Recap @ Science on the Semantic Web, Rutgers, October 2002

1. Recap @ 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 PI’s Workshop Amit Sheth & Isabel Cruz

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. 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

4. 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 Webmany 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

5. 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

6. 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/

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

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 documents and entities) Semantic Index

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

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

20. 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

21. 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.

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, 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

23. Compilation of the Amicalola Working Group's collective perception on the (bidirectional) interaction between the SW and the DB/IS research DB / IS subcommunity 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

24. 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/Spatial 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

25. 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

26. 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;

27. 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/Middleware Web services will benefit P2P, 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

28. 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

29. 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.