海外出張報告

OFMR2006
Various ontologies and
MMF Ontology Registration
OKABE, Masao
Co-editor, MMF Ontology Registration Project, ISO/IEC JTC1 SC32/WG2
Corporate Systems Department, Tokyo Electric Power Co., Inc.
2006.3.21
Introduction
Nowadays, “Ontology” is almost a buzzword and the meaning is very
broad and is not so clear.
In this presentation, I will present;
my personal understanding of ontologies
and
how MMF Ontology Registration* is related to my understanding of
ontologies.
This topic is very challenging to me. Any comments are very welcome.
* Note:
MMF Ontology Registration is a part of mulch part standards ISO/IEC 19763#
#
Project leader: Hajime Horiuchi (Tokyo International University, Japan)
Its current status is FCD.
Co-editors of MMF Ontology Registration are
 HE Keqing (SKLSE, Wuhan university, China)
 OKABE, Masao (Tokyo Electric Power Co., Inc., Japan)
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Outline
1. My personal understanding of ontologies
2. Various kinds of ontologies
3. Trustiness of ontologies
4. MMF Ontology Registration
Appendix: What TEPCO intends to do
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1. My personal understanding of ontologies
2. Various kinds of ontologies
3. Trustiness of ontologies
4. MMF Ontology Registration
Appendix: What TEPCO intends to do
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Famous definition of ontology
A famous definition is;
“An ontology is a specification of a conceptualization.”
by T. R. Gruber
at http://www-ksl.stanford.edu/kst/what-is-an-ontology.html
This definition is very abstract, but has a very important point.
That is, “a specification of a conceptualization”
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A specification of a conceptualization
Using the famous “Meaning Triangle” by Ogden and Richards,
a specification of a conceptualization can be explained as a image of
mapping from UoD to the world of symbols based on thought.
A chef bakes
a pancake!
Thought
 Chef
 y  Pancake
Bake（x, y）
x
specification of
conceptualization
Symbol
Referent (in UoD)
That is, ontology is not a thing in our mind and is an explicit expression
in some language (or word).
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Simple use case
A simple use case from
7.2 Wine Agent, 7.Usage Example, OWL Web Ontology Language Guide,
http://www.w3.org/TR/owl-guide/
Wine
Ontology
Tell me a appropriate wine for the dinner.
I will serve a special tomato based pasta
source with fresh pasta as the main course.
The recommended one is
Marietta Zinfandel.
Wine
agent
Where can I buy it?
Winerlibrary.com has a sale on it.
Wine agent needs to be able to understand Wine ontology
and find a suitable answer.
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What is an ontology?
From this simple use case, somewhat more specific
definition can be gained, that is,
An ontology is a set of descriptions of the UoD in a formal
language so that a computer can understand it and share it.
Then, the question is what it means that “A computer can
understand an ontology and share an ontology.”
I think there are two meanings
One is “Uniqueness of interpretation”.
 almost impossible
The other is “Uniqueness of inference”.
 needs to be embodied.
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Uniqueness of interpretation
“Uniqueness of interpretation” means that, for example, symbol “Wine”
in the Wine ontology cannot be interpreted as other than wine in the
UoD.
But, Semantics, Logical Entailment in KIF manual
（http://logic.stanford.edu/kif/Hypertext/node13.html）
The goal of knowledge encoding is to write enough sentences so that
unwanted interpretations are eliminated. Unfortunately, this is not
always possible.
I do not know why it is not always possible, but practically it seems so
because, first of all, to define UoD rigorously is almost impossible.
Actually, there may be an interpretation that interprets “Wine” as
Japanese sake and “Winegrape” as sake-rice.
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Uniqueness of inference (1 of 3)
A little bit different picture of Simple use case
Tell me a appropriate wine for the dinner.
I will serve a special tomato based pasta
source with fresh pasta as the main course.
MyPortal
agent
Wine
Ontology
Wine
agent
The recommended one is
Marietta Zinfandel.
Where can I buy it?
Winerlibrary.com has a sale on it.
How much is it?
Uhm… There are so many questions.
