1 © 2007 Adam Pease, Articulate Software - apease [at] articulatesoftware [dot] com The Suggested Upper Merged Ontology (SUMO) at Age 7: Progress and Promise.

1© 2007 Adam Pease, Articulate Software - apease [at] articulatesoftware [dot] com

The Suggested Upper Merged Ontology (SUMO) at Age 7:

Progress and Promise

Adam PeaseArticulate [email protected]

http://www.articulatesoftware.com

http://www.ontologyportal.org/

http://home.earthlink.net/~adampease/professional/

Presented at Ontolog

6 September 2007


Overview

• SUMO is a large, open source, formal ontology stated in first-order logic

• Mapped to a large multi-lingual lexicon

• With open source tools for ontology development and application


What's New

• More content about social relationships, justice and law, military events-people-processes

• Wikipedia (DBpedia) links

• Updated mappings to WordNet 3.0

• New tests of inference and many new inference engines

• SQL and XML generation tools

• Many new academic and commercial uses


SUMO Prize - 2007

• US$3000.00

• Due December 1, 2007

• Entries must be open source SUO-KIF files that extend SUMO

• Judged on several criteria:– Degree of formalization

– Scope and coverage

– Coherent new topic or domain

– Actual utility in an application


Pursuit of Rigor in Data Standards

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)))

Thanks to Steve Ray, NIST


C.K. Ogden/I.A. Richards, The Meaning of MeaningA Study in the Influence of Language upon Thought and The Science of SymbolismLondon 1923, 10th edition 1969

Concept

Referent

Refers To Symbolizes

Stands For“Orange”

Terms and Concepts

from the slide of [Bargmeyer, Bruce, Open Metadata Forum, Berlin, 2005]

Slide adpated from (c) Key-Sun Choi for Pan Localization 2005

Term

Ontology work should be here,since logic is needed to substitute for

human thought.

Lots of “ontology” workhas really been here.


Imagine...your view of the web

CV

name

education

work

private

Joe Smith

BS Case Western Reserve,1982MS UC Davis, 1984

1985-1990 ACME Software,programmer

Married, 2 children


...and the Computer's View

name

CV

education

work

private

Thanks to Frank van Harmelen for the original idea of this slide and Peter Yim for the Chinese language content


But wait, we've got XML -

<job name=”Joe Smith” title=”Programmer”>


But wait, we've got XML -

<job name=”Joe Smith” title=”Programmer”>

<x83 m92=”|||||||||” title=”..............”>


But wait, we've got Taxonomies -

Person

Mammal

JoeSmith


But wait, we've got Taxonomies -

o4839

x931

i3729


Wait, we've got semantics -

Person

Mammal

JoeSmith

instance

subclass

implies

Mammal

JoeSmith

instance


Wait, we've got semantics -

Person

Mammal

JoeSmith

instance

subclass

implies

Mammal

JoeSmith

instance

u8475

x9834

p3489

r53

r22

implies

x9834

p3489

r53


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)


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


Frames

• Object- or term-centered

• Frames, slots, values, (and attributes)

Adam: Person

height

occupation

5'8”

consultant

cardinality: 1


Frame Restrictions• b is between a and c

– (between1 a betweenness1)– (between2 b betweenness1)– (between3 c betweenness1)– vs– (between a b c)

• Adam is not an accountant– (notOccupation Adam Accountant)– vs– (not (occupation Adam Accountant))

• Existential vs. Universal quantification

• Similar problems for many description logics

• Very efficient computation however


Digression: Implementation is Different from Representation

• Why lose meaning at design time just because of runtime issues?– We can’t reason with English definitions, but that

doesn’t mean we shouldn’t document our terms

• Many different implementations may be done from the same representation

• This does not mean that run time issues should be ignored at design time– If you represent information you know can’t be

reasoned with, it better not be essential in most conceivable applications


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


Upper Ontology

•An attempt to capture the most general and reusable terms and definitions

•Provokes thought on clarifying the meaning of more specific terms

•Provides for large-scale reuse


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


Suggested Upper Merged Ontology

•1000 terms, 4000 axioms, 750 rules

•Mapped by hand to all of WordNet 1.6

• then ported to 3.0

•Development begun in 2000– US Government small business grant

•Associated domain ontologies totalling 20,000 terms and 70,000 axioms

•Free

• SUMO is owned by IEEE but basically public domain

• Domain ontologies are released under GNU

• www.ontologyportal.org


SUMO (continued)

