Introduction to Ontologies A presentation to the UBL members

1. Dr. Leo Obrst MITRE lobrst@mitre.org December 20, 2019 Introduction to OntologiesA presentation to the UBL members

2. Outline • Introduction • Problem, Solution • Interpretation Continuum • Ontology & Ontologies • Ontology Spectrum • Examples • Triangle of Signification • Use of Ontologies • Enablers, Related Standards, Where’s the Technology Going? • Ontological Engineering & Modeling Issues • Semantic Web • XML Stack/Layer Cake • RDF/S, DAML+OIL, OWL • Ontologies & UBL • Machine Interpretable Semantics?

3. ¥ Å @ ü Q # ¥ e & 5 ~ Æ � The Problem • With the increasing complexity of our systems and our IT needs, we need to increase human level interaction • We need to maximize the amount of knowledge we can utilize • From data and information level, we need to go to human knowledge level interaction Information Data Knowledge decide ID=34 Vehicle Located at Vise maneuver ACC ID=08 Tank NULL obscured PARRT Semi-mountainous terrain ¥ Run84 Noise Human Meaning

4. ¥ Å @ ü # Q ¥ e & 5 ~ Æ � The Solution • We need to offload the very real, heavy cognitive interpretation burdenfrom humans to our systems • Smart Interactive Agents Who Understand Human Semantics: for Advice, Training, DS, SE

5. But This Is Impossible! • Need ubiquitous multi-modal interaction • Need collaborative distributed intelligent agent teams across the entire Internet to help solve old and new problems • Need distributed deep semantic knowledge representation with dynamic composition/mapping and real-time inference • So what do we do? • What follows…

6. Advancing Along the Interpretation Continuum ... Richer Metadata: RDF/S Simple Metadata: XML Very Rich Metadata: DAML+OIL Computer interpreted Human interpreted Interpretation Continuum KNOWLEDGE DATA • Very structured • Logical • Relatively unstructured • Random • Info retrieval • Web search • Text summarization • Content extraction • Topic maps • Reasoning services • Ontology Induction Automatically acquire concepts; evolve ontologies into domain theories; link to institution repositories (e.g., MII) Automatically span domain theories and institution repositories; inter-operate with fully interpreting computer Store and connect patterns via conceptual model (i.e,. an ontology); link to docs to aid retrieval Find and correlate patterns in raw docs; display matches only Display raw documents; All interpretation done by humans Moving to the right depends on increasing automated semantic interpretation

7. Ontology & Ontologies 1 • An ontology defines the terms used to describe and represent an area of knowledge (subject matter) • An ontology also is the model (set of concepts) for the meaning of those terms • An ontology thus defines the vocabulary and the meaning of that vocabulary • Ontologies are used by people, databases, and applications that need to share domain information • Domain: a specific subject area or area of knowledge, like medicine, tool manufacturing, real estate, automobile repair, financial management, etc. • Ontologies include computer-usable definitions of basic concepts in the domain and the relationships among them • They encode domain knowledge (modular) • Knowledge that spans domains (composable) • Make knowledge available (reusable)

8. Ontology & Ontologies 2 • The term ontology has been used to describe models with different degrees of structure (see Ontology Spectrum) • Less structure: Taxonomies (Semio taxonomies, Yahoo hierarchy, biological taxonomy), Database Schemas (many) and metadata schemes • More Structure: Thesauri (WordNet), Conceptual Models (OO models, UML) • Most Structure: Logical Theories (Ontolingua, TOVE, CYC, Semantic Web) • Ontologies are usually expressed in a logic-based language • Enabling detailed, accurate, consistent, sound, meaningful distinctions to be made among the classes, properties, and relations • More expressive meaning but maintain “computability” • Using ontologies, tomorrow's applications can be "intelligent” • More accurately work at the human conceptual level • Ontologies are usually developed using special modeling tools (based on knowledge representation) that can model rich semantics

