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WSMO Discovery Realization in Semantic Web Fred. Michael Stollberg - 03 November 2004 -. Content. Starting Position General Architecture of a Discoverer Knowlegde Types & Discovery Techniques Action Knowlegde Discovery Object Knowledge Discovery Realization in VAMPIRE.

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wsmo discovery realization in semantic web fred

WSMO Discovery Realization in Semantic Web Fred

Michael Stollberg

- 03 November 2004 -

content
Content
  • Starting Position
  • General Architecture of a Discoverer
  • Knowlegde Types & Discovery Techniques
    • Action Knowlegde Discovery
    • Object Knowledge Discovery
  • Realization in VAMPIRE

Semantic Web Fred4-Sep-14

starting position
Starting Position
  • WSMO Discovery Framework (WSMO D5.1)
    • levels / types of discovery mechanisms
    • matching notions definition
  • SWF Framework
    • framework for agent cooperation with usage of WSMO technologies
    • requirements for WSMO Discovery Realization
  • preliminary trial & error approaches in WSMO

Semantic Web Fred4-Sep-14

discoverer architecture
Discoverer Architecture

initial architecture for

Web Service Discovery

DISCOVERER

Web Service

GOAL

  • WSMO Goal (WSMO 1.0)
  • Requested Service (WSMO 1.1)

A (Web) Service that can satisfy the Goal

Semantic Web Fred4-Sep-14

generalized architecture for discoverer can be used for discovering any wsmo top level notion
Generalized Architecture for Discoverercan be used for discovering any WSMO top level notion

DISCOVERER

Action Kn Filter

Discovery

Request

Discovery

Result

Object Matchmaker

  • can be aligned with any WSMO top level notion
  • has „more detailed“ information for discovery
    • Action Knowledge
    • Object Knowledge
    • Matchmaking Notion
  • can be aligned with any WSMO top level notion
  • “matching” according to:
    • Action Compatibility
    • Object Matching wrt to Matchmaking Notion

Semantic Web Fred4-Sep-14

action vs object
Action –vs – Object
  • 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, …)
    • Relation: Action(Object)
  • Action and Object Knowledge is different
    • “Action” defines what is to be done; interacting entities need to have compatible actions (e.g. buy <-> sell)
    • “Object” defines whereon a action is to be performed; interacting entities need to have not-contradicting objects
  • Object Matching is not enough, because 2 resources might have not-contradicting objects but not-compatible actions

Semantic Web Fred4-Sep-14

action vs object1
Action –vs – Object
  • different Discovery Technologies needed
  • Action Knowledge Discovery
    • aim: determine Compatibility of Actions in Resources
    • approach: Action Knowledge Ontologies + Filter
  • Object Knowledge Discovery
    • aim: determine Not-Contraction of Objects in Resources
    • approach: Semantic Resource Description + Matchmaking

In current WSMO Ontologies & resource descriptions Action & Object Knowledge is implicit; it has to be provided explicitly to Discoverer

Semantic Web Fred4-Sep-14

action knowledge ontology
Action Knowledge Ontology

concept action

compatibleAction ofType set action

concept buy subConceptOf action

compatibleAction ofType set sell

concept sell subConceptOf action

compatibleAction ofType set 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

instance <<http://.../goal1>> memberOf buyergoal

instance <<http://.../ws7>> memberOf sellerservice

Action

Taxonomy

Resource

Taxonomy

ALL resources of application need to be instantiated

Semantic Web Fred4-Sep-14

object matchmaking
Object Matchmaking
  • Based on WSMO D5.1 “WSMO Discovery”
    • defines different approaches
    • defines matching notions
  • further insights on Object Matchmaking
    • applicability of matching notions
    • why matching notion needs to be specified in Discovery Request
  • Realization with Vampire

Semantic Web Fred4-Sep-14

exact match

Legend:

information space of the request resource, i.e. set of all possible instances that would satisfy the description notion

information space of the result resource, i.e. set of all possible instances that would satisfy the description notion

Exact Match

exactMatch(request, result) impliedBy

forAll X ( request(X) equivalent result(X))

Usage:

  • e.g. effect matching
    • all effects of request resource shall be satisfied
    • no more effects by result resource

Semantic Web Fred4-Sep-14

plugin match

Legend:

information space of the request resource, i.e. set of all possible instances that would satisfy the description notion

information space of the result resource, i.e. set of all possible instances that would satisfy the description notion

PlugIn Match

pluginMatch(request, result) impliedBy

forAll X ( request(X) implies result(X))

Usage:

When all instances provided by result resource shall satisfy request

  • e.g. “give product information wherefore holds …”
  • e.g. “find all restaurants wherefore holds …”

Semantic Web Fred4-Sep-14

subsumption match
Subsumption Match

subsumptionMatch(request, result) impliedBy

forAll X ( request(X) impliedBy result(X))

Usage:

When several result resources are needed to satisfy the request

  • not used in SWF
  • applicable for discovering result resources that have to be composed to satisfy request

Semantic Web Fred4-Sep-14

intersection match
Intersection Match

intersectionMatch(request, result) impliedBy

exists X ( request(X) and result(X))

Usage:

When there shall be 1 object retrieved that satisfies the request

  • e.g. receive 1 purchase contract

Semantic Web Fred4-Sep-14

realization in vampire
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”

Semantic Web Fred4-Sep-14

knowledge representation
Knowledge Representation
  • Ontology Theory
    • basic ontological relations (transitive subsumption, etc.)
    • WSMO ontology meta model (when working on WSMO)
  • Universe

all domain ontology knowledge needed

    • Ontology schemas (allows to work on resource description)
    • Generic Instances
  • Knowledge Base
    • pre-defined instances
    • optional (but very useful)
  • Resource Description
    • WSMO resource description notions (postcondition, effects, etc)
    • only those notions which are needed for object matchmaking

Semantic Web Fred4-Sep-14

generic instance in universe
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

]

) ) .

Semantic Web Fred4-Sep-14

generic instances why

Generic Instance of

Ontology Concept

elementary datatype

type of attribute value

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.

not new – same technique in other approaches for realization of intersection match

Semantic Web Fred4-Sep-14

structure of proof obligation
Structure of Proof Obligation
  • Needed Knowledge Resources
    • include Ontology Theory and Universe
    • Knowledge Base optionally
  • Resource Description notion to be matched
    • Request Resource & Result Resource
    • Only those notions that are to be matched
  • Object Matchmaking notion
    • Include as specified in the Discovery Request

=> easy to generate dynamically

Semantic Web Fred4-Sep-14

vampire and
VAMPIRE (+) and (–)
  • Pro:
    • works on structural level, i.e. no need for intermediate insertion of hypothetical facts in system’s knowledge base
    • Ontology Theory + Universe as the only pre-defined knowledge resources needed
    • stable & quite fast compared to other CASC theorem provers
  • Con:
    • does not have any built-in functions =>
      • no support for basic datatype processing
      • no arithmetics
    • everything has to be provided as explicit FOL theories

Semantic Web Fred4-Sep-14

slide20
</ WSMO Discovery Realization – Theoretic Part>

usage in SWF + Prototype upcoming …

Semantic Web Fred4-Sep-14