•Formally defined, not dependent on a particular implementation

•Open source toolset for browsing and inference

– http://sigmakee.sourceforge.net

•Many uses of SUMO (independent of the SUMO authors and funders)

– http://www.ontologyportal.org/Pubs.html


SUMO Validation

• Mapping to all of WordNet lexicon– A check on coverage and completeness (at a given

level of generality)

• Peer review– Open source since its inception

• Formal validation with a theorem prover– Free of contradictions (within a generous time bound

for search)

• Application to dozens of domain ontologies


WordNet

•Lexical database

•100,000 word senses – synsets

•Created by George Miller's group at Princeton

•Free

•De facto standard in the linguistics world


WordNet to SUMO Mapping

•WordNet synset “plant, flora, plant_life” is equivalent to the formal SUMO term 'Plant'

00008864 03 n 03 plant 0 flora 0 plant_life 0 027@ . . . | a living organism lacking the power of locomotion &%Plant=

SUMO has axioms that explain formally what a plant is(=> (and (instance ?SUBSTANCE PlantSubstance) (instance ?PLANT Organism) (part ?SUBSTANCE ?PLANT)) (instance ?PLANT Plant))


WordNet to SUMO Mapping

•Most nouns map to classes•Most verbs map to subclasses of &

%Process•Most adjectives map to a &

%SubjectiveAssessmentAttribute•Most adverbs map to relations of &

%manner


Internationalization

• Translation of SUMO paraphrases to diverse multiple languages– Some confidence there’s no cultural or linguistic bias– Chinese, Hindi, Tagalog, Czech, German, Italian,

Korean, Romanian, Arabic– Estonian and Hungarian in development

• SUMO is linked to multiple very large lexicons (Euro WordNet, Balkanet, HowNet etc)– English, Chinese, Italian, Arabic


SUMO Structure

Structural Ontology

Base Ontology

Set/Class Theory Numeric Temporal Mereotopology

Graph Measure Processes Objects

Qualities


SUMO+Domain OntologyStructuralOntology

BaseOntology

Set/ClassTheory

Numeric Temporal Mereotopology

Graph Measure Processes Objects

Qualities

SUMO

Mid-Level

Military

Geography

Elements

Terrorist Attack Types

Communications

People

TransnationalIssues Financial

Ontology

TerroristEconomy

NAICS TerroristAttacks

…

FranceAfghanistan

UnitedStates

DistributedComputing

BiologicalViruses

WMD

ECommerceServices

Government

Transportation

WorldAirports

Total Terms Total Axioms Rules

20399 67108 2500


Are SUMO Terms Directly Usable?

• Yes.

• Study – 1/3 of upper ontology terms directly appear in answers on large test– Cohen, P., Chaudhri, V., Pease A., and Schrag, R.

(1999), Does Prior Knowledge Facilitate the Development of Knowledge Based Systems, In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-1999). Menlo Park, Calif.: AAAI Press. http://home.earthlink.net/~adampease/professional/cohen-aaai99.ps

• before (in time), agent (of a process), etc.