9. Ontology & Ontologies 3 • Ontologies are typically developed by a team with individuals of two types • Domain Experts: have the knowledge of a specfic domain • Modelers (ontologists): know how to formally model domains, spanning domains, semantic properties, relations • On-going research investigates semi-automation of ontology development • State-of-art for next 20 years will be semi-automation • Humans have rich semantic models & understanding, machines poor so far • Want our machines to interact more closely at human concept level • The more & richer the knowledge sources developed & used, the easier it gets (bootstrapping, learning) • Rigorous ontology development methodologies evolving (e.g., Methontology), today’s practice is set of principles/processes • Tools are being developed that apply formal ontology analysis techniques to assist KR-naïve domain experts in building ontologies (OntoClean)

10. Communities: AI/KR/NL, DB, Library/Info. Science, OO Is Disjoint Subclass of with transitivity property Problem: Local Semantic Expressivity: High Problem: Local Semantic Expressivity: Low Problem: Very General Semantic Expressivity: Very High Problem: General Semantic Expressivity: Medium Logical Domain Theory ML FOL Ontology Mathematically Rigorous, Rich & Consistent Formal Syntax, Structure, Semantics: Terms + Machine-Interpretable Meaning Spectrum Machine Interpretable Vocabulary and Types: Entities, Relations, Properties, Values, Constraints, Rules, Axioms AI/KR/NL DL strong semantics Syntax/Format, Structure, Semantics: Terms + Meaning Program Technologies Company has_expertise_in works Conceptual Model Is Subclass of Animal Personnel OO UML Project Knowledge Representation EER Division Staff Li/IS Mammal Management Object Model Reptile Agent Natural Language Bird Syntax/Format & Well-defined NL Definition: Terms Task Department A6 A5 A4 Cat Dog Snake Technical Program Thesaurus Telecommunications Leo Has Narrower Meaning Than Paul Inderjeet Logical Concepts W150 DB Semantic Interoperability Cocker Spaniel ER Controlled Vocabulary Director Entities: Metal working machinery, equipment and supplies, metal-cutting machinery, metal-turning equipment, metal-milling equipment, milling insert, turning insert, etc. Relations: subclass-of; instance-of; part-of; has-geometry; performs, used-on;etc. Properties: geometry; material; length; operation; UN/SPSC-code; ISO-code; etc. Values: 1; 2; 3; “2.5 inches”; “85-degree-diamond”; “231716”; “boring”; “drilling”; etc. Axioms/Rules:If milling-insert(X) & operation(Y) & material(Z)=HG_Steel & performs(X, Y, Z), then has-geometry(X, 85-degree-diamond). Etc. DARPA has Terms: Metal working machinery, equipment and supplies, metal-cutting machinery, metal-turning equipment, metal-milling equipment, milling insert, turning insert, etc. Relations: use, used-for, broader-term, narrower-term, related-term Navy requires Schema Assistant Director weak semantics Intelligence Reza R Lady Ann Brad Minimal Hierarchic Structure Howard Taxonomy/ Classification System knows Is Sub-Classification of Ontology Spectrum

11. Language L Models M(L) Ontology Intended models IM(L) More Formally: a common picture Conceptualization C * Guarino, 98, p. 7

12. Conceptualization B: Buyer Conceptualization S: Seller Conceptualization B1: Technical Buyer Conceptualization S1: Manufacturer Seller Conceptualization B2: Non-Technical Buyer Conceptualization S1: Distributor Seller Language LB1 Language LS1 Language LB2 Language LS2 Models MB1(LB1) Models MB2(LB2) Models MS2(LS2) Models MS1(LS1) Ontology Intended models IMB1(LB1) Intended models IMB1(LB1) Intended models IMB2(LB2) Intended models IMB1(LB1) A More Complex Actual Common Picture (from E-Commerce)

13. Many Types of Ontology! Marital Status Human Gender Male Female Married Occupation Joe DiMaggio Baseball Player Single Actor George Ives Director Marilyn Monroe Kevin Bacon

