Ontologies & the Semantic Web for Semantic Interoperability Representation Semantic Mapping Semantic Interoperability Dr. Leo Obrst MITRE Center for Innovative Computing & Informatics Information Semantics Lobrst@mitre.org April 21, 2017 Copyright 2004 by The MITRE Corporation

Overview The Problem Tightness of Coupling & Explicit Semantics Semantic Integration Implies Semantic Composition Dimensions of Interoperability & Integration Ontologies & the Semantic Web The Ontology Spectrum What are Ontologies? Levels of Ontology Representation What Problems do Ontologies Help Solve? Semantic Web Ontologies for Semantically Interoperable Systems Enabling Semantic Interoperability Examples Visions What do We Want the Future to be?

Semi-mountainous terrain The problem With the increasing complexity of our systems and our IT needs, and the distance between systems, we need to go toward human level interaction We need to maximize the amount of semantics we can utilize and make it increasingly explicit From data and information level, we need to go toward human semantic level interaction DATA Information Knowledge Run84 ID=08 NULL PARRT ACC ID=34 e 5 & # ~ Q ü @ ¥ Æ � Å Tank Noise Human Meaning Vehicle Located at Semi-mountainous terrain obscured decide Vise maneuver Semantic representation & semantic interoperability/integration become very important

Tightness of Coupling & Semantic Explicitness Explicit, Loose Far Performance = k / Integration_Flexibility Modal Policies Internet Semantic Mappings Semantic Brokers OWL-S Agent Programming RDF/S, OWL Peer-to-peer Semantics Explicitness Web Services: UDDI, WSDL Web Services: SOAP XML, XML Schema Data Applets Community Application N-Tier Architecture EAI Workflow Ontologies Same Intranet Conceptual Models Middleware Web Enterprise Data Marts Same Wide Area Network Client-Server Data Warehouses Same Local Area Network Federated DBs Distributed Systems OOP Systems of Systems Same DBMS Same OS Same Address Space Same CPU Linking From Synchronous Interaction to Asynchronous Communication Same Programming Language Same Process Space Compiling 1 System: Small Set of Developers Local Implicit, TIGHT Looseness of Coupling

Semantic Integration Implies Semantic Composition Complex Semantic Model, Knowledge, System Integration & Composition Unification of complex networks of graph Structures, with complex reasoning, complex Semantic Web ontologies: Complexity  Simple Semantic Model, Knowledge Integration & Composition Unification of tree or graph structures, with reasoning, simple Semantic Web ontologies:  2010 Simple Syntactic Object Integration & Composition Alignment of embedded interface definition language statements mapping two CORBA, Javabean objects 2005 1998 Time Simple Procedure Integration & Composition Concatenation, alignment of calling Procedure with called procedure: Caller: Do_this (integer: 5, string: “sales”) Called: Do_this (integer: X, string: Y) 1960  - signifies the composition operation

Dimensions of Interoperability & Integration Our interest lies here Community Enterprise 6 Levels of Interoperability System Semantic Application Component Syntactic Structural Object Data 3 Kinds of Integration 0% 100% Interoperability Scale

Semantic Interoperability/Integration Definition To interoperate is to participate in a common purpose Operation sets the context Purpose is the intention, the end to which activity is directed Semantics is fundamentally interpretation Within a particular context From a particular point of view Semantic Interoperability/Integration is fundamentally driven by communication of purpose Participants determined by interpreting capacity to meet operational objectives Service obligations and responsibilities explicitly contracted Today interoperability is achieved indirectly; semantic considerations are only in the heads of designers and implementers. OASIS will focus on making semantics an explicit part of the computational process. OASIS adds the dimensions of operational context and purpose to the analysis, design and implementation of semantically enriched objects. Configuration of semantic components is potentially negotiated between the service requester and the potential service provider.