High Level Distinctions

The first fundamental distinction is that between ‘Physical’ (things which have a position in space/time) and ‘Abstract’ (things which don’t)

Entity

Physical Abstract


High Level Distinctions

Partition of ‘Physical’ into ‘Objects’ and ‘Processes’

Physical

Object Process


Objects

ObjectSelfConnectedObject

SubstanceCorpuscularObject

RegionCollection


Processes

DualObjectProcess Substituting Transaction Comparing Attaching Detaching Combining SeparatingInternalChange BiologicalProcess QuantityChange Damaging ChemicalProcess SurfaceChange Creation StateChangeShapeChange

IntentionalProcess IntentionalPsychologicalProcess RecreationOrExercise OrganizationalProcess Guiding Keeping Maintaining Repairing Poking ContentDevelopment Making Searching SocialInteraction ManeuverMotion BodyMotion DirectionChange Transfer Transportation Radiating


Abstract

SetOrClassRelationPropositionQuantity

NumberPhysicalQuantity

AttributeGraphGraphElement


Case Roles

• Roles that entities play in a Process– agent, patient, instrument etc.


Case Roles

• “Brutus stabbed Caesar with a knife on Tuesday.”

A Stabbing

A Tuesday

A KnifeBrutus

Caesar

patient

agent

time

instrument


Case Roles

• “Brutus stabbed Caesar with a knife on Tuesday.”

(exists (?S ?K ?T) (and (instance ?S Stabbing) (instance ?K Knife) (instance ?T Tuesday) (agent ?S Brutus) (patient ?S Caesar) (time ?S ?T) (instrument ?S ?K)))

A Stabbing

A Tuesday

A KnifeBrutus

Caesar

patient

agent

time

instrument


Attributes and Roles

• (attribute JohnDoe Unemployed)

• (attribute GIJane Soldier)

• (attribute MyCar Blue)


Example Rules(=> (instance ?DRIVE Driving) (exists (?VEHICLE) (and (instance ?VEHICLE Vehicle) (patient ?DRIVE ?VEHICLE))))

“If there's an instance of Driving, there's a Vehicle that participatesin that action.”

Not just an English definition for humans to read, but a logicaldefinition that can be used in proofs.


Commercial Application

• One year project for Articulate Software

• Working with a company that creates financial transaction systems for royalty payments

• Re-engineer current ontology management business process, tools and ontology


Commercial Application

• Extensive current ontology

• Captured in spreadsheets

• Local term names and definitions for every customer– An essential part of their process

• Ontology management system that exports XML & RDF

• One end-user database is nearly 3GB– Ontology functions can be batch-process


Project Goals

• To add formality to existing model– To support full logical inference, consistency checks

• Give customers user-friendly ontology editor – so that they can maintain the ontology

• Create broader set of definitions – Enable greater DB integration

– Enable expansion into new markets

• Leverage work

• Exercise SUMO and Sigma in business


Initial Tasks

• Implement UI improvements to Sigma– Simplified tree-based editor

– Simplified frame-style browser

• XML/SQL ontology export– Uses meta-predicates for physical DB

structure

• Merge existing ontology with SUMO


DBPedia

• “People” content uses FOAF– Lightweight, redundant, ad-hoc

– Only a tiny portion is used• birthdate, deathdate, birthplace, deathplace, names,

firstname, lastname

– http://xmlns.com/foaf/spec/– 16MB KIF content

http://www.ontologyportal.org/content/DBPediaPeople.zip

• Recent announcement of DBPedia now mapped to WordNet– Which gets us links to SUMO


TPTP

• Research effort in automated theorem proving

• 40+ different first order logic provers

• Annual competition

• Thousands of test problems

• We will issue SUMO-based tests in TPTP format next month

• Sigma connected to TPTP prover suite


Controlled English to Logic Translation

• Automated translation from English to Logic

• Uses WordNet-SUMO mappings for 100,000 word sense vocabulary

• Domain-independent

• Development process– Start with a highly restricted language and

gradually add linguistic features

1 © 2007 Adam Pease, Articulate Software - apease [at] articulatesoftware [dot] com The Suggested Upper Merged Ontology (SUMO) at Age 7: Progress and Promise.

Documents

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application slide

xml slide

smart slide

nist slide

slide of bargmeyer

world slide