1 / 17

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

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 Shetth & Isabel Cruz. Recap @ Ontoweb3.

palani
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Invitational Workshop on Database and Information Systems Research For Semantic Web and EnterprisesAmit Sheth & Robert Meersman NSF Information & Data Management PI’s Workshop Amit Shetth & Isabel Cruz Recap @ Ontoweb3

  2. “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

  3. Challenges SCALE and PERFORMANCE Acceptable response time when you have millions and billions of instances • locking for sharing/storage management • Semantic similarity, mappings, interoperability (schema transformation/integration) • indexing for queries (semistructured and structured data) • workflow for WS process

  4. History (partial, DB/IS centric) • 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. • So Far (Schematically) yet So Near (Semantically) • Data Semantic: What, Where and How?Meersman, Navathe, Rosenthal, Sheth, • Semantic Interoperability on Webmany projects in 90s • 1993 CIKM paper on multiple preexisiting ontologies • Domain Modeling, Metadata, Context, Ontologies, Semantic Information Brokering, Agents, Spatio-temporal-geographic- image-video-multimodal semantics • Most of the above before “Semantic Web” term is coined

  5. Context for Amicalola workshop • Series of Workshops and upcoming conferences: Lisbon (9/00), Hong Kong (5/01), Palo Alto (7/01), Amsterdam (12/01); upcoming: 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, CooPIS’01] • At Castel Pergine, Italy

  6. Organization • 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) • Upcoming -- report and special issue • lsdis.cs.uga.edu/SemNSF/

  7. 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

  8. Medical metaphor • Ontologies: anatomy • Processes: physiology • Applications: pathology 

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

  10. 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

  11. 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)

  12. Ontology WG: DB Research in the Ontology LifeCycle III • Ontology Versioning • Collaborative editing • Meta Model specific versioning • Version of Schema/Meta Model Transformations

  13. 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 types of instances) Semantic Index

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

  15. Mike’s Humor • Services vs. Ontologies“Well done is better than well said.”Ben Franklin

  16. SWS– Fitting in and expanding IS/DB/DM: Or why Bhavani & George should care? Data => services, similar yet more challenging: • Modeling • 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.

  17. Research Issues • Conversational (state-based, event-based, history-based) • Interoperability of conversational services – compose, translate, • Representations for services: programmatic self-description • Commitments, contracts, negotiation • Discovery, location, binding • Compliance • Cooperation • 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

More Related