key-sun choi and yeun-bae kim* kaist korterm, nhk strl* [email protected]

Korea Terminology Research Center for Language and Knowledge Engineering

Terminology, Interactive Media and Standards - A Scenario toward Personalized Interactive K

nowledge Services -

Key-Sun Choi and Yeun-Bae Kim*KAIST Korterm, NHK STRL*

[email protected]

http://www.korterm.org/

mailto:[email protected]


Outline

Integrating the multiple number of lexical knowledge base

Question-answering for what-, and why-type question

By causality probingIntegration of Video clipping of answer

segments


Steps to Give Appropriate Answering to Each Customer Who Asks about BSE

BSE ?

Since it was identified in the mid-1980s in Britain, mad cow disease, or BSE, has resulted in the slaughter of millions of cattle -- and the deaths of dozens of people from the related brain-wasting disease known as...

Term: BSEBT: diseasesymptom: Sponge-like change of a brain partcause: infectious-prionsynonym: mad cow

(5) template: term “BSE”consists of characteristics for BT, symptom and casuse.

diseaseBSEcow disease

mad cow

disease

noun

infectious

Prionnervous ..

BT

synony

mPO

Scause

sympto

m

mad

related

(7) representation

…a fatal disease of cattle affecting the nervous system, resembling or identical with scrapie of sheep and goats, and probably caused by a prion transmitted by infected tissue -- an infectious protein particle similar tovirus …

(9) definition for semi-expert

a brain disease of cows that causes death, and some people who eat them…

(9’) definition for child

(8) (2) unit of

(3)

no item in dictionary when

the first BSE

BSE ?

Since it was identified in the mid-1980s in Britain, mad cow disease, or BSE, has resulted in the slaughter of millions of cattle -- and the deaths of dozens of people from the related brain-wasting disease known as...

(1) Query

Term: BSEBT: diseasesymptom: Sponge-like change of a brain partcause: infectious-prionsynonym: mad cow disease

(4) Acquisition(5) Templateterm “BSE”consists of characteristics for BT, symptom and casuse.


mad cow

disease

noun

infectious

Prionnervous ..

BT

synony

mPO

Scause

sympto

m

mad

related


mad cow

disease

noun

infectious

Prionnervous ..

BT

synony

mPO

Scause

sympto

m

mad

related

(7) Representation

…a fatal disease of cattle affecting the nervous system, resembling or identical with scrapie of sheep and goats, and probably caused by a prion transmitted by infected tissue -- an infectious protein particle similar tovirus …

a brain disease of cows that causes death, and some people who eat them…

(9’) Definition for Child

(6) Integration

(8) Presentation(2) Unit of Database

(3) Wrapping

no item in dictionary when

the first BSE

(9)Definition for Semi-expert


Cow disease database

D1D2

world space (a1)

(b1) cow disease relates to human disease

(b4) mad cow disease causes human brain disease

(b3) human disease causes cow disease

D4D3

disease

cow diseas

e

knowledge space (a2)

human brain disease

(a3) justification

Referent mapping

(a4)

BSE

disease food

disease beef

disease vegetable

disease meat

(b2) cow disease causes human disease

disease cow

disease

animal

disease plant

edible (d2)

Disease 　living thing

justification cause (d2)

disease

human

infected Referent

mapping (d2)

(d1) justification instance

human disease

(b) hypotheses

(c1) ontology for disease

(c3) ontology for living thing

Human disease

database

Justification process goes from the world space to the knowledge space


Does mad cow disease cause human brain disease?an enlarged information space

H2: cause(mad cow disease,human brain disease)

H3: cause(mad cow disease,human brain and neural disease)

H4: cause(mad cow disease,human brain and non-neural disease)

…

…

media on H3 article on H2 media on H1article on H4 …

Worldspace

Knowledgespace

justification

opposition

specification

H1: relate(cow disease,human disease)

hypothesisuniversal hypothesis

specification

generalization

absurd hypothesisreferent


Construction of knowledge space

How to construct the knowledge space and

its linkage to the associated justification world space By

Experienced humans or

(Semi-)automatic machines Knowledge Discovery from Text


Tell me whether a mad cow disease will cause a human brain disease.

