The InfoQuilt Project THE INFOQUILT VISION Semantic interoperability between systems, sharing knowledge using multiple ontologies Logical correlation

The InfoQuilt ProjectThe InfoQuilt Project

THE INFOQUILT VISIONTHE INFOQUILT VISION

Semantic interoperability between systems, sharing knowledge Semantic interoperability between systems, sharing knowledge using multiple ontologies using multiple ontologies

Logical correlation of informationLogical correlation of information

Media independent information processingMedia independent information processing

REALIZATION OF THE VISIONREALIZATION OF THE VISION

fully distributed, adaptable, agent-based systemfully distributed, adaptable, agent-based system

information/knowledgement supported by collaborative information/knowledgement supported by collaborative processes processes

http://lsdis.cs.uga.edu/proj/iq/iq.html

InfoQuilt Project: using the InfoQuilt Project: using the MMetadata etadata REFREFerence linkerence link

http://lsdis.cs.uga.edu/proj/iq/iq.html

MREF MREF

Complements HREF, creating a “logical web” through media Complements HREF, creating a “logical web” through media independent ontology & metadata based correlationindependent ontology & metadata based correlation

It is a description of the information asset we want to retrieveIt is a description of the information asset we want to retrieve

MREFMREF

domain ontologies

IQ_Asset ontology +extension ontologies

attributesrelations

constraints

keywords content attributes(color, scene cuts, …)

Semantic Correlation using MREF MREF Concept

Model for logical

correlation using

ontological terms

and metadata

Framework for

representing MREF’s

Serialization

(one implementation

choice)

X M L

M R E F

R D F

domain specific metadata: terms chosen from domain specific ontologies

Domain Specific Correlation – exampleDomain Specific Correlation – example

Potential locations for a future shopping mall identified by all regionsregions having a

populationpopulation greater than 5000, and areaarea greater than 50 sq. ft. having an urban

land coverland cover and moderate reliefrelief <A MREF ATTRIBUTES(population > 5000; area > 50;

region-type = ‘block’; land-cover = ‘urban’; relief = ‘moderate’) can be viewed here</A>

Population:Area:

Land cover:Relief:

Boundaries:

Census DB TIGER/Line DB US Geological Survey

Regions(SQL):

Boundaries

Image Features (image processing routines)

=> media-independent

relationships between domain

specific metadata: population,

area, land cover, relief

=> correlation between image

and structured data at a

higher domain specific level

as opposed to physical “link-

chasing” in the WWW

Domain Specific Correlation – exampleDomain Specific Correlation – example

A DL II approach for Information BrokeringA DL II approach for Information Brokering

CONSTRUCTING ADDITIONALMETA-INFORMATION RESOURCES

Physical/SimulationWorld

DISCOVERING COLLECTIONS OF HETEROGENEOUS INFORMATION AND

META-INFORMATION RESOURCES

Images Data Stores Documents Digital Media

DomainSpecific

Ontologies

Domain Independent Ontologies

Iscape N

CONSTRUCTING APPROPRIATE INFORMATION LANDSCAPESCONSTRUCTING APPROPRIATE INFORMATION LANDSCAPES

Iscape 1

ADEPT Information Landscape Concept PrototypeADEPT Information Landscape Concept Prototype(a scenario for Digital Earth:

learning in the context of the “El Niño” phenomenon)

Sample Iscapes Requests:

– How does El Niño affect sea animals? Look for

broadcast videos of less than 2 minutes.

– How are some regions affected by El Niño? Look at

East/West Pacific regions.

– What disasters have been related to El Niño?

– What storm occurrences are attributed to El Niño?

– Show reports related to El Niño that contain Clinton.