Ontology Spectrum: One View strong semantics Is Disjoint Subclass of with transitivity property Modal Logic Logical Theory Thesaurus Has Narrower Meaning Than Taxonomy Is Sub-Classification of Conceptual Model Is Subclass of DB Schemas, XML Schema UML First Order Logic Relational Model, XML ER Extended ER Description Logic DAML+OIL, OWL RDF/S XTM Semantic Interoperability Structural Interoperability Syntactic Interoperability weak semantics 1

Ontology Spectrum: One View strong semantics Modal Logic First Order Logic Problem: Very General Semantic Expressivity: Very High Problem: Local Semantic Expressivity: Low Problem: General Semantic Expressivity: Medium Semantic Expressivity: High Logical Theory Is Disjoint Subclass of with transitivity property Description Logic DAML+OIL, OWL UML Conceptual Model Is Subclass of Syntactic Interoperability Structural Interoperability Semantic Interoperability RDF/S XTM Extended ER Thesaurus Has Narrower Meaning Than ER DB Schemas, XML Schema Taxonomy Is Sub-Classification of Relational Model, XML weak semantics 1

A Business Example of Ontology Washer Catalog No. Shape Size Price iMetal Corp. E-Machina Manufacturer .45 1.25 Square 550298 .35 1.5 Round 550296 .75 XAB023 .25 XAB035 … Price ($US) Size (in) Shape Mfr No. .25 1.25 Square XAB035 .75 1.5 Round XAB023 … Price ($US) Size (in) Shape Catalog No. .45 31 S 550298 .35 37 R 550296 … Price ($US) Diam (mm) Geom. Part No. Supplier A Buyer Supplier B

What Problems Do Ontologies Help Solve? Heterogeneous database problem Different organizational units, Service Needers/Providers have radically different databases Different syntactically: what’s the format? Different structurally: how are they structured? Different semantically: what do they mean? They all speak different languages (access, description, schemas, meaning) Integration: rather than N2 problem, with single, adequate Ontology reduces to N Enterprise-wide system interoperability problem Currently: system-of-systems, vertical stovepipes Ontologies act as conceptual model representing enterprise consensus semantics Relevant document retrieval/question-answering problem What is the meaning of your query? What is the meaning of documents that would satisfy your query? Can you obtain only meaningful, relevant documents?

Emerging XML Stack Architecture for the Semantic Web + Grid + Agents Semantic Brokers Intelligent Agents Advanced Applications Use, Intent: Pragmatics Trust: Proof + Security + Identity Reasoning/Proof Methods OWL: Ontologies RDF Schema: Ontologies RDF: Instances (assertions) XML Schema: Encodings of Data Elements & Descriptions, Data Types, Local Models XML: Base Documents Grid & Semantic Grid: New System Services, Intelligent QoS Syntax: Data Structure Semantics Higher Semantics Reasoning/Proof XML XML Schema RDF/RDF Schema OWL Inference Engine Trust Security/Identity Use, Intent Pragmatic Web Intelligent Domain Services, Applications Agents, Brokers, Policies RULES Grid & Semantic Grid Services

Semantic Web Services Stack Semantics  Pragmatics RULES Adapted from: Bussler, Christoph; Dieter Fensel; Alexander Maedche. 2003. A Conceptual Architecture for Semantic Web Enabled Web Services. SIGMOD Record, Dec 2002. http://www.acm.org/sigmod/record/issues/0212/SPECIAL/4.Bussler1.pdf.

Enabling Semantic Interoperability Semantic Interoperability is enabled through: Establishing base semantic representation via ontologies (class level) and their knowledge bases (instance level) Defining semantic mappings & transformations among ontologies (and treating these mappings as individual theories just like ontologies) Defining algorithms that can determine semantic similarity and employing their output in a semantic mapping facility that uses ontologies The use of ontologies & semantic mapping software can reduce the loss of semantics (meaning) in information exchange among heterogeneous applications, such as: Web Services E-Commerce, E-Business Enterprise architectures, infrastructures, and applications Complex C4ISR systems-of-systems Integrated Intelligence analysis

Semantic Interoperability, Integration: Multiple Semantics Multiple contexts, views, application & user perspectives Multiple levels of precision, specification, definiteness required Multiple levels of semantic model verIisimilitude, fidelity, granularity Multiple kinds of semantic mappings, transformations needed: Entities, Relations, Properties, Ontologies, Model Modules, Namespaces, Meta-Levels, Facets (i.e., properties of properties), Units of Measure, Conversions, etc. Upper Ontologies will become more important To be able to interrelate domain ontologies

Simple Example: Semantics of Date Across Applications System1 Instance of Concept: Date1 Attribute: YR = Int 1 Attribute: MO = String “Aug” Attribute: DY = Int 12 System2: Instance of Concept = Date2 Attribute: DayOfWeek = Sunday Attribute: ActualDate = String “12082001” Semantically Equivalent? Then How? Once Assertions, Transformations Defined: become part of Integration Ontology & Reused Date2.ActualDate  Date1.DY  Date1.MO  Date1.YR Add Assertions, Apply Transformations (directional) DATE 1 DATE 2 Exactly Semantically Equivalent to? YR DayOfWeek ActualDate DY No: Approximately Semantically Equivalent to. So Mappings and Transformations are Needed! MO

