1 Defining Vocabularies, Ontological and Linguistic: A Tool for Ontologizing the Ontolog Patrick Cassidy MITRE Corporation* Presented to the Ontolog Forum

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Defining Vocabularies,Ontological and Linguistic:

A Tool for Ontologizing the Ontolog

Patrick CassidyMITRE Corporation*

Presented to the Ontolog ForumJuly 13, 2006

* NOTE: The author’s affiliation with The MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions or viewpoints expressed by the author.

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Outline• A Common Upper Ontology is a Good Thing to

have. The minimum upper ontology will represent the set of basic concepts sufficient to specify the meanings of all other more specialized concepts.

• A Linguistic Defining Vocabulary that parallels the Ontological Defining Vocabulary will make the Upper Ontology a lot easier to build, understand, and exploit.

• To help in ontologizing the Ontolog, we can use the Linguistic Defining Vocabulary right now.

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Problem Solving with Computers:Single Applications

Interface

Procedural ProgramSpecification:

Solve Current Problem. Report results

Short-TermTask-specific

Memory

PersistentStoredData

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Tightness of Coupling & Semantic Explicitness

Implicit, TIGHT

Explicit, Loose

Local

Far

1 System: Small Set of Developers

Systems of Systems

Enterprise

Community

Internet

Looseness of Coupling

Se

ma

nti

cs

Ex

plic

itn

ess

Data

Same Address Space

Same DBMS

Federated DBs

Data WarehousesData Marts

Workflow Ontologies

Semantic Mappings

XML, XML Schema

Conceptual Models

RDF/S, OWLWeb Services: UDDI, WSDL

OWL-S

Modal Policies

Application

Same Process Space

Same CPUSame OS

Same Programming Language

Same Local Area NetworkSame Wide Area Network Client-Server

Same Intranet

Compiling

Linking

Agent Programming

Web Services: SOAP

Distributed Systems OOP

Applets

Semantic Brokers

Middleware Web

Peer-to-peer

N-Tier Architecture EAI

From Synchronous Interaction to Asynchronous Communication

Performance = k / Integration_Flexibility

Source: Leo Obrst

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Problem Solving with Computers:Application Suite

Interface

Common Operating Systemfor Stored data Access

Short-TermTask-specific

Memory

StoredData 1

Message Format and Protocol

App 1 App 2 App 3

StoredData 2

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Upper Ontology:Provides defining concepts to specify

conceptual message Content

Where Does The Upper Ontology Fit?

NLP Understandingand generation

Case-Based Reasoning

Informtn.Retrieval

Probabilistic Reasoning

SpatialReasoning

Long-TermKnowledge

Base:Ontology

usesUpper

Ontology forConcept

Specifications

Interfaces

RoboticsExpert

Systems

Task Control: SelectProcesses To Solve Current Problem. Report results

Short-TermMemory:Ontology

usesUpper

Ontology

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Overlord:Situation Awareness

Goal-Directed Action Selection






Informtn.Retrieval

Probabilistic Reasoning

SpatialReasoning

EGO:Self Awareness

EpisodicMemory

KnowledgeBase:

Ontologyuses

UpperOntology for

ConceptSpecifications

Interfaces

RoboticsExpert

Systems


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Agents: Expectations & Norms

Goal StackRules:(1) Laws(2) Community(3) User(4) Supervisor/Owner(5) Self-generated(6) Cultural ExpectationsCommitment:

(1) Generate or Modify(2) Reference(3) Explain when asked

Is Goal PossibleWithin TimeConstraints?No. Report

Impediments

Yes. Request

Overlord action

Current Goal

OL

OL

OL

OL

Add/Modify/Delete Goals

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LearningProbabilistic Reasoning

SpatialReasoning

Long-TermKnowledge

Base:Ontology

usesUpper

Ontology forConcept

Specifications

Interfaces

RoboticsExpert

Systems



usesUpper

Ontology

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Is a Common Language EnoughFor Module Integration?