Where is relevant resources in response to a given knowledge request?

How to meta-data catalogs by categorizing the information content in each repository

Information Seeking Problem


Does the “mad cow disease” cause the “human brain disease”?

What is the correlation between “mad cow disease” and “human disease”?

Where is the related repository? the human disease repository, and

The mad cow disease repository.

Knowledge Seeking Problem


Tell me whether a mad cow disease will cause a human brain disease.

How to correlate concepts- Find possible relationships between animal

disease and human disease Link a animal disease record with the corresponding

a human disease report. What is shared ontology?

Disease, virus, … What is related contextual ontology?

Food chain, time period How to standardize that?

ISO/TC37 MPEG7


Knowledge Seeking Model

When you have a pneumonia, you should be rest and take an antifebrile if you have a high fever ….

When you have a pneumonia, you should be rest and take an antifebrile if you have a high fever ….

The new corona virus is the leading hypothesis for the cause of SARS . The primary way that SARS appears to spread is by close person-to-person contact.

The new corona virus is the leading hypothesis for the cause of SARS . The primary way that SARS appears to spread is by close person-to-person contact.

I feel cold and suffer a high fever.

I feel cold and suffer a high fever.

What is the cause of SARS?What is the cause of SARS?No specific treatment recommendations can be made at this time. Empiric therapy should include coverage for organisms associated with any community-acquired pneumonia of unclear etiology, …..

No specific treatment recommendations can be made at this time. Empiric therapy should include coverage for organisms associated with any community-acquired pneumonia of unclear etiology, …..

Would you please tell me an effective remedy?

Would you please tell me an effective remedy?

OntologyOntology

nutritionblood Waste mattersecretion

bacteria physiologyblood/secretion/waste matter

Health caredrink/eat

sense

rest Physical disability

condition/abnormalityimperfect

disease injury

animal

animate

object

inanimate

drug

Human activityaction

life

Natural phenomenawork<abstract>

abstract

concrete/abstract

concrete

High feverOBJ

feelSUBJ

Cold

cause symptom remedy sufferlipappear

blister

ADVSUBJ

understand

pneumonia

Knowledge Seeking


Knowledge Seeking

Necessity of Knowledge SeekingQuestions requiring -- "why", "what" , "how“ type questions

Problems of Knowledge SeekingJustification Probing on knowledge How to utilize various Knowledge resources

GoalBuilding an algorithm for

searching some topical paths in order to find causal explanations for questions

E.g., “Why do patients pay money to doctors?”


Knowledge Representation

Two types

Tree （ Hierarchical ）Graph


Knowledge Representation

Implication

doctor human, *cure, #occupation, medical Instantiation

*cure {doctor, medical worker, …} Event Relation

cause(cure,suffer-from)cure.patient = suffer-from.experiencer

cure.content = cure.content

Converse/Antonymy

Converse(take,give)


Knowledge Structure Knowledge structure : Multi-facet Structure

Knowledge space for each knowledge feature

Knowledge feature : e.g. isa(x,y)

give take

pay

*cure*cure

支払い

givegive take

Alter-possession

patient

doctor occupationoccupation money

cure

earn $earn$earn

#occupation#occupation

entity

event

earn

人間

why

role

question

cause

dictionaryConceptfacets

pay

human

converse

agent=possession=target=

agent=possession=source=


Justification by Knowledge Seeking

(1) Doctor cures the patient and earn the money as occupation(2) ‘Pay’ is ‘give’. Converse of ‘give’ is ‘take’. ‘Patient’ is the agent (*) of ‘give’

and the source ($) of ‘take’. ‘Take’ is the hypernym of ‘earn’. “The patient pay X to the doctor.” is “The doctor earn X.”

