Semantic Web Enabled Web Services: State-of-Art and Industrial Challenges Vagan Terziyan, Oleksandr Kononenko “Industrial Ontologies” Group Industrial Ontologies Group Int. Conference on Web Services Europe (ICWS-Europe’03), Erfurt, Germany, Sept , 2003 These slides are available from:

2 Industrial Ontologies Group  We develop Semantic Web solutions for industry –Our research contacts with: Metso, Tietoenator, Sonera, Nokia, …  Location: –University of Jyväskylä, Finland; –National University of Radioelectronics, Ukraine  Developed Concepts: –OntoServ.Net: Semantic Web-based large-scale automated industrial service integration framework for asset management (case of smart-devices maintenance is under development) –GUN (Global Understanding eNvironment): Approach for resource integration built using combination of Semantic Web, Web Services and Agent technologies –OntoShell: Agent-based representative of informational entities in semantic-enabled environment (OntoServ.Net) –OntoAdapter: Connector-software that adapts native service interface to OntoServ.Net find more details at

3 Web Services and Semantics  SWWS – Web Services with semantics represented explicitly via ontology-based descriptions  Intelligent agents will use semantic web services, discovering composing them accordingly their goals –New types of service consumers: user agents, devices, AI agents –Intelligent dynamic service integration  Reasoning about service capabilities requires more advanced service description framework than existing technology has The promise is that Web Service Technology in conjunction with Semantic Web Technology (“Semantic Web Services”) will make service integration dynamically possible for all types and sizes of environments compared to the “traditional” technologies

4 Semantic Web support for IT “The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications” The Semantic Web is an initiative with the goal of extending the current Web and facilitating Web automation, universally accessible web resources, and the 'Web of Trust', providing a universally accessible platform that allows data to be shared and processed by automated tools as well as by people.

5 SWWS main challenges (Dieter Fensel)  Service description  Service discovery  Service mediation Web Technology HTTP, URI Web Services UDDI, WSDL, SOAP Semantic Web XML, RDF(S), OWL Intelligent Web Services Interoperability, knowledge management E-commerce, EAI Human-oriented data Machine-processable data “Next-generation Web” Dynamic Static DimensionExistingSWWS ServicesSimpleComposable RequestorHuman (developer)Agent ProviderRegistrationNo registration MediatorKey PlayerFacilitator DescriptionTaxonomyOntology DescriptivenessClosed worldOpen world RepresentationSyntax-basedSemantics-based

6 UDDI needs semantics!  Service semantics  Service interface semantics  Message semantics  and more..  Service model  Constraints  Composition rules Domain ontologies E-Market E A I Business Processes Industrial Services DAML-S DAML-S is an upper-ontology for service description. Development of common domain ontologies will provide basis for semantic-enabled service descriptions. Will be there a Semantic UDDI?

7 Service description  Description models in: WSDL, UDDI, E-Speak, ebXML, RosettaNet BPEL4WS, WSCI, BPML  Existing technology: –No explicitly defined semantics in service descriptions –None of existing model proposes something more than basic ontology definition –Keyword-based search is not enough  Semantic Web for Web Services: –Standards via ontological definition –DAML-S (upper ontology of services) Reuses WSDL adding semantics-binding elements Provides ontological description of service model Adds semantic-bindings to service profile

8 Service Flow Orchestration, Choreography, Composition Where are changes?  BPEL4WS, WSCI, BPML Discovery Description Messaging Networking Semantic match Collaborative service-agents Ontology-based standards  UDDI  SOAP and extensions:  Transactions  Security  Routing, etc.  WSDL  HTTP, FTP, , etc.  RDF Messaging  DAML-S:  Service Model  Service Profile  Service Grounding (WSDL) Ontologies instead of language standards Agents can ‘understand’ what, when and how to use services Common and domain ontologies Still to be implemented

9  DAML-S is ontology for service description based on Resource Description Framework and DAML+OIL ontology language  DAML-S –ServiceProfile properties for automatic discovery (offered functionality, preconditions, inputs, outputs and effects of service invocation) –ServiceModel process model for automated integration and invocation –ServiceGrounding communication-level details (WSDL) Descriptiveness of DAML-S RequirementSatisfied by Flexibility and expressiveness RDF Semi-structured data support Categorization capabilities RDFS Ability to express constraints DAML+OIL

10 Smart-devices are becoming users of provided maintenance services. Maintenance Service Network Agents acting as service components in the Maintenance Service Network have ability to learn during work improving services’ performance. OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, March 2003,

