Invitational Workshop on Database and Information Systems Research For Semantic Web and Enterprises Amit Sheth

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Recap _at_ Science on the Semantic Web, Rutgers,
October 2002
Invitational Workshop on Database and
Information Systems Research For Semantic Web
and EnterprisesAmit Sheth Robert
Meersman NSF Information Data Management PIs
Workshop Amit Sheth Isabel Cruz
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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
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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, SemWeb2001 at WWW10,
  Hongkong, May 1, 2001.
• Semantic Web Rehash or Research Goldmine
  Fensel, Mylopoulous, Meersman, Sheth (Chair),
  CooPIS01
• At Castel Pergine, Italy

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Semantics IDM Brief History (partial)

• Semantic Data ModelingM. Hammer and D. McLeod
  "The Semantic Data Model A Modelling Machanism
  for Data Base Applications" Proc.. ACM SIGMOD,
  1978.
• Conceptual ModelingMichael 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

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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

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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)
• ltSWIS WG at IDM PIs meetinggt
• Review at OntoWeb3 Panel
• Final Report
• SIGMOD Record special issue December 2002
• Every thing is at lsdis.cs.uga.edu/SemNSF/

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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

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Medical metaphor

• Ontologies anatomy
• Processes physiology
• Applications pathology ?

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Application Pull Agenda

• Premises
• Every resource meaningfully available
• Current Planned Web Services
• Beneficiaries and Requirements
• Potential Semantic Services
• B2B, C2C, Intra-Enterprise
• Example Semantic Web Services
• Challenges / Questions / Concepts
• What the Semantic Web Will Look Like

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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

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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

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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,

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Ontology Search
Compare/Similarity
Merge/Refine/Assemble
Requirements/Analysis
Evaluation
MaintenanceVersioning
OntologyLearning
Creation/ Change
ConsistencyChecking
Deployment (e.g., Hypothesis Generation, Query)
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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
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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)

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Ontology WG DB Research in the Ontology
LifeCycle III

• Ontology Versioning
• Collaborative editing
• Meta Model specific versioning
• Version of Schema/Meta Model Transformations

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Ontology WG DB Research Semantic
Interoperation

• Inference v/s Query Rewriting/Processing for
  Semantic Integration
• E.g., RichPerson (AND Person (gt 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 (gt Salary 100)) instead of a
  list of all rich people)
• Semantic Associations (identification of
  meaningful relationships between different
  documents and entities)

Semantic Index
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Semantic WS Scope
Worth pursuing
Formally self-described
Std
currency.com
Self-described
Program
Hard code
Amazon
All
People
html
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Mikes Humor

• Services vs. OntologiesWell done is better than
  well said.Ben Franklin

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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

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SWS Fitting in and expanding IS/DB/DM Or why
Bhavani George should care?

• Data gt services, similar yet more challenging
• Modeling ltfunctional and operationalgt
• 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.

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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

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Compilation of the Amicalola Working Group's
collective perception on the (bidirectional)
interaction between the SW and the DB/IS research
 
 
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IDM PIs Meeting WG on SW

• Semantic Web Information Systems
• Again report available at
• http//lsdis.cs.uga.edu/SemNSF/