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

15. Big O: Ontology, Little O: ontology • Philosophy:“a particular system of categories accounting for a certain vision of the world” or domain of discourse, a conceptualization (Big O) • Artificial Intelligence: “an engineering product consisting of a specific vocabulary used to describe a part of reality, plus a set of explicit assumptions regarding the intended meaning of the vocabulary words”, “a specification of a conceptualization” (Little O) • Ontological Engineering: towards a formal, logical theory, usually ‘concepts’ (i.e., the entities, usually classes hierarchically structured in a special subsumption relation), ‘relations’, ‘properties’, ‘values’, ‘constraints’, ‘rules’, ‘instances’ * These definitions are derived from Guarino, 98; Guarino & Giaretta, 95

16. Triangle of Signification Intension <Joe_ Montana > Concepts Semantics: Meaning Reference/ Denotation Sense Real (& Possible) World Referents Terms “Joe” + “Montana” Syntax: Symbols Pragmatics: Use Extension

17. Ontology: General Picture But Also This! Most General Thing Upper Ontology (Generic Common Knowledge) Processes Locations Organizations Products/Services Middle Ontology (Domain-spanning Knowledge) Machinery & Tooling Lower Ontology (individual domains) Electronic Components Lowest Ontology (sub-domains) Cutting Blades E-commerce Area of Interest Mostly This Instances: in databases, documents, applications

18. Definitions: Conclusions • Ontology: a specification of a conceptualization, vocabulary + model, theory • Informally, ontology and model are taken to be synonymous, i.e.,a description of the structure and meaning of a domain, a conceptual model • Bottom Line: an Ontologyis the entities (usually structured in a class hierarchy with multiple inheritance), relations, properties (attributes), values, instances, constraints, and rules used to model one or more domains • A Taxonomy: entities structured in a hierarchy with single inheritance, sometimes with properties & values

19. Knowledge Management Ontological Engineering and Related Disciplines Enterprise Engineering Logic Philosophy Mathematics Industrial Engineering Formal Methods Linguistics Business Management Computer Science Ontology Formal Semantics Database Theory Sociology Artificial Intelligence Formal Ontology Informal Ontology Knowledge Representation Conceptual Modeling Ontological Engineering Knowledge Engineering Software/Data Engineering Object Modeling

20. 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 • Enterprise-wide system interoperability problem • Currently: system-of-systems, vertical stovepipes • Ontologies act as conceptual model representing enterprise consensus semantics • Well-defined, sound, consistent, extensible, reusable, modular models • 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?

21. Ontologies & the Data Integration Problem • DBs provide generality of storage and efficient access • Formal data model of databases insufficiently semantically expressive • The process of developing a database discards meaning • Conceptual model  Logical Model  Physical Model • Keys signify some relation, but no solid semantics • DB Semantics = Schema + Business Rules + Application Code • Ontologies can represent the rich common semantics that spans DBs • Link the different structures • Establish semantic properties of data • Provide mappings across data based on meaning • Also capture the rest of the meaning of data: • Enterprise rules • Application code (the inextricable semantics)

22. Disciplines Ontological engineering Computational semantics for natural language processing Knowledge acquisition Semantic context/view formalization Knowledge discovery Description logics & theorem-proving Knowledge representation & reasoning Formal ontology Practices & Technologies Logic & constraint programming Deductive databases (logic+database set at a time operators) Ontology document annotation Ontology management tools Ontology content development Semantic Web (RDF/S, DAML+OIL, OWL) & Web services (DAML-S, etc.) Ontology reasoning tools Technical Enablers

23. Related Standards Activities • W3C ongoing efforts • Resource Description Framework/Schema (RDF/S) • DARPA Agent Markup Language+Ontology Inference Layer (DAML+OIL) • Ontology Web Language (OWL) • Web Services: DAML-Services (DAML-S), Web Service Description Language (WSDL), etc. • IEEE Standard Upper Ontology candidates • Suggested Upper Merged Ontology (SUMO) • Information Flow Framework (IFF) • OpenCyc .6 • NIST, ISO/ANSI: KIF (Knowledge Interchange Format), now called Common Logic, axiomatic standards development • OASIS • Unified Business Language (UBL) • Unified Modeling Language (UML), Object Constraint Language (OCL) • Meta Object Framework (MOF) • XML Topic Maps (XTM)