TRY ISCAPE CONCEPT DEMO

request information using

keywordskeywords

domain-specific attributesdomain-specific attributes

domain-independent attributesdomain-independent attributes

http://ra.cs.uga.edu/~amit/adept/iscape.html

Putting MREFs to workPutting MREFs to work

UserAgent

ProfileManager

user information

MREF request

retri

eve

prof

ile

User

display results

changeprofile

design MREFdomain ontologies

MREF Builder

IQ_Asset ontology +extension ontologies

construct new MREF

Broker Agent

send MREFsend results

retrieve MREF

retrieve MREF

MREFrepository

MREFrepository

Userprofiles

Context: the lynchpin of semanticsContext: the lynchpin of semantics

“For instance, if you were to use Yahoo! or Infoseek to

search the web for pizza, your results would probably

be hundreds of matches for the word pizza. Many of

these could be pizza parlors around the world. Yet if

you run the same search within NeighborNet, you will

allows you to order pizza to be delivered instead of

shipped.”

From a Press Resease of FutureOne, Inc. March 24, 1999

http://home.futureone.com/about/pr/021699.asp

Cricket




Constructing c-contexts from ontological termsConstructing c-contexts from ontological terms

Advantages: Use of ontologies for an intensional

domain specific description of data Representation of extra information

Relationships between objects not represented in the database schema

Using terminological relationships in the ontology

ONTOLOGICAL TERMS

C-CONTEXT:

“All documents stored in the database

have been published by some agency”

=> Cdef(DOC) = <(hasOrganization, AgencyConcept)> C-Context = <(C1 , V1) (C2 , V2) ... (Ck , Vk) >

a collection of

contextual coordinates Ci s (roles) and

values Vi s (concepts/concept descriptions)

AgencyConcept

DATABASEOBJECTS

DocumentConcepthasOrganization

AGENCY(RegNo, Name, Affiliation)

DOC(Id, Title, Agency)

Using c-contexts to reason about Using c-contexts to reason about

information in databaseinformation in database

Cdef(DOC)

<(hasOrganization, AgencyConcept)>

CQ

<(hasOrganization, { “USGS”})>

- Reasoning with c-contexts: glb(Cdef(DOC), CQ)

- Ontological Inferences:

- DocumentConcept

- (hasOrganization, { “USGS” })

Challenge 1: use of multiple ontologies

Challenge 2: estimating the loss of information

EXAMPLEEXAMPLE

glb(Cdef(DOC), CQ)

<(self, DocumentConcept),(hasOrganization, { “USGS” })>

Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system

OBSERVER architectureOBSERVER architecture

Data Repositories

Mappings

Ontologies

COMPONENT NODE

Data Repositories

Mappings

Ontologies

COMPONENT NODE

Data Repositories

Mappings

OntologyServer

QueryProcessor

UserQuery

Ontologies

USER NODE

InterontologiesTerminologicalRelationships

IRM

IRM NODE

OntologyServer

OntologyServer

QueryProcessor

QueryProcessor

Eduardo Mena (III’98)


“Get title and number of pages of books written by Carl Sagan”

Query construction - ExampleQuery construction - Example


User ontology: WN

[name pages] for

(AND book (FILLS creator “Carl Sagan”))

Target ontology: Stanford-I

Integrated ontology WN-Stanford-I

[title number-of-pages] for

(AND book (FILLS doc-author-name “Carl Sagan”))

Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.htmlOntologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html

http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/





User ontology: WN

[name pages] for








Biblio-Thing

Document

Book

Edited-Book

Technical-Report

Periodical-Publication

Journal

Magazine

Newspaper

Miscellaneous-Publication

Technical-Manual

Computer-Program

Multimedia-DocumentArtwork

Cartographic-Map

Thesis

Doctoral-Thesis

Master-Thesis

Proceedings

Conference Agent

PersonAuthor Organization

Publisher University

Re-use of Knowledge:Bibliography Data Ontology

Re-use of Knowledge:Bibliography Data OntologyStanford-I





User ontology: WN

[name pages] for








Re-use of Knowledge:A subset of WordNet 1.5Re-use of Knowledge:

A subset of WordNet 1.5Print-Media

Press Publication Journalism

Newspaper MagazineBook

Periodical

Trade-Book Brochure TextBook

Reference-BookSongBook

PrayerBook

PictorialSeries

Journals

CookBook

Instruction-BookWordBook HandBook Directory Annual

Encyclopedia

Manual Bible GuideBook

Instructions Reference-Manual





User ontology: WN

[name pages] for








WN ontology and user query


Estimating the loss of informationEstimating the loss of information


To choose the plan with the least loss

To present a level of confidence in the answer

Based on intensional information (terminological difference)

