Rutgers, October 2002

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Invitational Workshop on Database and Information

Systems Research

For Semantic Web and Enterprises


Amit Sheth & Robert Meersman



NSF Information & Data Management PI’s Workshop

Amit Sheth & Isabel Cruz


Recap @ Science on the Semantic Web,

Rutgers, October 2002

“Ask not what the Semantic Web Can
do for you, ask what you can do for
the Semantic Web”

Hans
-
Georg Stork, European Union

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


Context for Amicalola workshop


Series of Workshops and upcoming conferences:
Lisbon (9/00), Hong Kong (5/01), Palo Alto (7/01),
Amsterdam (12/01); since then WWW2002/ISWC


Observation: visible lack of DB/IS involvement


“Semantic Web


The Road Ahead,”

[Decker, Hans
-
Georg Stork, Sheth, …
SemWeb’2001 at WWW10,
Hongkong, May 1, 2001.

]


Semantic Web: Rehash or Research Goldmine


[Fensel, Mylopoulous, Meersman, Sheth (Chair), CooPIS’01]


At Castel Pergine, Italy


Semantics & IDM


Brief History

(partial)


Semantic Data Modeling

M. Hammer and D. McLeod: "
The Semantic Data Model: A Modelling Machanism for Data
Base Applications
"; Proc.. ACM SIGMOD, 1978.



Conceptual Modeling

Michael Brodie, John Mylopoulos, and Joachim W. Schmidt.
On Conceptual Modeling
. Springer
Verlag, New York, NY, 1984. A series of preceding workshops.


Data Semantic: What, Where and How?

-

"Database Semantics", R.A. Meersman and T.B. Steel (eds), Proceedings of the IFIP DS
-
1
Conference, North
-
Holland (1985).


-

So Far (Schematically) yet So Near (Semantically)

Sheth, Keynote at DS
-
5


-

Meersman, Navathe, Rosenthal, Sheth (Chair); IFIP DS
-
6 Panel


Semantic Interoperability on Web

many projects in 90s


1994 CIKM paper on Semantic Information Brokering talked about query
processing in a multi
-
ontology environment


Domain Modeling, Metadata, Context, Ontologies, Semantic
Interoperability, Semantics in Schema Integration, Semantic
Information Brokering, Spatio
-
temporal
-
geographic
-

image
-
video
-
multimodal semantics


All these involving Semantics, Databases, IS and even Web


before “Semantic Web” term is coined


Challenges


unique role of IDM

SCALE and PERFORMANCE

Acceptable computation (query/analysis) time when
you have millions and billions of instances
(documents, digital content) and metadata
(annotation)


locking for sharing/storage management


Semantic similarity, mappings, interoperability
(schema transformation/integration aka ontology
mismatch)


indexing for expediting computations


workflow for Web Services
-
based processes


Organization/Output


20+ senior researchers/practitioners


2.5 days in Georgia Mountains


Proceedings of position papers (also talks)


Three workgroups: Application Pull
(Brodie/Dayal)
,
Ontology
(Decker/Kashyap)

and Web Services
(Fensel/Singh)


<SWIS WG at IDM PI’s meeting>


Review at OntoWeb3 Panel


Final Report


SIGMOD Record special issue December 2002

Every thing is at lsdis.cs.uga.edu/SemNSF/



Participants

Karl Aberer
, LSIR, EPFL, Switzerland

Mike Brodie
, Verizon

Isabel Cruz
, The University of Illinois at Chicago

Umeshwar Dayal
, Hewlett
-
Packard Labs

Stefan Decker
, Stanford University

Max Egenhofer
, University of Maine

Dieter Fensel
,


Vrije Universiteit Amsterdam

William Grosky
,University of Michigan
-
Dearborn

Michael Huhns
,


University of South Carolina

Ramesh Jain
, UC
-
San Diego, and Praja

Yahiko Kambayashi
, Kyoto University

Vipul Kashyap
, National Library of Medicine

Ling Liu
, Georgia Institute of Technology

Frank Manola
, The MITRE Corporation

Robert Meersman
, Vrije Universiteit Brussel (VUB)