24. Where is the Technology Going? • “The Semantic Web is very exciting, and now just starting off in the same grassroots mode as the Web did 10 years ago ... In 10 years it will in turn have revolutionized the way we do business, collaborate and learn.” - Tim Berners-Lee, CNET.com interview, 2001-12-12 • Semantic Web applications with trans-community semantics • Device interoperability in the ubiquitous computing future: achieved through semantics & contextual awareness • True realization of intelligent agent interoperability • Intelligent semantic information retrieval & search engines • Next generation electronic commerce/business & web services • Semantics beginning to be used once again in NLP: information extraction becomes knowledge extraction • Key to all of this is effective & efficient use of ontologies

25. (implies (isa ?BATTALION InfantryBattalion) (thereExistExactly 1 ?COMPANY (and (isa ?COMPANIES Company-UnitDesignation) (isa ?COMPANIES WeaponsUnit-MilitarySpecialty) (subOrgs-Direct ?BATTALION ?COMPANY) (subOrgs-Command ?BATTALION ?COMPANY)))) CYC MELD Expression Example Aside: Ontology/KRExpressible as Language and Graph • In ontology and knowledge bases, nodes are predicate, rule, variable, constant symbols, hence graph-based indexing, viewing • Links are connections between these symbols: Semantic Net! isa ?BATTALION implies InfantryBattalion thereExistExactly 1 and ?COMPANY isa ?COMPANIES Company-UnitDesignation isa WeaponsUnit-MilitarySpecialty) subOrgs-Direct What’s important is the logic!

26. Ontology/ies in Use: “Lattice of Theories”, Namespaces, Contexts Top of Lattice of Theories Ontology1 Namespace1 Ontology2 Namespace1 Context1 Context2 Occupation Person Skilled_Labor Namespace2 Carpenter Works_At Works_On_Craft Person Attribute Works_On_Wood Occupation Value Carpenter Ontology3 Namespace1

27. Namespace, Context, Lattice of Theories • Namespace: a space ensuring name uniqueness • An ontology can be a single namespace • If an ontology consists of multiple namespaces, usually hierarchic • Some ontology tools distinguish between “types”, hence name uniqueness defined wrt “type” • Example: relation.father vs. entity.father • More typically, however, no real distinction between class (entity) and relation, both are first-class citizens; a class is a unary relation, a relation is a class (can be “reified” as a class) • Context: comparable to a “view” in databases • Can be considered just another “theory” (ontology), but uses the base ontologies, “inheriting” some of their knowledge (appropriate parts), and “extending” or adding knowledge • Lattice of Theories • Less interest in a “monolithic” ontology • More interest in logically integrating ontologies: subsumption lattice, logically situated & linked “microtheories”

28. Ontology Modeling Issues • What do you model in? KR Language • OO Frame vs. DL Axiom? • What do you model? Content Issues • Concepts: • Entities & relations • Universals & Particulars • Classes & Instances/Individuals • How are Concepts modeled? • Meta-class, Class, Instance • Distinguished relations: subclass/isa, instance_of, part_of (part-whole) • Class as unary relation, No distinction between element & attribute • Attribute as relation, reification of relations (as first class citizens, etc.) • Domain & range of relation • Slots & roles: relations “attached” to an instance • Slots: in frame systems • Roles: in description logics