Simple Example: Semantics of Location Across Applications System1 Instance of Concept: Location1 Attribute: SourceDeadReckoning = A Attribute: SourceDRLatitude = B Attribute: SourceDRLongitude = C Attribute: TargetDRBearingLine = D Attribute: TargetDRAltitude = E Attribute: ActualMeasuredAltitude = F Attribute: PositionLine = G System2: Instance of Concept: Location2 Attribute: Address = H Attribute: City = I Attribute: StateProvince = J Attribute: Country = K Attribute: MailCode = L Approximately Semantically Equivalent to?

Electronic Commerce Example: One Company Sell Products TradingPartners Support Health TransWorld iMicro Chemical EndRun Electronic Metal Uses Location 3Initial Applications Africa Europe Asia ShippedBy TradingHub RFI/RFQ Portugal GivenBy Spain Shipping Methods Air ObtainedFrom LocatedAt Coordinate System Ground Sea Distributor Geographic UTM Truck Manufacturer Retailer Train GPS Wholesaler LatLong LocalCarrier RegionalCarrier AvailableAt MeasuredBy Time UnitOfMeasure Point Interval Mass Distance Liquid Solid

Now Assume Each Company Has Separate Enterprise Semantics, Multiply by the Number of Companies, & Have Them Interoperate and Preserve Semantics Products Metal Health Electronic Chemical Distributor Manufacturer Wholesaler Retailer EndRun TradingPartners TransWorld iMicro 3Initial Location Africa Europe Spain Portugal Asia Time Point Interval Coordinate System UTM Geographic LatLong GPS UnitOfMeasure Distance Mass Liquid Solid Shipping Methods Air Ground Truck RegionalCarrier LocalCarrier Sea Applications TradingHub RFI/RFQ Sell ShippedBy ObtainedFrom LocatedAt GivenBy MeasuredBy Uses Support AvailableAt Train Products Metal Health Electronic Chemical Distributor Manufacturer Wholesaler Retailer EndRun TradingPartners TransWorld iMicro 3Initial Location Africa Europe Spain Portugal Asia Time Point Interval Coordinate System UTM Geographic LatLong GPS UnitOfMeasure Distance Mass Liquid Solid Shipping Methods Air Ground Truck RegionalCarrier LocalCarrier Sea Applications TradingHub RFI/RFQ Sell ShippedBy ObtainedFrom LocatedAt GivenBy MeasuredBy Uses Support AvailableAt Train Products Metal Health Electronic Chemical Distributor Manufacturer Wholesaler Retailer EndRun TradingPartners TransWorld iMicro 3Initial Location Africa Europe Spain Portugal Asia Time Point Interval Coordinate System UTM Geographic LatLong GPS UnitOfMeasure Distance Mass Liquid Solid Shipping Methods Air Ground Truck RegionalCarrier LocalCarrier Sea Applications TradingHub RFI/RFQ Sell ShippedBy ObtainedFrom LocatedAt GivenBy MeasuredBy Uses Support AvailableAt Train Products Metal Health Electronic Chemical Distributor Manufacturer Wholesaler Retailer EndRun TradingPartners TransWorld iMicro 3Initial Location Africa Europe Spain Portugal Asia Time Point Interval Coordinate System UTM Geographic LatLong GPS UnitOfMeasure Distance Mass Liquid Solid Shipping Methods Air Ground Truck RegionalCarrier LocalCarrier Sea Applications TradingHub RFI/RFQ Sell ShippedBy ObtainedFrom LocatedAt GivenBy MeasuredBy Uses Support AvailableAt Train Try doing this without Ontologies! You can, but it’s a Nightmare, and it COSTS: Now & Later!