Amit Sheth
, University of Georgia and Voquette

Munindar Singh
, North Carolina State University

George Stork
, EU

Rudi Studer
, AIFB Universität Karlsruhe

Bhavani Thuraisingham
, NSF
-
CISE
-
IIS

Michael Uschold, The Boeing Company


Medical metaphor


Ontologies: anatomy


Processes: physiology


Applications: pathology



Application

Pull …Agenda


Premises


Every

resource

meaningfully

available


Current

&

Planned

Web

Services


Beneficiaries

and

Requirements



Potential

Semantic

Services


B
2
B,

C
2
C,

Intra
-
Enterprise



Example

Semantic

Web

Services


Challenges

/

Questions

/

Concepts



What

the

Semantic

Web

Will

Look

Like


Application Pull …Scenarios


Scenarios


Tax preparation (Individual)


Supply Chain (B2B)


Scientific Research



Semantics will be added at three
different levels in successive phases


Information


Transactions


Collaborations

Application Pull …Benefits / Requirements


Lowering

barriers

to

entry


Costs


Entrants


Consumers


Service

providers


Dynamic


Ability

to

adjust

to

rapidly

changing

circumstances


Continuous


Continuous

activity

(i
.
e
.
,

taxes,

financial

activity)

monitoring


Event

Detection


Do

taxes

anytime,

anywhere


X
-
Internet


Executable


Extended


Improved


Transparency


Timeliness


Accuracy


Optimization


Eliminate

mundane

tasks


Additional

services


Reliability

and

trust


Archiving


Data


Meta
-
data


Transaction

histories

Application Pull …Challenges


Upper

ontologies


Entities



Personal


Organizations


Activities

/

Events


Processes


Ontologies


Products


Services


Financial

contracts


Business

objects


Tax

laws

(all

agencies)


Financial

activities



Service

providers


Financial

planning


Supply

chain

processes


Activities

(to

be

monitored)



Ontology

activities


Search


Select


Create,

refine


Maintain,

version


Local


Shared


Global


Mapping


Ontology
-
based

activities


Accountability


Arbitration


Trust


Tracing


Engineering


Managing

ontologies

and

mappings


Scalability,

robustness,



Ontology

Learning

Ontology Search

Maintenance

Versioning

Compare/Similarity

Deployment

(e.g., Hypothesis Generation, Query)

Merge/

Refine/Assemble

Requirements/

Analysis

Creation/

Change

Evaluation

Consistency

Checking

DB Research in the Ontology LifeCycle


Operations to compare
Models/Ontologies


Scalability/Storage Indexing of
Ontologies


DB approaches data model specific


Need to support graph based data
models


Temporal Query Languages

Lots of work in Schema Integration/translation

Ontology WG: DB Research in the Ontology
LifeCycle II


Schema Mapping


Meta Model specific


Representation of exceptions, e.g.,
tweety


Specification of Inexact Schema
Correspondences


E.g., 40% of animals are 30% of humans


Meta Model
Transformations/Mappings (e.g.,
UML to RDF Schema)


Ontology WG: DB Research in the
Ontology LifeCycle III


Ontology Versioning


Collaborative editing


Meta Model specific versioning


Version of Schema/Meta Model
Transformations


Ontology WG: DB Research &

Semantic Interoperation



Inference v/s Query Rewriting/Processing for Semantic
Integration:



E.g., RichPerson = (AND Person (> Salary 100))



Can Query Processing/Concept Rewriting provide the
same functionality as inferences ? More efficiently ?



Distributed Inferences and Loss of Information



Query Languages for combining metadata and data queries



Graph
-
based data models and query languages



Schema Correspondences/Mappings


Intensional Answers (Answers are descriptions,


e.g. (AND Person (> Salary 100)) instead of a list of all rich people)



Semantic Associations (identification of meaningful


relationships between different documents and entities)


Semantic Index

Semantic WS Scope

All

html

People

Program

Amazon

Hard code

Std

currency.com

Self
-
described

Worth pursuing

Formally self
-
described

Mike’s Humor


Services vs. Ontologies

“Well done is better than well
said.”

Ben Franklin

Research Issues


Environment



Representation



Programming


Interaction (system)


Architecture


Utilities



Scalable, openness,
autonomy,
heterogeneity, evolving


Self
-
description,
conversation, contracts,
commitments, QoS


Compose & customize,
workflow, negotiation


Trust, security,
compliance


P2P, privacy,


Discovery, binding, trust
-
service

SWS


Fitting in and expanding IS/DB/DM:

Or why Bhavani & George should care?

Data => services, similar yet more
challenging:


Modeling <functional and operational>


Organizing collections


Discovery and comparison (reputation)


Distribution and replication


Access and fuse (composition)


Fulfillment


Contracts, coordination versus transactions


Quality: more general than correctness or precision


Compliance


Dynamic, flexible information security and
trust.