29. Ontology Modeling Issues • Other: times, events, processes, purposes, contexts, agents, functions • Formal Ontological Analysis Issues* • Meta-properties • Mereology: proper part of: asymmetric, transitive, etc., problems with extensionality: different properties/same parts, different parts/same properties • Identity, unity, essence: how can something change but yet keep its identity (frame problem), essential properties? • Essence and rigidity: if always, then rigid • Identity and identity criteria: synchronic, diacronic, e.g., physical location constancy? some notion of persistence. • Unity and unity criteria: necessary vs. sufficient, boundaries, connectedness, plurality (collections, whole is a sum) • Dependence: one object depends on another, dependent property • Meta-properties of the privileged relations: combinations of the above, subsumption (subclass) relation, stratification, multiple inheritance, etc. • Kinds of Properties • Basis of OntoClean methodology *Guarino, Nicola, and Christopher Welty. 2001. Conceptual Modeling and Ontological Analysis. http://reliant.teknowledge.com/IJCAI01/Guarino.ppt.

30. Category +R Non-sortal -I Attribution -R-D Role ~R+D Formal Role Material role Anti-rigid ~R Non-rigid -R Phased sortal -D Mixin -D Sortal+I Type +O Rigid +R Quasi-type -O Ontology Modeling Issues: A Formal Ontology of Properties* Property *Guarino, Nicola, and Christopher Welty. 2001. Conceptual Modeling and Ontological Analysis. http://reliant.teknowledge.com/IJCAI01/Guarino.ppt.

31. Ontology Modeling Issues: Ontological Levels* • Physical • Atomic (a minimal grain of matter) • Static (a configuration, a situation) • Mereological (an amount of matter, a collection) • Topological (a piece of matter) • Morphological (a cubic block, a constellation) • Functional (an artifact, a biological organ) • Biological (a human body) • Intentional (a person, a robot) • Social (a company) • Correspond to different kinds of IC/UC • All levels except the mereological one have non-extensional IC • A genericdependence relation links higher levels to their immediate inferior. *Guarino, Nicola, and Christopher Welty. 2001. Conceptual Modeling and Ontological Analysis. http://reliant.teknowledge.com/IJCAI01/Guarino.ppt.

32. Ontology Modeling Issues: Well-Founded Ontologies - Some Basic Design Principles* • Be clear about the domain • particulars (individuals) • universals (classes and relations) • linguistic entities (nouns, verbs, adjectives...) • Take identity seriously • different identity criteria imply disjoint classes • Isolate a basic taxonomic structure • only sortals like “person” (as opposite to “red”) are good candidates for being taxons • Sortals: objects which carry identity • Categories: objects which generalize sortals • Make an explicit distinction between types and roles (and other property kinds) *Guarino, Nicola, and Christopher Welty. 2001. Conceptual Modeling and Ontological Analysis. http://reliant.teknowledge.com/IJCAI01/Guarino.ppt.

33. Person Attribute Occupation Value Carpenter Ontology Modeling Issues: Reifying Relations? Ontology Relations Entities Range Domain Occupation Person Skilled_Labor Carpenter Works_At Works_On_Craft VS. Local Attributes: Works_On_Wood

34. KEY attributes: SKU Name Price Ontology node UN/SPSC node O:U Mappings U Application Aliases & Mappings Architecture, Ontology Layer: Ontology Mapped to UN/SPSC, Aliases Ontology UN/SPSC MORE attributes: Weight Height Density …

35. Ontology Related Tools: from OntoWeb*: Overview of ontology tools (I)Environments for building ontologies APECKSURL: Not available ApolloURL: http://apollo.open.ac.uk CODE4URL: http://www.csi.uottawa.ca/~doug/CODE4.html CO4URL: http://co4.inrialpes.fr/ DUET (DAML UML Enhanced Tool)URL: http://grcinet.grci.com/maria/www/CodipSite/Tools/Tools.html GKB-EditorURL: http://www.ai.sri.com/~gkb/ IKARUSURL: http://www.csi.uottawa.ca/~kavanagh/Ikarus/IkarusInfo.html JOE (Java Ontology Editor)URL: http://www.engr.sc.edu/research/CIT/demos/java/joe/ OilEdURL: http://img.cs.man.ac.uk/oil/ OntoEditURL: http://ontoserver.aifb.uni-karlsruhe.de/ontoedit/ OntolinguaURL: http://www-ksl-svc.stanford.edu:5915/ Ontological Constraints Manager (OCM) URL: http://protege.stanford.edu Ontology Editor by Steffen Schulze-Kremer URL: http://igd.rz-berlin.mpg.de/~www/prolog/oe.html OntoSaurusURL: http://www.isi.edu/isd/ontosaurus.html Protégé-2000URL: http://protege.stanford.edu VOID URL: http://www.swi.psy.uva.nl/projects/Kactus/toolkit/about.html WebODEURL: http://delicias.dia.fi.upm.es/webODE/index.html WebOntoURL: http://webonto.open.ac.uk

