Semantic Interoperability in Infocosm:Semantic Interoperability in Infocosm: Beyond Beyond Infrastructural and Data Interoperability in Federated Infrastructural and Data Interoperability in Federated

Information SystemsInformation Systems

Keynote TalkKeynote TalkInternational Conference on Interoperating Geographic Systems (Interop’97), International Conference on Interoperating Geographic Systems (Interop’97),

Santa Barbara, December 3-4 1997Santa Barbara, December 3-4 1997

Amit ShethAmit ShethLarge Scale Distributed Information Systems LabLarge Scale Distributed Information Systems Lab

University of GeorgiaUniversity of Georgiahttp://lsdis.cs.uga.eduhttp://lsdis.cs.uga.edu

Thanks: Vipul Kashyap, Kshitij ShahThanks: Vipul Kashyap, Kshitij Shah

Semantic Interoperability in Infocosm:Semantic Interoperability in Infocosm: Beyond Beyond Infrastructural and Data Interoperability in Federated Infrastructural and Data Interoperability in Federated

Information SystemsInformation Systems

Keynote TalkKeynote TalkInternational Conference on Interoperating Geographic Systems (Interop’97), International Conference on Interoperating Geographic Systems (Interop’97),

Santa Barbara, December 3-4 1997Santa Barbara, December 3-4 1997

Amit ShethAmit ShethLarge Scale Distributed Information Systems LabLarge Scale Distributed Information Systems Lab

University of GeorgiaUniversity of Georgiahttp://lsdis.cs.uga.eduhttp://lsdis.cs.uga.edu

Thanks: Vipul Kashyap, Kshitij ShahThanks: Vipul Kashyap, Kshitij Shah

• Information Integration Perspective:Information Integration Perspective:Distribution, Heterogeneity, AutonomyDistribution, Heterogeneity, Autonomy

• Information Integration Perspective:Information Integration Perspective:Distribution, Heterogeneity, AutonomyDistribution, Heterogeneity, Autonomy

Three perspectivesThree perspectives

• Information Brokering Perspective:Information Brokering Perspective:Data, Metadata, Data, Metadata, Semantic (Terminological, Contextual) Semantic (Terminological, Contextual)

• Information Brokering Perspective:Information Brokering Perspective:Data, Metadata, Data, Metadata, Semantic (Terminological, Contextual) Semantic (Terminological, Contextual)

• ““Vision” Perspective: Connectivity+Computation, Vision” Perspective: Connectivity+Computation, Information, KnowledgeInformation, Knowledge

• ““Vision” Perspective: Connectivity+Computation, Vision” Perspective: Connectivity+Computation, Information, KnowledgeInformation, Knowledge

Evolving targets and approaches in Evolving targets and approaches in integrating data and information: integrating data and information: a personal perspectivea personal perspective

Evolving targets and approaches in Evolving targets and approaches in integrating data and information: integrating data and information: a personal perspectivea personal perspective

MermaidMermaidDDTSDDTS

Multibase, MRDSM, ADDS, Multibase, MRDSM, ADDS, IISS, Omnibase, ...IISS, Omnibase, ... Early 80s

Infoscopes, HERMES, SIMS, ...Infoscopes, HERMES, SIMS, ...TSIMMIS,Harvest, RUFUS,... TSIMMIS,Harvest, RUFUS,...

VisualHarnessVisualHarnessInfoHarnessInfoHarness

1990

InfocosmInfocosm

Digital Library Projects, ..Digital Library Projects, ..

