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1 Explicit Semantics for Business Ontology - an interim work report from the Ontolog Forum Adam Pease Articulate.

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Presentation on theme: "1 Explicit Semantics for Business Ontology - an interim work report from the Ontolog Forum Adam Pease Articulate."— Presentation transcript:

1 1 Explicit Semantics for Business Ontology - an interim work report from the Ontolog Forum Adam Pease Articulate Software Peter Yim CIM Engineering, Inc. Presented at the Semantics Harmonization Panel Session of the EIDX Conference Dec. 1, 2004 – Menlo Park, CA, USA by v 1.00

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

3 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 ontologs 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 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 5 Presentation Contents Ontolog Forum Ontology Suggested Upper Merged Ontology Core Component Type representation effort

6 6 Pursuit of Rigor in Data Standards Old-style (most common) standards specifications: (ISO 14258, Requirements for enterprise-reference architectures and methodologies) 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 , Product Description and Support) ENTITY product_context SUBTYPE OF (application_context_element); discipline_type : label; END_ENTITY; Semantic-model standards (IEEE P SUMO, ISO , PSL Core) (forall (?t1 ?t2 ?t3) (=> (and (before ?t1 ?t2) (before ?t2 ?t3)) (before ?t1 ?t3))) Thanks to Steve Ray, NIST

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

8 8...and the Computer's View name CV educationworkprivate

9 9 But wait, we've got XML -

10 10 But wait, we've got XML -

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

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

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

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

15 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 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 17 Language Formality & Expressiveness Formality Expressiveness Human Language OWL+RuleML, KIF weak semantics 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 Syntactic Interoperability Structural Interoperability Semantic Interoperability Thanks to Leo Obrst, MITRE

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

19 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 20 Validation ( Tool Screenshot) Thanks to Peter Denno, NIST

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

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

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

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

25 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 26 SUMO (continued) Formally defined, not dependent on a particular implementation Open source toolset for browsing and inference –https://sourceforge.net/projects/sigmakee/https://sourceforge Many uses of SUMO (independent of the SUMO authors and funders) –http://www.ontologyportal.org/Pubs.html

27 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 28 SUMO Structure Structural Ontology Base Ontology Set/Class TheoryNumericTemporal Mereotopology GraphMeasureProcessesObjects Qualities

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

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

31 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:

32 32 CCT-Rep Project – Worksheet and Ontology

33 33 CCT-Rep Project – example: defining URI

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

35 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 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-BobSmithKurtConrad-BoNewman-BobSmith


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