Wine ontology is open to all.
You can find the answer by yourself.
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Uniqueness of inference (2 of 3)
Then,
Wine
Ontology
Again,
Tell me a appropriate wine for the dinner.
I will serve a special tomato based pasta
source with fresh pasta as the main course.
The recommended one is
St. Clement Merlot.
MyPortal
agent
What!!! Isn’t it Marietta Zinfandel???
Uniqueness of inference means that this kind of things never happen.
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Uniqueness of inference (3 of 3)
Suppose
computer A and B have the same ontology, i.e. the same set of
descriptions of the universe of discourse.
Then,
From the ontology, computer A can draw all the inferences that
computer B can draw from the ontology. And vice versa.
The inferences that computer A and B draw from the ontology does
not contradict the inferences a human draws from the universe of
discourse that the ontology describes.
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Requirements of a language for ontologies
Based on the definitions of ontologies above,
there are two requirements of a language for ontologies
First, “Syntax”
so that a computer can analyze the descriptions.
Second, “Formal semantics (or model-theoretic semantics
or interpretation conditions etc.)”
that specifies conditions of interpretations and procedures of
inference at least sound, usually complete and preferably
decidable
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Ontology vs. Knowledge base
So, in my formal position, there is no specific distinction
between an ontology and a knowledge base.
But, intuitively, there is difference between an ontology and
a knowledge base.
An ontology does not lay an emphasis on inference so
much as an knowledge base does, but mainly focuses on
describing the universe of discourse so far as necessary.
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About formal semantics
In my informal position, I accept ontologies that are
described in a language that does not have its formal
semantics explicitly. So, for example, a model in UML can
be an ontology.
It depends on what kind of query (in a broad sense) an
ontology accepts whether an ontology needs its formal
semantics or not.
If an ontology accepts only usual procedural queries,
something like in SQL, then it does not need its formal
semantics explicitly.
If an ontology accepts more declarative queries, something
like ‘Tell me the suitable wine for the pasta with tomato’,
then it needs its formal semantics.
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1. My personal understanding of ontologies
2. Various kinds of ontologies
3. Trustiness of ontologies
4. MMF Ontology Registration
Appendix: What TEPCO intends to do
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Various kinds of ontologies
Based on my understanding of ontologies above,
there are several axes to classify ontologies,
which are not necessarily uncorrelated,
in addition to “Upper ontology vs. Domain ontology”.
Heavy weight ontology vs. Light weight ontology
Ontology of the real world vs. of information systems
Ontology on the static aspect vs. on the dynamic aspect
Ontology with data-flavor vs. with program-flavor
Ontology for a computer vs. for a human
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Heavy weight vs. Light weight
Heavy weight ontology
A rich and formal ontology that an inference engine can derive necessary
results.
Example
 “Wine ontology” (and “Food ontology”) from which an agent can derive a suitable
wine for a dish.
–Actual “Wine ontology” at http://www.w3.org/TR/2003/PR-owl-guide-20031209/wine and
“Food ontology” at http://www.w3.org/TR/2003/PR-owl-guide-20031209/food are not so rich,
even though “Food ontology” has several rules on the appropriate combination of food and
wine.
 Ontologies based on PSL-Core and -Outercore in TC184 or FLOWS-Core in W3C
Light weight ontology
A relatively simple ontology that mainly focuses on the relations among
concepts.
No explicit distinction from a taxonomy and a thesaurus.
Example:
 Ontologies in RDF or Topic Maps
 Traditional terminological medical ontologies such as SNOMED-III
 Ontologies in MIT Process Handbook
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Real world vs. Information systems (1 of 2)
The real world
information systems (specifications)
UoD
UoD
modeling for
specifications of conceptualizations
Ontology of the real world
stands for
stands for
information
system
Ontology of information systems
Note:
This issue is almost the same as whether class “employee” means an employee
in the real world or the objects in the HR systems at an object-oriented analysis.
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Real world vs. Information systems (2 of 2)
Ontology of the real world
Example:
 Wine ontology, Food ontology
 Medical ontologies such as SNOMED-CT, Galen etc.
