the suggested upper merged ontology (sumo) at age 7: progress and promise

Suggested Upper Merged Ontology© 2007 Adam Pease, Articulate Software - apease [at] articulatesoftware [dot] com
The Suggested Upper Merged Ontology (SUMO) at Age 7: Progress and Promise
Presented at Ontolog
6 September 2007
© 2007 Adam Pease, Articulate Software - apease [at] articulatesoftware [dot] com
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
Wikipedia (DBpedia) links
New tests of inference and many new inference engines
SQL and XML generation tools
Many new academic and commercial uses
SUMO Prize - 2007
Entries must be open source SUO-KIF files that extend SUMO
Judged on several criteria:
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
(forall (?t1 ?t2 ?t3)‏
(=> (and (before ?t1 ?t2)
Terms and Concepts
Concept
Referent
“Orange”
from the slide of [Bargmeyer, Bruce, Open Metadata Forum, Berlin, 2005]
Slide adpated from (c) Key-Sun Choi for Pan Localization 2005
C.K. Ogden/I.A. Richards, The Meaning of Meaning
A Study in the Influence of Language upon Thought and The Science of Symbolism
London 1923, 10th edition 1969
Refers To
since logic is needed to substitute for
human thought.
Imagine...your view of the web
CV
name
education
work
private
...and the Computer's View
Thanks to Frank van Harmelen for the original idea of this slide and Peter Yim for the Chinese language content
name
CV
education
work
private
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 -
Wait, we've got semantics -
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
Adam: Person
Frame Restrictions
(between1 a betweenness1)‏
(between2 b betweenness1)‏
(between3 c betweenness1)‏
(notOccupation Adam Accountant)‏
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
Suggested Upper Merged Ontology
Mapped by hand to all of WordNet 1.6
then ported to 3.0
Development begun in 2000
Associated domain ontologies totalling 20,000 terms and 70,000 axioms
Free
Domain ontologies are released under GNU
www.ontologyportal.org
SUMO (continued)‏
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
Formal validation with a theorem prover
Free of contradictions (within a generous time bound for search)‏
Application to dozens of domain ontologies
WordNet
Free
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 [email protected] . . . | a living organism lacking the power of locomotion &%Plant=
(=>
WordNet to SUMO Mapping
Most verbs map to subclasses of &%Process
Most adjectives map to a &%SubjectiveAssessmentAttribute
Most adverbs map to relations of &%manner
Internationalization
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
SUMO+Domain Ontology
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
High Level Distinctions
Physical
Objects
Object
SelfConnectedObject
Substance
CorpuscularObject
Region
Collection
Processes
DualObjectProcess
Substituting
Transaction
Comparing
Attaching
Detaching
Combining
Separating
InternalChange
BiologicalProcess
QuantityChange
Damaging
ChemicalProcess
SurfaceChange
Creation
StateChange
ShapeChange
IntentionalProcess
IntentionalPsychologicalProcess
RecreationOrExercise
OrganizationalProcess
Guiding
Keeping
Maintaining
Repairing
Poking
ContentDevelopment
Making
Searching
SocialInteraction
Maneuver
Motion
BodyMotion
DirectionChange
Transfer
Transportation
Radiating
Abstract
SetOrClass
Relation
Proposition
Quantity
Number
PhysicalQuantity
Attribute
Graph
GraphElement
Case Roles
agent, patient, instrument etc.
Case Roles
A Stabbing
A Tuesday
A Knife
Case Roles
(exists (?S ?K ?T)‏
Attributes and Roles
(attribute JohnDoe Unemployed)‏
(attribute GIJane Soldier)‏
(attribute MyCar Blue)‏
Example Rules
(instance ?VEHICLE Vehicle)‏
(patient ?DRIVE ?VEHICLE))))‏
“If there's an instance of Driving, there's a Vehicle that participates
in that action.”
Not just an English definition for humans to read, but a logical
definition 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
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 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
Leverage work
Initial Tasks
Simplified tree-based editor
Simplified frame-style browser
XML/SQL ontology export
Merge existing ontology with SUMO
DBPedia
birthdate, deathdate, birthplace, deathplace, names, firstname, lastname
http://xmlns.com/foaf/spec/
Which gets us links to SUMO
TPTP
Annual competition
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
A Stabbing
A Tuesday
A Knife

the suggested upper merged ontology (sumo) at age 7: progress and promise

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