Research Issues


Conversational (state
-
based, event
-
based, history
-
based)


Interoperability of conversational services


compose,
translate,


Representations for services: programmatic self
-
description


Commitments, contracts, negotiation, compliance,
cooperation


Discovery, location, binding


Transactional workflow: rollback, roll
-
forward, semantic
exception handling, recovery


Trustworthy service (discovery, provisioning,
composition, description)


Security; privacy vs. personalization


Quality
-
of
-
Service, w.r.t. various aspects, negotiable

Compilation

of

the

Amicalola

Working

Group's

collective

perception

on

the

(bidirectional)

interaction

between

the

SW

and

the

DB/IS

research








DB

/

IS

subcommu
nity


How

is

it

relevant

to

research

on

the

SW


How

may

the

SW

stimulate

research

in

this

community


DB

theory


Type

theory,

Complexity,

theory

of

concurrency


Ontology

axiomatics

and

theory
;

formal

semantics
;

semantics

for

incomplete,

inconsistent

and

evolving

representations


Data(base)

semantics


Everything
;

in

particular

ontology

language

development
;

constraints
;

data

structures


Ontology

modeling
;

formal

semantics

of

web

services


Normalization/
design


Not

specifically

as

such
;

some

work

on

Non
-
First

Normal

Form



Requirement

for

formal

properties

for

ontology

organization
;

perhaps

ontology

design

guidelines

or

“semantic

normal

forms”
;

conflict

resolution
;

redundancy

checks

in

general


Data

modeling


reuse/extend/map

DM

formalisms,

techniques

and

methods

e
.
g
.

EER,

ORM,

UML

for

ontology

(content)

specification

and

design


semantic

data

modeling
;

ontology

content

creation

techniques

and

methods
;

complex

ontological

relationships
;

domain

models


View

integration


Ontology

alignment,

translation,

object

identities,

updateable

views

;

model

mappings


see

Federated

DBs
;

ontology

support

for

view

and

application

integration
;

ontology

composition

and

update


Schema

integration


apply

to

autonomously

designed

schemas
;

global

schemas

as

pre
-
ontologies?

conflict

detection


Ontology

alignment
;

new

kinds

of

models

will

pose

new

kinds

of

problems


Deductive

DB/Datalog


Learn

from

its

failure,

query

processing

and

F
-
logic


how

to

handle

different

complexity

levels

efficiently


Multimedia

DB


Image

ontologies
;

semantic

indexing
;

similarity
-
based

search


Image
-
based

ontologies?


Temporal/Spati
al

DB


GIS

semantics

and

archiving
;

histories

data

management
;



requirement

to

model

temporal

knowledge

as

first

class

citizen

in

ontologies
;

spatial,

temporal

modeling

in

upper

ontologies
;

versioning

of

GIS

becomes

critical

issue


Document

DB


Digital

libraries,

unstructured

data
;

standards

for

digital

library

resource

descriptions

to

beused

on

the

SW


Lack

of

a

priori

global

model

presents

a

research

challenge


OO

DB


Object
-
oriented

and

object
-
based

models

for

ontologies,

extensible

databases
;

modeling

of

object

behavior
;

build

OODB

into

Java


management

of

large

collections

of

object
-
,

behavior
-

and

resource

identifiers


Visual

DB


Visualization

for

the

SW,

visual

queries
;

ontology

visualization


semantic

upgrades

of

image

databases

to

be

used

as

visual

ontologies


XML/Web

DB


Most

relevant,

caching


Size

and

semantics
;

XML

shortcomings

for

semantics

definition


Distributed

DB


everything


trust/privacy/compliance

issues

in

distributed

DBMS
;

design/dynamic

tailoring

of

DDBMS

underlying

web

services



Constraint

DB


Constraint

enforcement

as

semantics

mechanism
;

semantics
-
based

query

processing


Non
-
closed

world

assumption

issues


Transaction

modeling


loosening

of

ACID

properties


Web

services,

Extended

distributed

transaction

models
;

non
-
CWA

issues
;

smart

user

profiling


Transaction

processing


limits

of

what

can/must

be

transactional


ACID

properties

of

Web

services
;

semantic

support

for

very

long

transactions


Mobile

DB


not

directly
;

“mobile”

is

a

platform

issue


context
-
aware

computing
;

device

location
-
independent

semantics
;

mobility

issues

raised/enabled

by

the

(Semantic)

Web


Main

memory

DB


Semantic

caching


possibly

semantic

caching

i
.
e
.

using

application

semantics

or

context


Parallel

DB


unclear

at

present
;

straightforward

reuse/apply

(e
.
g
.

parallel

queries,

transactions,


)

in

certain

niches


Not

clear

at

present

Web

SoA
;

parallel

architectures

for

ontology

servers?