36. W. McCarthy’s Resource-Event-Agent (REA) Ontology in Protégé

39. Ontology Related Tools: from OntoWeb*: Overview of ontology tools (II) Ontology merging and integration tools ChimaeraURL: http://www.ksl.stanford.edu/software/chimaera/ FCA-Merge ToolURL: Not available. PROMPTURL: http://protege.stanford.edu/plugins/prompt/prompt.html Ontology-based annotation tools OntoMarkup Annotation ToolURL: http://kmi.open.ac.uk/projects/akt/ OntoMatURL: http://ontobroker.semanticweb.org/annotation/ontomat/index.html OntoAnnotateURL: http://www.ontoprise.de/com/co_produ_tool2.htm SHOE Knowledge AnnotatorURL: http://www.cs.umd.edu/projects/plus/SHOE/KnowledgeAnnotator.html UBOT DAML AnnotationURL: http://ubot.lockheedmartin.com/ubot/ Ontology learning tools ASIUMURL: http://www.lri.fr/~faure/Demonstration.UK/Presentation_Demo.html CORPORUM-OntoBuilderURL: http://ontoserver.cognit.no LTG Text Processing WorkbenchURL: http://www.ltg.ed.ac.uk/%7Emikheev/workbench.html Text-To-OntoURL: http://ontoserver.aifb.uni-karlsruhe.de/texttoonto/

40. Some Problems & Issues 1 • Entity-centric modeling vs. process-centric • Products: sellable items, capital items vs. consumables (MRO: maintenance, repair, & operation), properties/values, parametric search • Services: hierarchic/composable • Web service support: UDDI, ebXML, DAML-S, etc. • Processes: buyer environment, process/workflow models • Standards (taxonomies, attributes, protocols,etc.) • Universal Standard Products and Services Classification (UNSPSC), NAICS, eClass, RosettaNet, etc. • XML, EDI, Semantic Web, Emerging Web services • Methodology • Domain Knowledge + Ontology Engineering (Modeling) • Verification & Validation

41. Some Problems & Issues 2 • Different technical notions of “ontology” • Ontology as Namespace (unique concept naming standards) • Ontology as Theory (any node is a root subsuming a theory or subtheory/subontology) • Ontology as Work Module (for concurrent development, ownership, locking/unlocking, versioning) • Upper Ontology/ies • Linking to domains, UoM, etc. • Modularity • Meta Ontology (validation, tools) • Meta-properties of taxonomic (subclass, instance-of) relation • E.g., Guarino & Welty (2000), IFF (Kent, 2001)

42. Some Problems & Issues 3 • Presentation vs. Representation • Display names • Support for contexts (“views), applications • Synonyms • User (role) modeling: technical vs. Non-technical • ApplicationEquivalentTerm(OntologyConcept, Application(Term/s, UserRole)) • Multiple Languages, Cultures supported • Tools • Knowledge Representation/Ontology language: frame, logic • User interface: support ontologists & domain experts, cross-reference concepts, relations (domains, ranges), graphical display • Concurrent distributed development • Versioning: major, incremental, module, node • Import/Export formatting support • Ontology integration, merging, application/data mapping • Ontology services: name/aliasing, inference

43. Some Problems & Issues 4 • Planning, scoping, scheduling • strategic, tactical • Education & Marketing • ontologists, domain experts, sw development staff • business managers, executive officers, sales/industry people • external consortia, external contractors, sellers, buyers • Control

