1. Partner and Service Discovery for Collaboration Establishment with Semantic Web Services Michael Stollberg, Uwe Keller, Dieter Fensel DERI – Digital Enterprise Research Institute 3rd International Conference on Web Services (ICWS 2005) Orlando, Florida, July 12th

2. 2 Content 1.Automated Collaboration on the Semantic Web 2.Semantically Enabled Discovery 3.Partner and Service Discoverer Architecture 4.Findings 5.Conclusions

3. 3 Motivating Example LMPD Agent A(P) Goal: plan[ - med. treatment for M - chauffeur(P) - consistent with calendar(P)] Plan: - check plan from A(L) - if OK, then book else: revise plan Agent A(L) Goal: plan[ - med. treatment for M - chauffeur(P)] Plan: 1) find suitable doctor 2) coordinate chauffeur(P) med. info service calendar service transport info serv. owns O-personO-medicalO-date&timeO-transport Agent A(D) Goal: offer[ - med. treatment - appointement] Plan: 1) find customer 2) book appointement owns book app. service doctor information (website) ontology usage (also for agents, omitted here) 1:need medical treatment 2: inform, chaffeur(P) 3a: assign goal 3b: make / find plan 3b: make / find plan 3a: assign goal Xb: make / find plan Xa: assign goal 4:find doctor uses provides 5:inform, plan 6:revise plan 7:book appointment interact uses 8: add plan

4. 4 Conceptual Architecture Agent B Goal Instance Agent A Goal Instance has(1,n) compatible goals = collaboration partners automated collaboration execution Domain Knowledge uses(1,n) has(1,n) uses(1,n) Collaboration Service Web Service Goal Template Repository Service Repository Collaboration Service Web Service Owner goal assignment service discovery owns Ontology

5. 5 Agent Semantic Description Agents electronic representative of real world entity that wants to achieve an objective by automated collaboration Class agent hasNonFunctionalProperties type nonFunctionalProperties importsOntology type ontology usesMediator type ooMediator owner type owner collaboration type collaboration history type collaboration Class owner hasNonFunctionalProperties type nonFunctionalProperties owner type instance preference type axiom policy type axiom serviceUsagePermission type service Class collaboration hasNonFunctionalProperties type nonFunctionalProperties goal type goal single-valued service type service

6. 6 Goals Semantic Description Goal Template predefined schema of user objective, described as WSMO 1.0 goal Class goalTemplate is-a wsmov1.0Goal hasNonFunctionalProperties type nonFunctionalProperties importsOntology type ontology usesMediator type {ooMediator, ggMediator} hasPostcondition type axiom hasEffect type axiom Goal Instance concrete objective assigned to an agent, instantiated Goal Template, client for service usage Class goalInstance sub-Instance goalTemplate hasNonFunctionalProperties type nonFunctionalProperties importsOntology type ontology usesMediator type ooMediator hasPostcondition type axiom hasEffect type axiom hasSubmission type instance hasResult type instance goalResolutionStatus type goalResolutionStatus

7. 7 Collaboration Service Description Collaboration Services facility an agent uses for participating in an automatically executed collaboration interacts with other agents and utilizes Semantic Web resources via its orchestration Class collaborationService is-a wsmoService hasNonFunctionalProperties type nonFunctionalProperties importsOntology type ontology usesMediator type ooMediator hasCapability type capability single-valued hasSharedVariables type sharedVariable hasPrecondition type axiom hasAssumption type axiom hasPostcondition type axiom hasEffect type axiom hasInterface type interface clientInterface type serviceInterfaceDescription orchestration type serviceInterfaceDescription Class serviceInterfaceDescription sub-Class wsmoServiceInterface hasNonFunctionalProperties type nonFunctionalProperties importsOntology type ontology usesMediator type ooMediator hasVocabulary type concept_in, concept_out, concept_controlled hasState type ontology hasGuardedTransition type if (condition) then update – for client interface if (condition) then operation(CS, R, update) – for orchestration

8. 8 Collaboration Management Partner Discoverer Service Discoverer Orchestration Validator GI {GI} GI {S} boolean {(CS,R)} Collaboration (preliminary) (A 1 (G 1, {CS} 1 ), A 2 (G 2, {CS} 2 ),..) Goal Instance Agent (A 1 (G 1, CS 1,{R} CS1 ), A 2 (G 2, CS 2,{R} CS2 ),..) Goal Instance … separately for each GI each possible service combination Agent Collaboration (final) Collaboration Executor each collaborationgoal resolution for the goal of each collaboration partner goal resolution for the goal of each collaboration partner

9. 9 Semantically Driven Discovery find appropriate facility (Web) Service for automatically resolving a goal as the objective of a requester Key Word Matching match natural language key words in resource descriptions Controlled Vocabulary ontology-based key word matching Semantic Matchmaking … what Semantic Web Services aim at Ease of provisionPossible Accuracy

10. 10 Action and Object Knowledge Knowledge Types Distinction -Action = activity to be performed on object (e.g. buy, sell,..) -Object = entity that action is performed on (e.g. product, ticket, …) Action and Object Knowledge is different -“Action” defines what is to be done; interacting entities need to have compatible actions (e.g. buy sell) => Action-Resource Ontology -“Object” defines whereon an action is to be performed; interacting entities need to have not-contradicting object definitions => Set-theoretic Object Matchmaking Combination is needed (agents / resources might have not-contradicting objects but incompatible actions)