InfoQuiltInfoQuilt 1997

Generation 1Generation 1

Generation 2Generation 2

Generation 3Generation 3

Generation IGeneration I

• Data recognized as corporate resource -- leverage it!• Most data in structured databases (and the rest in

files), different data models, transitioning from Network and Hierarchical to Relational DBMSs

• Connectivity/access -- a major issue • Heterogeneity (system, modeling and schematic) as

well as need to support autonomy posed main challenges

• Support for corporate IS applications as the primary objective, update often required, data integrity important

Generation IIGeneration II

• Significant improvements in computing and connectivity (standardization of protocol, public network, Internet/Web); remote data access as given

• Increasing diversity in data formats, with focus on variety of textual data and semi-structured documents (and lesser focus on structured data)

• Many more data sources, diverse domains, but not necessarily better understanding of data

• Use of data beyond traditional business applications -- mining + warehousing, marketing, commerce

• Query only, little attention to updates; extensive use of IR techniques

• Focus shift from data to metadata; earlier, distribution applied to data only, now it also applies to metadata

• Wrapper part of Mediator Architecture*, Metadata component of Information Brokering Architecture

• Early work on ontology support

Generation IIGeneration II

* Gio Wiederhold* Gio Wiederhold

• Increasing information overload• Changes in Web architecture: push,…• Broader variety of content with increasing amount

of visual information• Continued standardization related to Web for

representational and metadata issues (MCF, RDF, XML) and distributed computing (CORBA, Java)

• Not just metadata, logical correlation

• Users demand simplicity, but complexities continue to rise

Generation IIIGeneration III

• Broader variety of users and applications; well beyond business and scientific uses (e.g., focused marketing-- more than information on the web)

• Not just data access, but decision supportdecision support through “data mining and information discovery, information fusion, information dissemination, knowledge creation and management”, “information management complemented by cooperation between the information system and humans”

Generation III Generation III (contd)(contd)

DistributionDistribution

AutonomyAutonomy

HeterogeneityHeterogeneity

Dimensions for interoperability and integration: Dimensions for interoperability and integration: Perspective used for Federated DatabasesPerspective used for Federated Databases

FDBS: Schema ArchitectureFDBS: Schema Architecture

ComponentComponentDBSDBS

LocalLocalSchemaSchema

ComponentComponentSchemaSchema

ExportExportSchemaSchema

ExportExportSchemaSchema

ExportExportSchemaSchema

FederatedFederatedSchemaSchema

ExternalExternalSchemaSchema

ExternalExternalSchemaSchema

o o oo o o

o o oo o o

o o oo o o

o o oo o o

o o oo o o

ComponentComponentDBSDBS

LocalLocalSchemaSchema

ComponentComponentSchemaSchema

• Model Heterogeneity:Model Heterogeneity: Common/Canonical Data Model Common/Canonical Data Model Schema Translation Schema Translation• Information Sharing while Information Sharing while preserving Autonomy preserving Autonomy

schema translationschema translation

schema integrationschema integration

Heterogeneity in FDBMSsHeterogeneity in FDBMSs

Hardware/System• instruction set• data representation/coding• configuration

Operating System• file system• naming, file types, operation• transaction support• IPC

Database System• Semantic HeterogeneitySemantic Heterogeneity• Differences in DBMSDifferences in DBMS

• data models data models (abstractions, constraints, query languages) (abstractions, constraints, query languages)• System level support System level support (concurrency control, commit, recovery) (concurrency control, commit, recovery)

Communication

1970s1970s

1980s1980s

Characterization of Schematic ConflictsCharacterization of Schematic Conflictsin Multidatabase Systemsin Multidatabase Systems

Characterization of Schematic ConflictsCharacterization of Schematic Conflictsin Multidatabase Systemsin Multidatabase Systems

SchematicConflicts

Domain DefinitionDomain DefinitionIncompatibilityIncompatibility

Naming Conflicts

Data RepresentationConflicts

Data Scaling Conflicts

Data Precision ConflictsDefault Value Conflicts

Attribute IntegrityConstraint Conflicts

Data ValueIncompatibility

KnownInconsistency

TemporalInconsistency

AcceptableInconsistency

Abstraction LevelIncompatibility

GeneralizationConflicts

AggregationConflicts

SchematicDiscrepancies

Data Value Attribute Conflict

Entity Attribute Conflict

Data Value Entity Conflict

Entity DefinitionIncompatibility

Naming Conflicts

Database IdentifierConflicts

SchemaIsomorphismConflicts

Missing Data ItemsConflicts

Sheth & Kashyap, Kim & SeoSheth & Kashyap, Kim & Seo

Observations and Lessons LearntObservations and Lessons Learnt

• “tightly coupled” vs “loosely coupled” debate

• “good common data model” debate

• “tightly coupled” harder to build, but can give better control over data sharing, provide more transparent access, and can possibly support update; lessons learned in schema integration can be reapplied in newer situations