Some doubt has been expressed:“Merely encapsulating this machinery into modules

that pass messages among each other does not allow for the internal operation of one algorithm to be influenced by another”– Nicholas Cassamatis, A Cognitive Substrate for Achieving

Human-Level Intelligence, AI Magazine, Summer 2006, pp. 45-56.

However, the use of the Upper Ontology not only permits effective message passing, but fuses all components into a unified knowledge base. The ontology has not only a message format, but logical inference which may be used by multiple modules.

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Modules May Have Tight Coupling



LearningProbabilistic Reasoning

SpatialReasoning

Long-TermKnowledge

Base:Ontology

usesUpper

Ontology forConcept

Specifications

Interfaces

RoboticsExpert

Systems



usesUpper

Ontology

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Where Does Upper Ontology Fitin Natural Language Understanding?

Parsing Disambiguation LearningEntity

ExtractionMetaphoricReasoning

KnowledgeBase:

Ontologyuses

UpperOntology for

ConceptSpecifications

Interfaces

WordExperts


TC

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What About Ontology Mapping Rather Than a Common UO?

• OK if:– Modest accuracy (10-80%) is acceptable– Very shallow reasoning (e.g. taxonomy only)

is to be used

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First Order Logic?

• In a multimodule message/blackboard system, FOL is only one of potentially many reasoning mechanisms.

• Decidability and inference efficiency are not rate-limiting unless FOL alone is to be used on the full knowledge base.

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So, What’s the Problemwith existing Upper Ontologies?

• It is time-consuming to learn how to use them effectively.– If an interface that uses language people

already know can be developed, it will make upper ontologies easier to exploit.

– Human language is easy for people to use.

– A defining vocabulary can serve as the intermediate phase to improve usability.

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Outline• A Common Upper Ontology is a Good Thing to have

A Linguistic Defining Vocabulary that parallels the Ontological Defining Vocabulary will make the Upper Ontology a lot easier to build, understand, and exploit.

• To help in ontologizing the Ontolog, we can use the Linguistic Defining Vocabulary right now.

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What is a “Defining Vocabulary”?

• For lexicographers, a controlled list of words which are the only words allowed to be used in creating definitions (e.g. in LDOCE).– makes definitions easier to understand, especially

for learners of a language

• For Ontologists, the set of basic concepts (and their ontological representations) which are sufficient to specify the meanings of any other concepts (or terms) by combinations of the basic concepts – a special type of “Upper Ontology”.

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How Big is the Defining Vocabulary?

• Longman’s Dictionary of Contemporary English (LDOCE) uses about 2000 root words, some of which are used in more than one sense. With morphological variants, there are over 9000 words.

• For the conceptual defining vocabulary, probably at least 4000 senses will be needed.

• The vocabulary will probably grow over time.

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Is There a Relation Between theLinguistic Defining Vocabulary and

The Conceptual Defining Vocabulary (Upper Ontology)?

• Hypothesis: yes, we should be able to use a linguistic controlled vocabulary like that of LDOCE and have definitions in that vocabulary translate directly and automatically into logical specifications using the conceptual inventory of the Upper Ontology.

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How Does this Differ from other Ontology Projects?

• By emphasizing the primary importance of developing the defining vocabulary – ontological and linguistic – and creating relations between them, before attempting representation of complex domain-specific concepts.

• The automatic conversion of linguistic to logical specifications is an essential element.

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So, This has no Immediate Real-World Application?

• Right! We do not already know the minimum set of conceptual components for representing everything; which basic concepts are required needs to be discovered by using a common upper ontology in generating other concepts.

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Is This Just Basic Research?

• NO! The need for information communication is immediate, and clear descriptions of information content can have immediate benefits.

• There are no negative side-effects to creating clear and comprehensible descriptions of information content.

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Specialists Will Want to Use Specialized Terms in Definitions

• The “Controlled Defining Vocabulary” is infinitely expandable.