Based on extensional information (precision and recall)

Plans in the examplePlans in the example User Query: (AND book (FILLS doc-author-name “Carl Sagan”))

Plan 1: (AND document (FILLS doc-author-name “Carl Sagan”))

Plan 2: (AND periodical-publication (FILLS doc-author-name “Carl Sagan”))

Plan 3: (AND journal (FILLS doc-author-name “Carl Sagan”))

Plan 4: (AND UNION(book, proceedings, thesis, misc-publication, technical-report)

(FILLS doc-author-name “Carl Sagan”))


Loss of information based on intensional informationLoss of information based on intensional information


User Query: (AND book (FILLS doc-author-name “Carl Sagan”))

Plan 1:

(AND document (FILLS doc-author-name “Carl Sagan”))

book:=(AND publication (AT-LEAST 1 ISBN))

publication:=(AND document (AT-LEAST 1 place-of-publication))

Loss: “Instead of books written by Carl Sagan, OBSERVER is

providing all the documents written by Carl Sagan (even if they

do not have an ISBN and place of publication)”


Example: loss for the plansExample: loss for the plans


Plan 1: (AND document (FILLS doc-author-name “Carl Sagan”)) [case 2]

91.57% < (1-Loss) < 91.75%

Plan 2: (AND periodical-publication (FILLS doc-author-name “Carl Sagan”))

94.03% < (1-Loss) < 100% [case 3]

Plan 3: (AND journal (FILLS doc-author-name “Carl Sagan”)) [case 3]

98.56% < (1-Loss) < 100%

Plan 4: (AND UNION(book, proceedings, thesis, misc-publication, technical-

report) (FILLS doc-author-name “Carl Sagan”)) [case 1]

0% < (1-Loss) < 7.22%

Summary Summary

TextTextStructured DatabasesStructured Databases DataData Syntax,Syntax,

SystemSystem Federated DBFederated DB

Semi-structuredSemi-structured MetadataMetadata Structural,Structural,SchematicSchematic

Mediator,Mediator,Federated ISFederated IS

Visual,Visual,Scientific/Eng.Scientific/Eng. KnowledgeKnowledge SemanticSemantic

Knowledge Mgmt.,Knowledge Mgmt.,InformationInformationBrokering,Brokering,

Cooperative ISCooperative IS

Agenda for research Agenda for research

Interoperation not at systems level, but at informational and

possibly knowledge level

– traditional database and information retrieval solutions

do not suffice

– need to understand context; measures of similarities

Need to increase impetus on semantic level issues involving

terminological and contextual differences, possible

perceptual

or cognitive differences in future

– information systems and humans need to cooperate,

possible involving a coordination and collaborative

processes

http://lsdis.cs.uga.eduhttp://lsdis.cs.uga.edu[See publications on Metadata, Semantics,Context, [See publications on Metadata, Semantics,Context, InfoHarness/InfoQuilt]InfoHarness/InfoQuilt]

[email protected]@cs.uga.edu

Acknowledgements:Acknowledgements:Tarcisio LimaTarcisio Lima

Vipul KashyapVipul Kashyap

Related ReadingRelated Reading

Books: Information Brokering for Digital Media, Kashyap and Sheth, Kluwer,

1999 (to appear)

Multimedia Data Management: Using Metadata to Integrate and Apply

Digital Media, Sheth and Klas Eds, McGraw-Hill, 1998

Cooperative Information Systems, Papazoglou and Schlageter Eds.,

Academic Press, 1998

Management of Heterogeneous and Autonomous Database Systems,

Elmagarmid, Rusinkiewica, Sheth Eds, Morgan Kaufmann, 1998.

Special Issues and Proceedings: Formal Ontologies in Information Systems, Guarino Ed., IOS Press, 1998

Semantic Interoperability in Global Information Systems, Ouksel and

Sheth, SIGMOD Record, March 1999.