• “loosely coupled” more flexible, but generally require more user involvement

Retracing the path Retracing the path without learning from past expeditionswithout learning from past expeditions

Steps for transitioning from Data Marts to Warehouses:• Create consistent dimensions in the data marts• Create a data warehouse data model and convert data

marts to it• Go back and build an enterprise data warehouse, then

convert data marts to the new common data model and architectures

The above is doomed to repeat past mistakes. Integrating metadata is not easy!

PC Week, November 24 , 1997PC Week, November 24 , 1997

Autonomy Heterogeneity Distribution

InfoHarness Semi autonomous High - semanticissues not addressed

Data fullydistributed -metadatacentralized

ConceptualIndexing

N/A Extensible semanticknowledge base

N/A

HERMES Autonomous High HighTSIMMIS Autonomous High - semantic

issues not addressedHigh

SIMS Autonomous High HighInfoSleuth Semi autonomous High - semantic

brokeringHigh

KMed Centralized control High semanticheterogeneityfocused on imagesonly

None

Generation 1 concern:So far (schematically),

yet so near (semantically)!

Generation 3 concern:So near (schematically), yet so far (semantically)!

Generation IIand

Generation III

Information Brokering: Information Brokering: A Three-Level ApproachA Three-Level Approach

OntologyOntology

ContentContent

RepresentationRepresentation

used-by

abstracted-into

SemanticSemantic(Domain, Application specific)

Metadata Metadata (content descriptions, intentional)

DataData(heterogeneous types, media)

used-by

abstracted-into

Top Down

BottomUpE

mph

asis

fro

m G

en.I

to G

en.I

II

An Architecture An Architecture for Information Brokeringfor Information Brokering

An Architecture An Architecture for Information Brokeringfor Information Brokering

User Query/Information Request

User Query/Information Request

User Query/Information Request

...DATA REPOSITORIES

...DATA REPOSITORIES

Information System 1 Information System N

INFORMATION BROKERING

Data BrokeringData Brokering (CORBA, HTTP, IIOP) (CORBA, HTTP, IIOP)

Inter-VocabularyRelationships Manager

VocabularyBroker

VocabularyBrokerVocabulary BrokeringVocabulary Brokering

MetadataBroker

MetadataRepository

Metadata System

MetadataBroker

MetadataRepository

Metadata System

Metadata BrokeringMetadata Brokering

Generation 2:Limited Types of Metadata,

Extractors,Mappers,Wrappers

Global/EnterpriseWeb Repositories

NexisUPIAP

DB

METADATA

EXTRACTORS

Generation 2Generation 2

Data Integration

Data Publishing

Publishing Rule

Publisher

Extraction Rules

Extractor

Mapping Rules

Mapper

Internet

Wrappers (SDL Description)

Text

IDTApplication

RDBMS

JungleeJunglee

Gen.2Gen.2

Find Marketing Manager positions in a company that is within 15 miles of San Francisco and whose stock price has been growing at a rate of at least 25% per year over the last three years

Junglee, SIGMOD Record, Dec. 1997Junglee, SIGMOD Record, Dec. 1997

• can automatically identify data/media type• can be extended at any time (pre-specified

or parameterized routines)• can run at data source, metadata storage site or at

IQ server• can run at pre-specified times or events, or on

demand• can route metadata to appropriate metadatabase

repositories

Extractors use agent & networking computing (NC) technologies and are implemented in PERL/ Java

ExtractorsExtractors

A ClassificationA Classification of Metadata of Metadata

• Content Independent Metadata e.g. creation-date, location, ...

