Adam Pease Articulate Software adampease@earthlink ontologyportal/

1. Adam Pease Articulate Software adampease@earthlink.net http://www.ontologyportal.org/ http://home.earthlink.net/~adampease/professional/ Peter Yim CIM Engineering, Inc. peter.yim@cim3.com http://www.cim3.com/ Explicit Semantics for Business Ontology- an interim work report fromthe Ontolog Forumhttp://ontolog.cim3.net Presented at the “Semantics Harmonization” Panel Session of the EIDX Conference Dec. 1, 2004 – Menlo Park, CA, USA by v 1.00

2. Presentation Contents • Ontolog Forum • Ontology • Suggested Upper Merged Ontology • Core Component Type representation effort

3. Ontolog Forum (started May 2002) • Ontolog is an open forum to: • Discuss practical issues and strategies associated with the development of both formal and informal ontologies used in business • Identify ontological engineering approaches that might be applied to the UBL effort (and by extension, to the broader domain of eBusiness standardization efforts) • Virtual team collaboration with open source tools • About 100 member from 12 countries - Industry, Government, and Academia, geographically distributed • Among ontolog’s activities: Collaboration on business ontology - Component projects to encode a business ontology in formal logic • Acknowledgement: group participation that produced what we are presenting here - Patrick Cassidy (Micra), Kurt Conrad (SagebrushGroup), Peter Denno (NIST), Robert Garigue (BMO), Nenad Ivezic (NIST), Holger Knublauch (Stanford-Protégé), Monica Martin (Sun), Bill McCarthy (MSU), Tim McGrath (UBL-LCSC), Garret Minakawa (Oracle), Brand Niemann (EPA), Bo Newman (KMForum), Leo Obrst (MITRE), Adam Pease (Articulate), Sue Probert (UN/CEFACT-TBG17), Steve Ray (NIST), Bob Smith (TallTreeLabs), Alan Stitzer (UN/CEFACT-CCTS), Susan Turnbull (GSA), Evan Wallace (NIST) & Peter Yim (CIM3)

4. Ontolog: CCT-Representation project • Goal: To influence the adoption of ontology in eBusiness standards • Mission • Ontologize ebXML Core Component Types ("CCT") • engage CCT community • produce a reference CCT ontology • report on findings and recommendations for submission to UN/CEFACT CCTS (and possibly the Harmonization) working group(s). • Deliverables: • a reference ontology of approved ebXML Core Component Types ("CCTONT") • a report on findings and recommendations regarding the current CCT specifications

5. Presentation Contents • Ontolog Forum • Ontology • Suggested Upper Merged Ontology • Core Component Type representation effort

6. Old-style (most common) standards specifications: (ISO 14258, Requirements for enterprise-reference architectures and methodologies) “3.6.1.1 Time representation If an individual element of the enterprise system has to be traced then properties of time need to be modeled to describe short-term changes. If the property time is introduced in terms of duration, it provides the base to do further analyses (e.g., process time). There are two kinds of behavior description relative to time: static and dynamic.” Data-model standards (ISO 10303-41, Product Description and Support) ENTITY product_context SUBTYPE OF (application_context_element); discipline_type : label; END_ENTITY; Semantic-model standards (IEEE P1600.1 - SUMO, ISO 18629-11, PSL Core) (forall (?t1 ?t2 ?t3) (=> (and (before ?t1 ?t2) (before ?t2 ?t3)) (before ?t1 ?t3))) Pursuit of Rigor in Data Standards Thanks to Steve Ray, NIST

7. name Joe Smith BS Case Western Reserve, 1982 MS UC Davis, 1984 education CV 1985-1990 ACME Software, programmer work private Married, 2 children Imagine...your view of the web

8. name education CV work private ...and the Computer's View

9. <job name=”Joe Smith” title=”Programmer”> But wait, we've got XML -

10. <job name=”Joe Smith” title=”Programmer”> <x83 m92=”|||||||||” title=”..............”> But wait, we've got XML -

11. Mammal Person JoeSmith But wait, we've got Taxonomies -

12. x931 o4839 i3729 But wait, we've got Taxonomies -

13. Mammal Mammal subclass Person implies instance instance JoeSmith JoeSmith Wait, we've got semantics -

14. Mammal Mammal subclass Person implies instance instance JoeSmith JoeSmith x9834 x9834 r22 u8475 implies r53 r53 p3489 p3489 Wait, we've got semantics -

15. Semantics Helps a Machine Appear Smart • A “smart” machine should be able to make the same inferences we do • (let's not debate the AI philosophy about whether it would actually be smart)

16. Definitions • An ontology is a shared conceptualization of a domain • An ontology is a set of definitions in a formal language for terms describing the world