11 Semantic-enabled web services in OntoServ.Net  We add semantic-enabled descriptions of services to facilitate: –automated discovery and use of services by smart-devices; –automated integration of services; –communication between heterogeneous services. Maintenance Platform Set of “service components” Service Platform Set of “service components”

12Interoperability Ontology A: DocumentsOntology B: Research A commitment to a common ontology is a guarantee of a consistency and thus possibility of data (and knowledge) sharing Common (shared) ontology Ontology C: Services System 1 System 2 \\AgServ\vagan\InBCT_1.doc V. Terziyan A:Report A:Location 3.1: analysis A:Subject A:Author Instance-of Semantic Web A:name

13 Service discovery  DAML-S service profiles  Supporting domain ontologies  Semantic match procedure Requested Service profile: - service of class “Text Search Service” (subclass-of “Search Service”) - “Data Source” is “PDF File” Input data: 1: “URL Location” of “Data Source” 2: “Search String” Required result: 1: “Occurrence position” of “Search String” Service profile Class: “PDF Search Service” (subclass-of “Text Search Service”) Input: 1: “Search String” 2: “Case-sensitive Flag” 3: “URL Location” Output: 1: “Page Number” 2: “Occurrence position” Semantic match Ontology of service description Common Ontologies Data Source Search String URL String … … … … …

14 Service discovery in OntoServ.Net  Peer-to-peer network of platforms for maintenance services  No centralized service registration  Peer-to-peer semantic search based on service profiles  Profile includes history of “service use efficiency” Maintenance Centers and Maintenance Platforms

15 Services as Agents  Service (an agent) is a self-interested, autonomous, active, mobile(!!!) entity  If Service = Agent, than Service is mobile –Mobile code is carried by agent –Agent-shell for web services –Agent-shell as service adapter Main strategies: Service composition via collaboration between agent- services Service composition by “Service Manager” accordingly to specific goals of service platform Mobility factors: Security Bandwidth Time and other constraints

16 Semantic adapters OntoServ.Net resources: –Services –Device –Human –Data repository + Semantic adapter = OntoServ.Net Service Semantic-based communication via standard protocols (semantic queries, ontologically described data) OntoServ.Net service Specific communication methods Application Resource OntoServ.Net service Semantic adapter Human, smart-device, application, service, algorithm… Service profile and configuration  In OntoServ.Net common “language” is used between adapters allows mapping into and from internal service-specific protocols.  Implementation of generic semantic-adapter software is non-trivial task, but adapters for restricted class of services (for device data access, data retrieval from DB, alarm system notifications) are less challenging.

17 Service composition in OntoServ.Net  System is composed accordingly to Task Ontology  New services are requested from the network when needed  Service’s diagnostic “experience” is concerned Platform Maintenance Manager Service Diagnostic Services Smart-devices Task Ontology

18 Ontology support for OntoServ.Net  Common intermediate language: Unambiguous agreed vocabulary and semantics - Service taxonomy - Maintenance domain Data access Data access services Diagnostics Diagnostic services Maintenance activities User Agents  Sharable diagnostic knowledge representation  System configuration  Semantic adapter configuration

19 OntoServ.Net Challenges smart industrial devices  New group of Web service users – smart industrial devices.  Semantic Web enabled services  Internalexternalservice platforms  Internal (embedded) and external (Web-based) service platforms. Mobile Service Component  “Mobile Service Component” concept supposes that any service component can move, be executed and learn at any platform from the Service Network, including service requestor side.  Semantic Peer-to-Peer  Semantic Peer-to-Peer concept for service network management assumes ontology-based decentralized service network management.

20Conclusion Traditional web service technology is able to address some of problems today. However, in combination with Semantic Web Web Services have the potential to address these needs much better Semantic WebcontextSemantic Web it is a new context within which one should rethink and re-interpret his existing businesses, resources, services, technologies, processes, environments, products etc. to raise them to totally new level of performance… The OntoServ.Net concept of Distributed Maintenance Network can be a good pilot case to implement the benefits of Semantic Web and Web Services integrated framework Contact: Vagan Terziyan

21 Acknowledgements Agora Center (University of Jyvaskyla): Agora Center includes a network of good-quality research groups from various disciplines. These groups have numerous international contacts in their own research fields. Agora Center also coordinates and administrates research and development projects that are done in cooperation with different units of university, business life, public sector and other actors. The mutual vision is to develop future's knowledge society from the human point of view. InBCT Project ( ): Innovations in Business, Communication and Technology