• Content Dependent Metadata e.g. size, number of colors in an image

– Content-(directly)based Metadata e.g. inverted lists, doc vectors

– Content-descriptive Metadata

• Domain Independent (structural) Metadata

e.g. parse tree of a C++ program, HTML/SGML DTDs

• Domain Specific Metadatascale, coordinate, land-cover, relief (GIS Domain), area, population (Census Domain), concept descriptions from Domain Specific Ontologies

Move in this direction to tackle information overload !!

Query Processing andInformation Requests

•traditional queries based on keywords•attribute-based queries•content-based queries•'high-level' information requests

involving ontology-based, iconic, mixed-media,

and media-independent information requests•user selected ontology, use of profile

E.g., Kabila’s political activities (in all media) E.g., Kabila’s political activities (in all media)

Generation 2Generation 2

Generation 3Generation 3

VisualHarness

..

..

Image Data

Color CompTexture Structure

Other Attributes

VIR

Extraction

Null Image

Metadata for combined access

User QueryUser Query

VHVH

ResultsResultsResultsResults

Metadata Brokering in VisualHarnessMetadata Brokering in VisualHarness

VisualHarness VisualHarness An Example An Example

What else can Information What else can Information Brokering do?Brokering do?

WWW

• A confusing heterogeneity of media, formats (Tower of Babel)

• Information correlation using physical (HREF) links at the extensional data level

• Location dependent browsing of information using physical (HREF) links => User has to keep track of information content !!

WWW+Information Brokering

• Domain Specific Ontologies as “semantic conceptual views”

• Information correlation using concept mappings at the intensional concept level

• Browsing of information using terminological relationships across ontologies=> Higher level of abstraction, closer to user view of information !!

Ontologies for Ontologies for semantic interchangesemantic interchange

• Need for “transcending” local subject areas/domains => Design Adaptable systems which “adapt/adjust” themselves in the face of vocabularies from different domains

• Coordination and interrelation of models across domainsOne approach => utilize terminological relationships across concepts in ontologies

• Specification languages for ontologies:– Description Logics, Rule-based Languages

– Support for mechanisms for Coordination and Correlation, viz., representation and reasoning with terminological relationships

The InfoQuilt Project

http://lsdis.cs.uga.edu/infoquilt

MREFMetadata Reference Link -- complementing HREF

Creating “logical web” through

Media Independent Metadata based

Correlation

Metadata Reference Link (<A MREF …>)

• <A HREF=“URL”>Document Description</A>

physical link between document (components)

• <A MREF KEYWORDS=<list-of-keywords>; THRESH=<real>>Document Description</A>

• <A MREF ATTRIBUTES(<list-of-attribute-value-pairs>)>Document Description</A>

• <A MREF(<parameterized_routine(….)> Document Description</A>

Correlation based on Correlation based on Content-descriptive Metadata Content-descriptive Metadata

Some interesting <A MREF KEYWORDS=“scenic waterfall mountain”; THRESH = 0.9>information on scenic waterfalls</A> is available here.

Content Descriptive Metadata

Marina wonderlandYou are seeing the nature’s beauty of marina wonderland situated in thecoastal region of the southern part of India. It consists of huge mountainsand water flowing in between the mountains.

WAISLSIGlimpseSMART….….

waterfall.gif (Data)

Full TextIndexing

height, width and size

waterflow.gif (Data)Metadata Storage

waterflow.gif

……gif

……ppm

Major component(RGB)Major component(RGB)

Blue

Content based MetadataContent based Metadata

ContentDependentMetadata

Correlation based on Content-based Metadata

Some interesting <A MREF KEYWORDS= “scenic waterfalls”; THRESH = 0.9; ATTRIBUTES (major-color = ‘blue’)> information on scenic waterfalls</A> is available here.

Metadata,Metadata,Domain Specific OntologiesDomain Specific Ontologies

Metadata,Metadata,Domain Specific OntologiesDomain Specific Ontologies

Get the titlestitles, authorsauthors, documentsdocuments, maps published by the United States Geological Service (USGS) about regionsregions having a populationpopulation greater than 5000, areaarea greater than 1000 acreshaving a low density urban area land coverurban area land cover

domain specific metadata: terms chosen from domain specific ontologies

What is Metadata ?

