1. 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

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 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. So Far (Schematically) yet So Near (Semantically) Data Semantic: What, Where and How? Meersman, Navathe, Rosenthal, Sheth, Semantic Interoperability on Web many 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 AbererKarl Aberer, LSIR, EPFL, Switzerland Mike BrodieMike Brodie, Verizon Isabel CruzIsabel Cruz, The University of Illinois at Chicago Umeshwar DayalUmeshwar Dayal, Hewlett-Packard Labs Stefan DeckerStefan Decker, Stanford University Max EgenhoferMax Egenhofer, University of Maine Dieter FenselDieter Fensel, Vrije Universiteit Amsterdam William GroskyWilliam Grosky,University of Michigan-Dearborn Michael HuhnsMichael Huhns, University of South Carolina Ramesh JainRamesh Jain, UC-San Diego, and Praja Yahiko KambayashiYahiko Kambayashi, Kyoto University Vipul KashyapVipul Kashyap, National Library of Medicine Ling LiuLing Liu, Georgia Institute of Technology Frank ManolaFrank Manola, The MITRE Corporation Robert MeersmanRobert Meersman, Vrije Universiteit Brussel (VUB) Amit ShethAmit Sheth, University of Georgia and Voquette Munindar SinghMunindar Singh, North Carolina State University George StorkGeorge Stork, EU Rudi StuderRudi Studer, AIFB Universität Karlsruhe Bhavani ThuraisinghamBhavani Thuraisingham, NSF-CISE-IIS Michael Uschold, The Boeing Company

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

9. 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

10. 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

11. 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

12. 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,

13. Ontology Learning Ontology Search Maintenance Versioning Compare/Similarity Deployment (e.g., Hypothesis Generation, Query) Merge/ Refine/Assemble Requirements/ Analysis Creation/ Change Evaluation Consistency Checking

14. 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

15. 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)

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

17. 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

18. Semantic WS Scope All html People Program Amazon Hard code Std currency.comSelf-described Worth pursuing Formally self-described

19. 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

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

21. 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.

22. 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

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

24. Questions: 1 A. What are necessary elements and principles for a WS modeling and development framework? B. How can a composed service be described vis à vis constituent services? –On the fly –Composable descriptions –Decomposable descriptions –Metadata attributes, i.e., functional, execution, QoS, security C. How can service descriptions be organized and disseminated? –Taking metadata attributes into account –Architectures, e.g., self-organizing

25. Questions: 2 D. How can abstractions for WS composition (e.g., contracts, coordination) be described and applied? –Monitor fulfillment –Check compliance –Assign credit and blame E. How can a collection of services interact (collaborate/compete) to solve the needs of a service requestor? –Negotiate among providers and requestors F. How can trust be supported in the discovery, selection, composition, and fulfillment of Web services? –Architectures without trusted third parties G. How can process and data models interoperate? –Automation –Discovery