Ontology of information systems
Practically, this is very important application domain of ontologies.
Example:
 Ontologies for semantics web services
 Ontologies that commercial tools focus on
–Ontologies by Sandpiper’s Medius
–Ontologies by ILOG’s Business Rule Management System
–Ontologies by FairIsaac’s Blaze Advisor
–Ontologies by Ontologyworks’s Integrated Ontology Development Environment
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Static aspect vs. Dynamic aspect (1 of 2)
Analogy to UML model
Class diagram etc.
Activity diagram etc.
formalized
formalized
Ontology on the static aspect
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Static aspect vs. Dynamic aspect (2 of 2)
Ontology of the static aspect
Example:
 Wine ontology, Food ontology
 Medical ontologies such as SNOMED-CT, Galen etc.
 Ontologies by Ontologyworks’s Integrated Ontology Development
Environment
Ontology of the dynamic aspect
There are several naming.
 process ontology, service ontology, task ontology…
Example:
 Ontologies for semantics web services
 Ontologies based on PSL-Core and –Outercore
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Data-flavor vs. Program-flavor
Ontology with data-flavor
Ontology that are analyzed by others
Example:
 Wine ontology, Food ontology
 Medical ontologies such as SNOMED-CT, Galen etc.
 “ServiceProfile” of ontologies in OWL-S
Ontology with program-flavor
An ontology that itself is executed,
mainly focuses on Semantic Web services
Example:
 “ServiceModel” of ontologies in OWL-S
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For a computer vs. for a human
Ontology for a computer
According to my definition, any ontologies have to be understandable
to a computer in some sense.
Example
 Wine ontology, Food ontology, Ontologies in OWL-S, Ontologies in CLIF
 Ontologies by Ontologyworks’s Integrated Ontology Development
Environment etc.
Ontology for a human
But, practically what a computer can understand from, for example,
ontologies in RDF or even in OWL is almost limited to query (i.e.
SPARQL) and annotations for a human (i.e. definitions in a natural
language) in these ontologies are very important.
Moreover, there are ontologies that a computer can hardly
understand.
Example
 Ontologies in MIT Process Handbook
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1. My personal understanding of ontologies
2. Various kinds of ontologies
3. Trustiness of ontologies
4. MMF Ontology Registration
Appendix: What TEPCO intends to do
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About “Truth” of ontologies
In an ontology or model theory,
every sentence in an ontology is “true”
by nature.
But in reality, it is not easy
to know whether an ontology can really be trusted,
to define (characterize fully) the existing concept
so as to conform what human recognize it,
to check consistency among many ontologies.
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Semantic Web’s position on “trustiness” (1 of 2)
The famous story by Tim Berners-Lee
…Lucy instructed her Semantic Web agent through her
handheld Web browser. The agent promptly retrieved
information about Mom's prescribed treatment from the
doctor's agent, looked up several lists of providers, and
checked for the ones in-plan for Mom's insurance within a
20-mile radius of her home and with a rating of excellent
or very good on trusted rating services…
at http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21
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Semantic Web’s position on “trustiness” (2 of2)
Semantic Web seems very optimistic on “trustiness”.
At the top of the Semantic Web stack, there is “Trust”,
but it still seems to have no substance.
Semantic web people seem to believe that
the knowledge on the web can be trusted
because it must have been weeded out
if it could not be trusted.
This is too optimistic to
apply ontologies to industries.
From Tim Berners-Lee's Keynote at WWW2005
http://www.w3.org/2005/Talks/0511-keynote-tbl/#[17]
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1. My personal understanding of ontologies
2. Various kinds of ontologies
3. Trustiness of ontologies
4. MMF Ontology Registration
Appendix: What TEPCO intends to do
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Objectives of MMF Ontology Registration
The objectives of MMF Ontology Registration are to support
trustiness of ontologies so that ontologies can be used
practically in industries.
To ensure their trustiness, standardized ontologies in each
business domain should be registered in MMF Ontology
Registration registry as “Reference Ontologies”.
