Mismatches & Issues
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

20 ODM Profiles 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

21 ODM Mappings 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

22 Bridging KR and MDA

23 Technology Architecture

24 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 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 – see Plans for recommendation / vote for adoption December meeting

25 Implementation Strategies
Native OWL Tool UML Tool with ODM Plug-In RDFS/OWL XMI Representation Enables Use in Eclipse/EMF, MOF-Based Tools Use Reasoning to Find Inconsistencies Validate Logic, Policies Use UML to Refine Linkage Ontologies Ontology Analysis & Validation Java Theorem Prover (JTP) Hybrid Reasoning System Link ODM Models (conceptual / semantic) To logical (ERWIN, Rose) and physical models (ERWIN, Rose, Power Designer) MOF/ODM – OWL Bridge MOF Repository Tools Use Links / Mappings to Find & Eliminate Redundancies thru Reasoning Use Links / Mappings For Query Planning, Business Intelligence

26 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

27 OMG Standards & Zachman Framework
Production Rules RFP Semantics for Business Vocabularies & Rules Ontology Definition Metamodel

28 Related ISO Metadata Standards
ISO/IEC – Metadata Registries Part 2: Classification for Administered Items Part 3: Registry Metamodel & Basic Attributes ISO/IEC – Framework for Metamodel Interoperability Part 3: Metamodel Framework for Ontology Registration (depends on ODM Metamodels, ISO classification) Part 4: Metamodel Framework for Mapping (depends on MOF QVT specification) XMDR Program – Extended Metadata Registration OWL ontologies for ISO 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

29 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

30 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

31 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

32 Questions / Discussion

33 John Sowa’s Top-Level Categories

34 Physical Quantities & Dimensions

35 Constant Quantities & Units

36 Standard International Units

37 Example Measurement Class Definition

38 Example Corrosion Rate Definition

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