• Probably, at least three levels will emerge:– the basic irreducible defining vocabulary– the general defining vocabulary, having terms

which are defined by use of the basic vocabulary

– specialized defining vocabularies, containing terms of interest to specific domains

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How Will We Know When We Have Succeeded in Building The

Linguistic Conceptual Defining Vocabulary?

When almost all new terms can be defined using intuitive linguistic phrases, and the words are already in the defining vocabulary.

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How Will We Know When We Have Succeeded in Building A Correct

Mapping of Linguistic and Conceptual Defining Vocabularies?

• When people can enter and retrieve information of a basic nature using intuitive linguistic phrases.

• The most honest measure of correct representation is a correct answer to a straightforward question.

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Example Definitions from Longman’s

• Raspberry– a soft sweet red berry, or the bush that this

berry grows on

• Obligation– a moral or legal duty to do something

• Automobile -- a car

• Car– a vehicle with four wheels and an engine, that

can carry a small number of passengers

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Language Logic

• Duty -- Longman: something that you have to do because it is morally or legally right

• More specific:– An action that an intelligent agent must

perform or refrain from; the failure to perform or refrain from that action carries some undesirable consequence for that agent; the undesirable consequence may be enforced by the authority that assigned the duty.

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KIF “Duty”(=> (hasDuty ?AGENT ?DUTY)) (and (instance ?DUTY ActionOrInaction) (exists (?AUTHORITY ?CONSEQUENCE) (and (hasAttribute ?CONSEQUENCE (UndesirableFor ?

AGENT)) (imposed ?AUTHORITY ?DUTY) (=> (not (performed ?AGENT ?DUTY)) (hasLiability ?AGENT (enforces ?AUTHORITY ?

CONSEQUENCE)))))))

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Spatial Metaphors

• Orientation: Up, Down, Front, Back, Side• Proximity: in Space, In time• Basic shapes: compact, filament, sheet,

multi-armed, network, hollow (container)• Motion: origin, goal, path• Edge: limit, obstacle, support• Contact: Force, gravity, causing motion• Body shape: Head, Foot, Arm• Parts and structural relations

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What About Specialized Applications That Don’t Need a

High-Level Ontology?

• They can interoperate with other applications if they map the concepts they do use to the precisely defined concepts in the upper ontology.

• This principle can be applied transitively, through multiple levels of expressiveness.

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Definition Acceptance Hierarchy

Executable Specification: Methods, Sequence, States

Axiomatic Ontology: Quasi-2nd Order, Function Terms

OpenCycSUMO DOLCE

Restricted FOL: OWL

Taxonomy/Thesaurus/Terminologyaccepts

accepts

is used in

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What Can We Do Now?

• Begin immediately to define terms in Knowledge Organization Systems (ontologies, taxonomies, glossaries, etc.) using the basic English defining vocabulary.

• Add terms to the supplemental defining vocabulary as needed, with their definitions created from the basic terms

• Add terms to the community domain vocabularies, with their definitions created from the basic or supplemental defining vocabulary.

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Where is the Defining Vocabulary?

• A version that runs in Windows XP is on the ONTACWG web site, along with a Java utility to check definitions against the controlled vocabulary.

http://colab.cim3.net/file/work/SICoP/ontac/reference/ControlledVocabulary/CheckCV.ZIP

Unzip in a separate directory and run the .bat file to use the utility (Opening screen shot, next slide).

http://colab.cim3.net/file/work/SICoP/ontac/reference/ControlledVocabulary/CheckCV.ZIP

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And if The English Logic Translation Project Lags?

• We will have terminologies and knowledge classifications with well-defined terms, understandable to anyone with a basic knowledge of English.

– In itself, this result will be worth the effort.

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END

Documents

1 Defining Vocabularies, Ontological and Linguistic: A Tool for Ontologizing the Ontolog Patrick Cassidy MITRE Corporation* Presented to the Ontolog Forum