44. Well-defined subclass relation Other ontological relations Ill-defined parent-child relation Mappings General Problem: Ontology To Ontology/Taxonomy Mapping In practice, Merging is not an option General product Industrial process Industrial process A Z M Equipment used In the process Generated from Specific product Products of the process Y Employees involved in the process X W B C D E S F V T G H I J Simple, Informal E-Commerce Application Taxonomy (Reference) Ontology

45. Taxonomic Standard to Ontology Mapping Ontology Electronics Namespace UN/SPSC Namespace Ontology node UNSPSC node Inheritance (subclass) UNSPSC Mapped to Electronics Domain Ontology Use of Nebenstruktur (shadow structure)

46. Goals for Creating Mappings • Representational Adequacy:A mapping solution should represent the mapping between the source and ontology completely and consistently. • Completeness: any information in the application that could be queried by an external application has been mapped • Consistency: the most appropriate node in the ontology is mapped to the external structure • Heuristic adequacy: A mapping solution must be relatively easy to create and convenient to access. • An individual mapping should not require significant computation or user interaction • Represent once:A mapping solution should not introduce duplication in representation. • Knowledge engineering cost/Supports automation:A mapping solution should not increase the workload or day-to-day activities of ontologists and domain experts. • Solution should provide support to automate mappings • Because not all mappings can be automated, tools are needed to allow non-domain/non-ontology experts to create mappings

47. Ontology Mapping Methods • Microtheories: Lena, Guha, et al, 1990, etc., Cycorp • “Little Theories” and Theory Interpretation: Farmer et al, 1994, MITRE • Articulation Ontologies: Wiederhold, Mitra, Jannink, 2000, Stanford U. • Graph Homomorphisms: Many • Conceptual Anchoring: Noy, 2001, SMI • Local Models Semantics (Context): Giunchiglia, Ghidini, 1997, U. Trento • Formalized Context: McCarthy, Guha, Buvač, 1990, etc. Stanford U. • Morphisms (Category Theory): Many • Information Flow Theory: Barwise & Seligman, 1997 • Information Flow Framework Candidate Upper Ontology (IEEE Standard Upper Ontology): Robert Kent, 2001 • Intercontext Correlation: Skvortsov, Kalinichenko, 2001, Institute for Problems of Informatics, Russian Academy of Science • Schema Mapping: Rahm & Bernstein, Universität Leipzig, 2001

48. Semantic Web • Current Web is machine-readable, not machine-understandable • What is 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." • T. Berners-Lee, J. Hendler, and O. Lassila. 2001. The Semantic Web. In The Scientific American, May, 2001, http://www.scientificamerican.com/2001/0501issue/0501berners-lee.html • Languages to support machine-interpretable semantics of Web artifacts

49. Semantic Web & Its Languages • Base Semantic Web Languages: • XML, XML Schema, XML domain languages: ebXML, etc. • Semantic Web Languages: • RDF/S: Resource Description Framework/Schema • DAML+OIL: DARPA Agent Markup Language+ Ontology Inference Layer • OWL: Ontology Web Language • Future?

50. Is XML the Ticket? Well, It Helps, But, No • “The great thing about XML is that it enables the incredible experimentation we see in the marketplace. But there are hundreds of XML groups creating Internet commerce 'languages'. This, coupled with the various transaction standards in common use, presents formidable obstacles to organizations wishing to build or participate in global trading webs."Howard Smith, Director, Ontology.org, & Director of Strategy, E-Business, CSC Europe, 2000 • “Interoperable computing solutions imply the existence of a sharable ontology, or common set of object semantics. Implementers will still be able to use localized and otherwise customized XML markup languages if they choose, but it should be possible to express and validate the semantics of the design as well as the raw XML syntax.” Robin Cover, XML & Semantic Transparency, http://www.oasisopen.org/cover/xmlAndSemantics.html • XML enables basic DB-like structure for documents