17. strong semantics Modal Logic First Order Logic Human Language Logical Theory Description Logic DAML+OIL, OWL UML Conceptual Model Semantic Interoperability RDF/S Taxonomy XTM Extended ER Thesaurus ER DB Schemas, XML Schema Structural Interoperability Expressiveness weak semantics Syntactic Interoperability Formality Language Formality & Expressiveness OWL+RuleML, KIF Is Disjoint Subclass of with transitivity property Is Subclass of Has Narrower Meaning Than Relational Model, XML Is Sub-Classification of Thanks to Leo Obrst, MITRE

18. Cyc WordNet SUMO+domain SUMO UMLS Yahoo! DOLCE Taxonomy Lexicons Formal Ontology Size Formality Content Formality and Size

19. Many Ways to Use Ontology • As an information engineering tool • Create a database schema • Map the schema to an upper ontology • Use the ontology as a set of reminders for additional information that should be included • As more formal comments • Define an ontology that is used to create a DB or OO system • Use a theorem prover at design time to check for inconsistencies • For taxonomic reasoning • Do limited run-time inference in Prolog, a description logic, or even Java • For first order logical inference • Full-blown use of all the axioms at run time

20. Validation (2004-11-23 Tool Screenshot) Thanks to Peter Denno, NIST

21. CCTONT – Protégé version Thanks to Pat Cassidy, MICRA

22. Upper Ontology • An attempt to capture the most general and reusable terms and definitions

23. Ontology Language - Expandable - language independent - machine understandable - understood by humans - ambiguous Knowledge - changes rapidly - may be local to an entity Ontology vs Language and Knowledge

24. Presentation Contents • Ontolog Forum • Ontology • Suggested Upper Merged Ontology • Core Component Type representation effort

25. Suggested Upper Merged Ontology • 1000 terms, 4000 axioms, 750 rules • Mapped by hand to all of WordNet 1.6 • then ported to 2.0 • A “starter document” in the IEEE SUO group • Associated domain ontologies totalling 20,000 terms and 60,000 axioms • Free • SUMO is owned by IEEE but basically public domain • Domain ontologies are released under GNU

26. SUMO (continued) • Formally defined, not dependent on a particular implementation • Open source toolset for browsing and inference • https://sourceforge.net/projects/sigmakee/ • Many uses of SUMO (independent of the SUMO authors and funders) • http://www.ontologyportal.org/Pubs.html

27. WordNet • Lexical database • 100,000 word senses – synsets • Created by George Miller's group at Princeton • Free • De facto standard in the linguistics world

28. Structural Ontology Base Ontology Set/Class Theory Numeric Temporal Mereotopology Graph Measure Processes Objects Qualities SUMO Structure

29. SUMO Structural Ontology Base Ontology Set/Class Theory Numeric Temporal Mereotopology Graph Measure Processes Objects Qualities Mid-Level WMD Transnational Issues Financial Ontology Geography ECommerce Services Communications Distributed Computing Government People Military Terrorist Attack Types Terrorist Transportation Economy Biological Viruses Terrorist Attacks UnitedStates Elements NAICS Afghanistan France World Airports … SUMO+Domain Ontology Total Terms Total Axioms Total Rules 20399 67108 2500

30. Presentation Contents • Ontolog Forum • Ontology • Suggested Upper Merged Ontology • Core Component Type representation effort

31. ebXML Core Component Types • Map each concept to the SUMO and its domain ontologies • 10 Core Components mapped • 43 Supplemental Components mapped • 7 terms needed to extend SUMO • Ref. CCT-Representation Project • see: http://ontolog.cim3.net/cgi-bin/wiki.pl?CctRepresentation

32. CCT-Rep Project – Worksheet and Ontology

33. CCT-Rep Project – example: defining URI

34. CCT-Rep Project– CCT-to-SUMO Mapping

35. Issues • Clarifying code vs. identifier • An issue of purpose not of content? • Requiring a formalization of each in logic results in clear and unambiguous definition • Clarifying implementation vs implementation independent semantics

36. Conclusion: Business Case • Standards development is hard work • Most standards bodies work harder than they have to • Standards-setting bodies are susceptible to ontological gaps • Gaps hamper progress and threaten both the expressiveness and semantic stability of the resulting specifications • Ontologically-formalized standards should be easier to adopt • They provide numerous migration, integration, and interoperability advantages • This approach will yield the greatest benefits when it incorporates • conceptual modeling • ontological engineering • use of a standardized upper ontology • An ontological engineering approach will identify knowledge gaps • which will need to be addressed, but should improve the flow of knowledge both within the standards committee and to downstream communities • Businesses and other communities can be expected to enjoy standards that are more stable, easier and less expensive to develop, and provide more rapid returns on investments Source: KurtConrad-BoNewman-BobSmith