Semantic Issues: Complexity An ontology allows for near linear semantic integration (actually 2n - 1) rather than near n2 (actually n2 - n) integration Each application/database maps to the "lingua franca" of the ontology, rather than to each other Ordinary Integration Ontology Integration A B A C B 2 Nodes 3 Nodes 4 Nodes 5 Nodes 2 Edges 6 Edges 12 Edges 20 Edges 2 Nodes 3 Nodes 4 Nodes 5 Nodes 2 Edges 4 Edges 6 Edges 8 Edges A C B C B C A D Add D: A D A B C D Add D: A D B D   C D

Measures of Semantic Similarity Synonyms: Approximation, Similarity Syntactic Methods Formal & Logical Methods: Inference Graph Theory Information Theory, Probability Semantic Methods Formal Concept Analysis Possible Worlds Semantics: Close to Far Accessible Worlds Accessibility relation usually taken to be entailment Hybrid Syntactic + Semantic Methods Category Theory Anchored Concepts + Graph Theory Approximation & plausibility methods Computational linguistics: corpus statistical measures + NL semantics Symbolic to numeric/stochastic/continuous semantic conversions

Graph Homomorphism/Analogy? With Anchoring? In general, solutions based on graph homomorphisms won’t work: structural correspondence does not ensure semantic correspondence hammer handgun NOT Semantically Equivalent sledge revolver pistol claw fiberglass claw 6 lb. 12 lb. single-action automatic semi-automatic But, with some semantic “anchoring”, structure may help with semantics hammer hand tool Approximately Semantically Equivalent sledge pliers claw ANCHOR steel hammer fiberglass claw 6 lb. 12 lb. linesman carpenter drilling

Ontology Mapping: Namespaces, Contexts, Lattice of Theories Top of Lattice of Theories Ontology1 Namespace1 Ontology2 Namespace1 Context1 Context2 Namespace2 Ontology3 Namespace1

Ontology Mappings & Context Contexts as Domain Theories/Ontologies Elaborate based on need Compose on fly Mapped Ontologies Context 1 or Subdomain Theory 1 “training” Initial Ontology Context 2 or Subdomain Theory 2 “orbital” Context 3 or Subdomain Theory 3 “ground” Context 4 or Subdomain Theory 4 “Air Force” Context 5 or Subdomain Theory 5 “Navy” altitude tank Whatever it specifically “means”, I.e., relationships, subclasses, constraints Whatever it specifically “means”, I.e., relationships, subclasses, constraints Whatever it specifically “means”, I.e., relationships, subclasses, constraints Whatever it specifically “means”, I.e., relationships, subclasses, constraints Whatever it usually or Canonically “means”, I.e., relationships, subclasses, constraints Whatever it specifically “means”, I.e., relationships, subclasses, constraints What “altitude” means In the context of ground What “altitude” means In the context of training What “altitude” means In the context of orbital What “altitude” means In the context of ground And Navy What “altitude” means In the context of orbital And Air Force

Vision: Semantic Broker Web-Based Machine-Interpretable Semantics (stacked languages) Schema Application Data Mappings Ontologies Documents Semantic Broker Semantic Mapper Contexts Requests Services Use/Intent Proof OWL Agent Services Web Services RDF/S XTM XLT Specific XML Languages Schema

Vision: Semantically Interoperable Systems Users: Purchasers, Sellers, Decision-Makers Consumers, Analysts, Manufacturers Ontology and Reasoning Services Queries Semantic Broker Application Meta-data Ontologies Meta-Knowledge Agency Meta-data Active Application Agent Active Application Agent Active Application Agent Upper Ontology: Generic Base Processes Workflow Products & Svcs Organizations Mapping Knowledge Fielded Systems Application Application Application Interaction Knowledge Semantic Mappings Documents Databases

What do we want the future to be? 2100 A.D: models, models, models There are no human-programmed programming languages There are only Models Transformations, Compilations INFRASTRUCTURE Ontological Models Knowledge Models Belief Models Application Models Presentation Models Target Platform Models Executable Code

Thank You! Questions? lobrst@mitre.org Shameless Plug: The Semantic Web: The Future of XML, Web Services, and Knowledge Management, -- Mike Daconta, Leo Obrst, & Kevin Smith, Wiley, June, 2003 http://www.amazon.com/exec/obidos/ASIN/0471432571/qid%3D1050264600/sr%3D11-1/ref%3Dsr%5F11%5F1/103-0725498-4215019 Contents: What is the Semantic Web? The Business Case for the Semantic Web Understanding XML and its Impact on the Enterprise Understanding Web Services Understanding the Resource Description Framework Understanding the Rest of the Alphabet Soup Understanding Taxonomies Understanding Ontologies Crafting Your Company’s Roadmap to the Semantic Web