(3) The hypernym of ‘pay’ is ‘take’. From “pay money”, the possession of ‘take’ is ‘money’.

patient doctor occupation money

$cure *cure earn $earn

#occupation

*pay $pay

give takeconverse

agent=patientpossession=target=

agent=possession=source=patient

entity

alter-position event

conversecrossing

(1)

(2)

(3)

doctor

doctormoneymoney


Video indexing scheme based on natural language content description

T3

Vid

eo

Material

segm

entatio

n

T1

T2

T3

T1

T2

T3

“doctor cures patient”

“doctor earns”

parsin

g

SDS-type indexScript-type index

doctorcure patient

earn

subject

subject

object(from T1 to T2)

(from T2 to T3)timeT1 T2

doctorcure

patientearn

descriptivecomponents

Attach

ing

d

escriptio

n


Gaps in possible syllogism Syllogism

If pay(human2,money,to-human1) is earn(human1,money,from-human2), and earn(doctor,money,from-human2),Then pay(human2,money,to-doctor).

Axiom-1Converse (antonymy) event role inter-relation

pay(agent=human2,content=X,target=human1) iff earn(agent=human1,content=X, source=human2)

Axiom-2If cure(doctor, human2) and occupation(doctor), then earn(doctor,money,source=human2)

Extended syllogismIf Axiom-2 and cure(doctor,human2) and occupation(doctor), then earn(doctor,money,from-human2).


Gaps in Linguistic Knowledge Base Query:

Why doctor cure patient?Why doctor earn money?Why patient pay money to doctor?

Extended syllogismAxiom-2

If cure(doctor, human2) and occupation(doctor), then earn(doctor,money,source=human2)

If Axiom-2 and cure(doctor,human2) and occupation(doctor), then earn(doctor,money,from-human2).

Gaps in Linguistic Knowledge BaseNo axiom

Motivation of Crossover algorithm: causal linkingto link the gaps in linguistic knowledge baseto find possible axioms


Motivation of Crossover algorithm

Motivation of Crossover algorithm: causal linking

to link the gaps in linguistic knowledge base

to find possible axioms


Similarity: mission

Mission

for two nodes, check the connectability between two nodes

if sense ambiguities for a word,select the best sense.

if there are multiple expansion possibilities in the next step,

select the best node.

Final goal

to find a causal linkage


Similarity: motivated example

For query: “Why does doctor cures patient?”similarity guides to find all kinds of possible situation between doctor and patient before/during/after “doctor cures patient”.

for example, Patient suffers from a disease. Doctor cures the patient. Doctor is an occupation. Occupation is to earn money for living. All properties of doctor (e.g., cure) is relevant to the

occupation. Curing is to earn the money. Doctor earns the money. Patient pays the money to the doctor.


Similarity for causal connectability

How to find the relevance from linguistic knowledge base incl. role relation for:

patient ~ disease

suffer from ~ cure

earn ~ cure

pay ~ patient

patient ~ disease

role relation suffer from ~ cure

cause interrelation earn ~ cure

subject of event

relevance of occupation

pay ~ patient

converse event relationpay ~ earn


Similarity Measure patient ~ disease

role relation suffer from ~ cure

cause interrelation earn ~ cure

subject of eventrelevance

of occupation pay ~ patient

path up and hypernym’s general properties’ inheritance

pay < actpatient < human < *act

patient ~ diseaseto find the same event feature.patient human $cure *sufferFromdisease medical $cure undesired

suffer from ~ cureto expand thru event interrelation.sufferFrom(exp=X,cont=Y) causes cure(agent=A,patient=X,cont=Y)

earn ~ cure*earn ~ *cure, *earn ~ $cure

to compare of role entities of events.doctor *cure #occupation

*cure ~#occupation if they are inside of the definition of ‘doctor’.

occupation affairs earn alive pay ~ patient

pay << act (hypernym)human *actpatient << human

path-up, to find role entity of event, path-down


Rationale of Similar, but exhausted list must be checked.