- data/information about data- useful/derived properties of media- properties/relationships between objects

What are Ontologies ?- collection of terms, definitions and their interrelationships- specification of a representational vocabulary for a shared domain of discourse

TIGER/Line DB

Population: Area:

Boundaries:

Land cover:Relief:

Census DB Image/Map DB

Regions(SQL)

Boundaries

Image Features(image processing routines)

Repositories and Repositories and the Media Typesthe Media Types

Domain Specific CorrelationDomain Specific Correlation

Potential locations for a future shopping mall identified by all regions having a population greater than 500 and area greater than 50 sq ft having an urban land cover and moderate relief <A MREF ATTRIBUTES(population > 500; area > 50; region-type = ‘block’; land-cover = ‘urban’; relief = ‘moderate’)>can be viewed here</A>

=> 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

InfoQuilt Architecture (partial)

Media Independent Information Requests[Browsing Collections, Keyword-based queries,Attribute-based queries]

CorrelationServer

Media and Domainspecific ExtractorAgents

...

IQR: Metadata & DomainKnowledge Repository and Registry

loc, type, author

Attr.Metadata

ParameterizedRoutines

InfoQuiltServer

KnowledgeBase

Other InfoQuilt Servers

Domain Knowledge

Indices

Text, Image, Audio, Video media repositories

Wrapper Wrapper Wrapper

What next What next (after comprehensive use of metadata)(after comprehensive use of metadata) ? ?

• Context, context, context• Semantic Proximity

– domain– context– modeling/abstraction/representation– state

• Characterizing Loss of Information incurred due to differences in vocabulary

BIG challenge: identifying relationship orsimilarity between objects of different media, developed and managed by different persons and systems

A Semantic TaxonomyA Semantic Taxonomy

Semantic Semantic ProximityProximity

SemanticSemanticResemblanceResemblance

SemanticSemanticRelevanceRelevance

SemanticSemanticRelationshipRelationship

SemanticSemanticEquivalenceEquivalence

SemanticSemanticIncompatibilityIncompatibility

Tools to support semantics

ontologiesontologies

profilesprofiles

contextcontext

domain-specific metadatadomain-specific metadata

Computing Communication

Information

Knowledge

Data

Decision

Connectivity and Data Access

Interoperability

Cooperation

Computing Communication

Information

Knowledge

Data

Decision

Connectivity

Interoperability

Cooperation

Interoperability in the ‘80s

System level interoperability like TCP/IP. Standard communication channels, data exchange formats, etc.

Basic infrastructural work for higher level interoperability.

HTTP, IIOP, TCP/IPHTTP, IIOP, TCP/IP

Computing Communication

Information

Knowledge

Data

Decision

Connectivity

Interoperability

Cooperation

Interoperability in the ‘90s

Information level interoperability. Standards evolve that go beyond connectivity and define information standards. Systems start exchanging metadata (MCF,RDF,..).

Business Objects, CORBA, DCOM, EDI

Computing Communication

Information

Knowledge

Data

Connectivity

Interoperability

Cooperation

Where we are headed

Semantic interoperability where systems share ontologies and knowledge.

Systems and human can cooperate in decision making and can generate new knowledge as a collective entity.

KNOWLEDGE

Cooperative Information SystemsCooperative Information Systems

Collective exploitation of complementary

technologies

InformationManagement

Coordination» Schedulin

g» Workflow

Collaboration» Video

Conferencing

» Whiteboarding

» Application sharing

Computing Communication

Information Interoperablity

Knowledge

Data

InfocosmInfocosm

Cooperating Information Systems

Summary Summary

• We have addressed many data level (schematic, representational,…) issues so far

• We are in a good position to solve additional issues using metadata level; need to support domain-specific metadata and “media-independent” information requests, qualified by use of ontologies

• some challenges remain: e.g., consistency of metadata

Agenda for ResearchAgenda 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, InfoHarness/InfoQuilt][See publications on Metadata, Semantics, InfoHarness/InfoQuilt]

amit@cs.uga.eduamit@cs.uga.edu