11. 11 Action-Resource Ontology concept action compatibleAction symmetric ofType action concept buy subConceptOf action compatibleAction symmetric ofType sell concept sell subConceptOf action compatibleAction symmetric ofType buy concept resource hasAction ofType set action concept goal subConceptOf resource concept service subConceptOf resource concept buyergoal subConceptOf goal hasAction ofType set buy concept sellerservice subConceptOf service hasAction ofType set sell Action Taxonomy Resource Taxonomy all resources are defined as instances of resource types allows determining action compatibility / equality of agents & resources supports handling multi-party collaborations

12. 12 Object Matchmaking Set-Based Resource Descriptions Information Space all possible instances of used ontologies Description Notion all possible instances that satisfy restricted information space postcondition definedBy exists ?PurchaseItem(?PurchaseItem[ item hasValue ?PurchaseFurniture ] memberOf swfmo:product) and exists ?PurchaseFurniture(?PurchaseFurniture[ material hasValues {wood}, ] memberOf furn:chair) and ?X[ purchaseItem hasValue ?PurchaseItem, buyer hasValue kb:MichaelStollberg, purchasePayment hasValue kb:MSCreditCard ] memberOf swfmo:purchaseContract. Goal Instance Postcondition - Objective: receive a purchase contract for a wooden chair for Michael Stollberg, payment with credit card - meta-varibale X (dynamically quantified by matchmaking notion) - restrictions on several ontology notions - WSML syntax

13. 13 Object Matchmaking Set Theoretic Matchmaking Notions 1.Exact Match: D Q, D R, O, M ╞ x. (D Q D R ) 2.PlugIn Match: D Q, D R, O, M ╞ x. (D Q => D R ) 3.Subsumption Match: D Q, D R, O, M ╞ x. (D Q <= D R ) 4.Intersection Match: D Q, D R, O, M ╞ x. (D Q  D R ) 5.Non Match: D Q, D R, O, M ╞ ¬x. (D Q  D R ) = D Q = D R X

14. 14 Realization in VAMPIRE Theorem Prover -developed at University of Manchester -winner of several CASC competitions works with TPTP -FOL language, “standardized” -Transformation WSML -> TPTP without loss of information & “easy”

15. 15 Structure of Proof Obligation 1.Needed Knowledge Resources -include Ontology Theory and Universe -Knowledge Base optionally 2.Resource Description notion to be matched -Request Resource & Result Resource -Only those notions that are to be matched 3.Object Matchmaking notion -Include as specified in the Discovery Request => easy to generate dynamically

16. 16 Knowledge Representation 1.Ontology Theory -basic ontological relations (transitive subsumption, etc.) -WSMO ontology meta model (when working on WSMO) 2.Universe all domain ontology knowledge needed -Ontology schemas (allows to work on resource description) -Generic Instances 3.Knowledge Base -pre-defined instances -optional (but very useful) 4.Resource Description -WSMO resource description notions (postcondition, effects, etc) -only those notions which are needed for object matchmaking

17. 17 Generic Instance in Universe Generic Instance definition: -existentially quantified object -complete fact with universally quantified variables as attribute values forAll ?A, ?B, ?C( exists ?X(?X instanceOf concept[ attributeA hasValue ?A, attributeB hasValue ?B, attributeC hasValue ?C ] ) ).

18. 18 Generic Instances – Why? support for Intersection match: -Intersection Matching Notion searches for an existing object wherefore the request and result predicate do not contradict -If there are Generic Instances for all concepts of all used domain ontologies, then the existence of an instance of every possible ontology object can be proved by constructing a hypothetical graph wherein for each node there exists a generic instance. Generic Instance of Ontology Concept elementary datatype type of attribute value not new – same technique in other approaches for realization of intersection match

19. 19 VAMPIRE (+) and (–) Pro: -Not bound to limited reasoning facilities supported by existing reasoners => we can create object matchmaking as -Ontology Theory + Universe as the only pre-defined knowledge resources needed => efficient & easy to handle at runtime Con: -does not have any built-in functions ono support for basic datatype processing ono arithmetics => everything has to be provided as explicit FOL theories

20. 20 Partner & Service Discovery Components run independently, invoked by agents apply ARO & OMM as defined above Design Principles: 1.Common Discoverer Architecture 2.Modularized Functionality 3.Layered Architecture (stepwise narrow search space) 4.Reduction of expensive operations (efficiency)

21. 21 Partner Discoverer Architecture GI i Action-Resource Ontology DiscoveryResult sets of compatible Goal Instances (2) GG Matcher Discovery Request initiating Goal Instance GT i (1) Cooperation Knowledge Filter GT g GI g instanceOf instanceOf, status = ‘open’ Action Compatibility

22. 22 Service Discoverer Architecture Discovery Request Goal Instance Discovery Result usable Services Service Repository Discovery Result (intermediary) Service Filter (2) GIS Matcher GI i GT i instanceOf (1) Pre-Selector Action Equality

23. 23 Findings Functional Correctness approved in use cases –Distinction Action – Object Knowledge needed –Performance (Vampire): –average 1-2 sec (domain dependent !!) –correctness & scalability more important than celerity Goal and Service descriptions are mostly constrained object definitions without complex logical expressions => object matchmaking rather logical reasoning Industrial applicability: –“hide logics from users and system developers” –semantic descriptions by Knowledge Engineers –exhaustive tool support required for users and system developers –abandon logical expressivity if it hampers automation

24. 24 Conclusions Framework for Collaboration on the Semantic Web –agents, goals, Web Services –clear separation of partner & service discovery Realization of Semantic Discovery –distinction Action-Object Knowledge –semantic discovery techniques oAction-Resource Ontology (action compatibility / equality) oSet-based Object Matchmaking (not-contradicting objects) –discoverer architectures (modularized, layered, efficient) based on Web Service Modeling Ontology WSMO –semantic element descriptions –set-based object matchmaking