1. feature similarsimilar(1)

2. expand similarsearch “rolerelation”

for event interrelation3. inverse and sister similar

a. expand inverse similarsimilar if their role entities share these events.(expanded version) similar if their inverse concepts share these two concepts.

b. expand sister and feature similarIf they are in sisters, they are similar, and plus if the sister (e.g., occupation) contains it as a feature. (crossover(1))

4. Algorithm: path-up + inverse + path-down

• path similarSimilarity between two hypernyms

1. patient ~ diseaseto find the same event feature.patient human $cure *sufferFromdisease medica $cure undesired

2. suffer from ~ cureto expand thru event interrelation.sufferFrom(exp=X,cont=Y) causes cure(agent=A,patient=X,cont=Y)

3. earn ~ curea. *earn ~ *cure, *earn ~ $cure

to compare of role entities of events.b. doctor *cure #occupation

*cure ~#occupation if they are inside of the definition of ‘doctor’.

occupation affairs earn alive4. pay ~ patient

pay << act (hypernym)human *actpatient << human

path-up, to find role entity of event, path-down

Link


similar(Level)

Goalto merge all of similarity measures

path similar

feature similar

crossover similar (not yet fixed)

to give Level of SimilarityNotation: similar(Level)


Level of Similarity

Motivation:

to be able to compare the similarity levels

to be dynamically adaptable to the knowledge levels

Levels of details

Level 0: based on itself and its top path= path similar

Level 1: to expand to the features=feature similar

Level 2:


human*sufferFrom$cure

doctor cure patientwhy

human#occupation*curemedical

agentpatientcontentmedical

medicine*disease&medical#

cure>cause>sufferFrom{patient=experiencer,content=content}

cure>possible consequence>beRecovered{patient=experiencer}

similar level 0

commercial$earn*buy#sell$setAside

patient pay moneywhy

human*sufferFrom$cure

agentcontentsource

payer*money advanced$

give>consequence>lose{agent=possessor,possession=possession}

give<implication<receive{possessor=target,possession=possession}

doctor

give

hypernym

take

converse

hypernym

occupation affirsearn

human#occupation*curemedical

Virtual Knowledge Base: causal linking


A Snapshot


Experimentation

SourceConcept

Reached concept’s path size

path = 1 path = 2 path = 3

Cure 275 593 24,854

Eat 268 605 24,903

Study 276 358 23,172

Food 532 650 18,066

Human 6,713 3,686 51,171

Money 328 1,312 19,827

Q2: Why does researcher read textbook?Path: researcher → #knowledge → #information ← readings ← textbook

Interpretation: Researcher is related to the knowledge and the knowledge is related to the information. Textbook is the object of readings and readings is related to information. So researcher read textbook.

Connected concepts

Q1: Why do patients pay money to doctors? Path: patient → $cure → doctor → #occupation ← $earn ← money

Interpretation: Patients cured by doctor. The doctor is related to the occupation and the occupation is that to earn the money. So patients pay money to doctors.


Future Works Resources

EDR, WordNet, CycHowNet based

Algorithm and RepresentationLexical ChainInterpretation as AbductionBayesian Belief Net

CausalityStopping ConditionWhat is “Causality” and “Explanation”?

Automatic Video Synchronization with Causal Justification Path


Conclusion

How to link the already existing linguistic knowledge base To be tested for the definition and the causality link. To be adapted for the user knowledge level

to find more causality link. How to link the video archive to linguistic causal path

Dependency structure

MPEG7

key-sun choi and yeun-bae kim* kaist korterm, nhk strl* [email protected]

Documents

human disease repository

human disease report

causemad cow disease

mad cow disease repository

animal disease record

knowledge seekingknowledge

human brain diseaseh3

human diseaselink