DB

machines





Not

clear

at

present

Web

SoA


DB

security


A

lot,

e
.
g
.
,

access

control


trust

and

privacy,

QoS
;

dynamically

changing

and

conflicting

security

requirements


Federated

DB


Autonomy
;

approaches

for

integrating

heterogeneous

data

sources,

in

particular

web

information

sources
;

mediator/

wrapper
-
based

architectures


www

=

huge

federated

DB
;

develop

more

powerful

(scalable)

approaches

for

ontology

alignment

and

integration
;

heterogeneous

sources

may

have

different

credibility
;

service

composition


Query

processing


high

applicability
;

e
.
g
.

“smart”

query

enhancement


Query

optimization


high

applicability
;

e
.
g
.

use

domain
-
knowledge

to

optimize

query

execution

and

rewriting





Information

retrieval


broad

applicability

of

techniques

and

theory
;






DB

interoperability


Everything
;

esp
.

see

federated

DBs
;

see

schema

integration


Semantic

aspects

of

interoperability
;

see

federated

DBs
;

quality

of

interoperation


DB

versioning


Link

maintenance
;

ontology

versioning


Annotations,

ontology

modeling,

versioning

of

instance

data


Metadata


Annotations,

ontology

modeling,

versioning


Mediation/Mi
ddleware


Web

services

will

benefit


P
2
P,

collaboration,

new

market

for

mediating

components


DB

warehousing


DW

architectures

for

decision

support
;

improve

e
.
g
.

web

service

efficiency
;

see

the

(S)Web

as

a

giant

DW


Smart

data

warehousing
;

share/compose

application

semantics
;

ontology

behind

“real”

data


Data(base)

mining


web

mining
;

clustering
;

learning
;

information

extraction

profiles


mining

from

text
;

exploit

semantics

in

mining
;

derive

semantics

inductively

from

query

results

on

“real”

data

including

exceptions
;

machine

learning


Database

architectures

and

DBMS


DBMS

(components)

as

web

service(s)
;

add

semantics

to

every

function/module

in

a

DBMS’s

architectures


Ontology

support

in

data

dictionaries
;

new,

more

flexible

DB

architectures

for

better

SW

support

and

processing

on

the

web


Web
-
IS

architectures


fitting

enterprise

IS

(components)

into

the

SW
;

Web

IS
;

also

see

DBMS

architectures


New

architectures

and

design

principles

for

Web

IS


Functional

modeling


design

of

web

services
;

functional

modeling

that

deals

explicitly

with

a

domain’s

semantics


Decomposition

and

composition

of

web

services
;

event

modeling


IS

in

organizations


looser

coupling

required,

provide

potential

for

organizations

to

morph

into

the

SW
;

see

also

workflow

modeling


serving

new

organizations

of

business,

community

and

government

with

emergent

SW
-
based

IS

technology


Web
-
IS

applications





smart

(ontology
-
driven)

SW

portals

and

search

engines

(“Google++”
-
type)
;

SW
-
based

“direct

marketing”
-
style

systems
;

smart

user

profiling


IS

workflow

modeling


exception

handling

in

long

(business)

transactions
;

workflows

as

“the”

paradigm

for

“programming”

the

SW


unreliability

of

components
;

unavailability

of

services


IS

methodologies


ontology

lifecycle

issues
;

as

IS

components

become

more

intelligent,

work

shifts

to

self
-
organization


New

thinking

required!

E
.
g
.

Web

IS

in

enterprises
;

how

must

business

processes

change

to

deal

with

existence

of

the

SW
;

develop/maintain

SW
-
based

systems

for

user

community

unknown

a

priori


CASE

tools


ontology

management

systems


User

interfaces


new

applications

of

design

principles

for

GUIs


New

and

complex

requirements

and

methods,

immersive

environments


DB

application

architectures





Web

application

service


AI
-
and
-
DB


knowledge

representation,

inference





Uncharted

territory

1





Sensor

input

and

stream

data

management


Uncharted

territory

2


In

general,

most

algorithms

in

DM

are

poor

when

they

are

applied

to

access,

report

etc

data

on

the

web
.

Domain

semantics

in

such

requests

need

to

be

exploited
;

however

“centralized”

solutions

(where

resources

need

to

notify

potential

requestors)

will

not

be

scalable
.