Also ontologies localized for some application based on
Reference Ontologies should registered in MMF Ontology
Registration registry as “ Local Ontologies”.
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Ontologies for MMF Ontology Registration
What kind of ontologies should be registered in MMF
Ontology Registration registry?
Any kinds of ontologies, including the ones presented in
“2. Various kinds of ontologies”, should be able to registered
in MMF Ontology Registration registry as Reference or
Local Ontologies, so far as they are useful.
Therefore, MMF Ontology Registration has to have very
generic structure that can be applied to almost any kinds of
ontologies.
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Common basic structure of ontology
A very simplified but common three granularity level structure is;
Ontology
An ontology consists of sentences.
e.g. Example_Ontology consists of
 Buyer has.Creditrating(Tony)
 Buyer(Tony)
 Creditrating(Credit-A)
Sentence
A sentence consists of symbols.
e.g. Buyer has.Creditrating(Tony) consists of
 Buyer
 Creditrating
 has
 Tony
 logical symbols  ,  ,  (and variables )
Symbol
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MMF Ontology Registration structure(1)
MMF Ontology Registration consists of
Ontology, Ontology Component, Ontology Atomic Construct
that correspond to
ontology, sentence, symbol * respectively
and that have
administrative information ** of its correspondent
structural information of this level
a reference(URI) to its correspondent,
for further semantics, if necessary
Note
* : Logical symbols such as  ,  ,  and variables are ignored.
**: inherited from Administered Item of ISO/IEC 11179-3 MDR ,
such as registration authority, creation date etc.
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MMF Ontology Registration structure(2)
Actual ontology
Ontology
MMF Ontology Registration
reference
Ontology
+administrative info.
consistOf
Sentence
reference
Ontology Component
+administrative info
use
Symbol
Ontology Atomic Construct
reference +administrative info
MMF Ontology Registration mainly relies
on OMG ODM for actual ontologies.
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 e.g.
Administrative information etc.
corresponding to
 Example_Ontology
 e.g.
Administrative information etc.
corresponding to each of
 Buyer has.Creditrating(Tony)
 Buyer(Tony)
 Creditrating(Credit-A)
 e.g.
Administrative information etc.
corresponding to of each
 Buyer  Creditrating
 has
 Tony
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Reference and Local Ontologies
Reference Ontology
Standardized ontology in each business domain
Trustworthy to others
A reference ontology is composed by sentences only in reference
ontologies.
A sentence in a reference ontology uses symbols only in reference
ontologies
Local ontology
Localized ontology for some application system based on Reference
Ontologies
It is its user’s responsibility to trust this ontology or not.
A local ontology is composed by sentences both in this local ontology
and other reference ontology.
A sentence in a local ontology uses a symbols in this local ontology
and other reference ontologies.
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Core portion of MMF Ontology Registration metamodel
Reference Ontology
Reference Ontology Component
Local Ontology
sameAs
Local Ontology Component
0:1
0:*
Reference Ontology Atomic Construct
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0:1
0:*
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1. My personal understanding of ontologies
2. Various kinds of ontologies
3. Trustiness of ontologies
4. MMF Ontology Registration
Appendix: What TEPCO intends to do
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Appendix: What TEPCO intends to do (1 of 2)
TEPCO (Tokyo Electric Power Co.,Inc)
is one of the largest private electric power utility companies and
operates and maintains many facilities, including nuclear power
stations, fusel fuel power stations, hydro power stations, sub-stations
etc.
faces the time when its skilled engineers are retiring.
So, it is important to maintain and improve the engineering
know-how systematically.
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Appendix: What TEPCO intends to do (2 of 2)
TEPCO is now trying to establish ontologies that support the
operations and maintenances of our facilities.
The ontologies TEPCO intends to establish are
on the real worlds,
mainly on the dynamic aspect,
with data-flavor,
mainly for a human and partially for a computer,
hopefully heavy-weight, so that a computer can check invalid
operations and maintenance procedures.
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Thank you for your attention.
Any comments are very welcome to
okabe.masao<at>tepco.co.jp
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