Microsoft PowerPoint - RuleResponder-NCCU-Taipei-Mar2008-talk [Compatibility Mode]

Rule Responder:Rule Responder:RuleML-BasedSemantic Web Ser icesSemantic Web ServicesTalk at Dept. of Computer Science, NCCUTaipei, Taiwan, 7 March 2008

Harold Boley1, Benjamin Craig1, Adrian Paschke2y , j g ,1 National Research Council of Canada

University of New Brunswick, Canada2 Bioinformatics, BIOTEC Center,, ,Technical University Dresden, Germany

Introductiont oduct o

Topic of this talk Rule Responder: Extending the Semantic Web

towards a Semantic-Pragmatic Web Service infrastructure for distributed rule-basedinfrastructure for distributed rule-basedhuman-computer collaboration

Rules employed for semi-automated decision support delegation/coordination negotiation flow

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reaction logic

Towards a Pragmatic Webo a ds a ag at c eb1. Explicit Meta-data

vCard, PICS, Dublin C RDF IEEE LOMCore, RDF, IEEE LOM

(Learning Objects Metadata), Micro Formats, FOAF, SIOC

2. Ontologies RDFS, OWL Lite|DL|Full

3. Logic and Inference e.g. Logic Programming

Rule/Inference Engines4. Software Agents and

Web ServicesWeb Services FIPA, Semantic Web

Services, RBSLA,

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Pragmatic Web with Two Kinds of Collaborative Agents

Interaction

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Pragmatic Web Semiotics, the study of sign processes, includes the study of how signs are

constructed, understood, disseminated, and acted upon. Pragmatics, a basic field of linguistics today, originally had its roots in Morris's idea of

a division of signs concerned with "the relations of signs to their interpreters" ora division of signs concerned with the relations of signs to their interpreters or users.

Eugen Halton, http://www.nd.edu/~ehalton/Morrisbio.htm, 1992

Pragmatic Web Utilize the heterogenous Semantic Web resources meta data and Utilize the heterogenous Semantic Web resources, meta data and

meaning representations with intelligent agents and web-based services with the ability to understand the others intended meaning (pragmatic

competence)p ) Collaborate in a communicative conversation-based process where

content and context is interchanged in terms of messages (relation of signs) between senders and receivers (interpreters/users).

Pragmatic layer/wrapper around semantic/content e g by KQML / ACL Pragmatic layer/wrapper around semantic/content e.g. by KQML- / ACL like speech-act primitives (e.g. assert(content), retract(content), query(kb),

known under the technical term performatives) Model, negotiate and control individual and shared meanings

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learning and knowledge adaption / updates

Pragmatic Agent WebAgent Web

R l R d P j t

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Rule Responder Project:http://responder.ruleml.org

Benefits of Rule-Based Decision and Reaction Logic

1. Compact declarative representation of rulesp p Clear semantics Global rules which might apply in several contexts (reusability) Separation of contract rules from the application codep pp Extensibility of the rule base (without changing the interpreter)

2. Efficient, generic interpreters (rule engines) for automated rule chaining and execution of reaction rules

3. Automated conflict detection of rule conflicts Traceable and verifiable results Integrity constraints are possible Integrity constraints are possible Automated conflict resolution by rule prioritization

Rules play an important role to automatically transform contextual data,

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derive new conclusions and decisions from existing knowledge and behaviourally act according to changed conditions or occurred events

Rule Responder Architecture (MDA)Rule Responder Architecture (MDA)1. Computational independent model (CIM) with rules,

ti l fl ( i t l i lprocesses, conversational flows (e.g. in a natural or visual language)

2. Platform independent model (PIM) which represents the rules, events and ontologies in a common (standardized)

interchange format (e g a markup language)interchange format (e.g. a markup language)

3. Platform specific model (PSM) which encodes the rule statements in the language of a specific execution environment (e.g. a rule engine / inference service or compiled code)

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compiled code)

RuleML / Reaction RuleMLRuleML / Reaction RuleMLPlatform Independent

Rule Interchange FormatRule Interchange Format

http://ruleml orghttp://ruleml.org

http://ibis.in.tum.de/research/ReactionRuleML/

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RuleML: Common Rule Interchange Format Rule Markup and Modeling Initiative (RuleML) (www.ruleml.org)

Standardization effort for a rule markup and modelling language, tools and applications

RuleML is the de facto open language standard for rule interchange /rule markup on the Web

Reaction RuleML (http://ibis.in.tum.de/research/ReactionRuleML/) Language family for reaction rules and complex event messaging / processing

RuleML

Derivation Rules

Reaction Rules

Integrity Constraints

Transformation Rules

Reaction RuleML

RuleML TranslatorsHomogeneous Approach

9Derivation RuleML

Integrity RuleML

RuleML Enables ...u e ab es

modellingmodellingmarkup

t l tiUML

RDFRule

translationinterchange in

RDFXMLRule interchange

executionbli ti

in XMLASCII

publicationarchiving

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g

RuleML Language Family Derivation RuleML

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Schema Modularization XML Schema + EBNF Syntax Full RDF compatibility via type and

role tags (akin to triple syntax); XML Schema Modularization:

Layered and uniform design The layers are organized around

increasing expressiveness levelsB fi Benefits:

- easier to learn the language and to understand their relationships

- facilitates reusability and complex language assemblies from moduleslanguage assemblies from modules

- provides certain guidance to vendors who might be interested only in a particular subset of the features

i t i t i d- easier to maintain, manage and extend in a distributed environment

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RuleML Business Rule Example''The discount for a customer buying a product is 5.0 percent if

the customer is premium and the product is regular.'' Implies

discount

customerproduct

Implieshead

Atomop Rel discount

Var customerproduct5.0 percent

Var productInd 5.0 percent

body

premiumcustomer

bodyAnd

Atomop Rel premium

Var customer

regularproduct

Atomopr Rel regular

Var product

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Scope of Reaction RuleMLScope o eact o u e

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General Syntax for Reaction Rules(R ti R l ML 0 2)(Reaction RuleML 0.2)

o d objec de e /o d

l D ! l / lt ti ti / l D

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Messages in Reaction RuleMLessages eact o u e

@mode = inbound|outbound attribute defining the type of a message @directive attribute defining the pragmatic context of the message e g one @directive attribute defining the pragmatic context of the message, e.g. one

or more FIPA ACL performatives, KQML, OWL-QL, Standard Deontic Logic norms,

< oid > the conversation id used to distinguish multiple conversations and g pconversation states

< protocol > a transport protocol such as HTTP, JMS, SOAP, Jade, Enterprise Service Bus (ESB) ...

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< sender >< receiver > the sender/receiver agent/service of the message < content > message payload transporting a RuleML / Reaction RuleML

query, answer, or rule base

Enterprise Service BusEnterprise Service BusCommunication Middleware

++Service Object Broker

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Mule Enterprise Service Busu e te p se Se ce us Mule ESB Open

SourceM Pl tf d Message Platform and distributed Object Broker

Staged Event Driven Architecture (SEDA)

> 30 Protocols (JMS > 30 Protocols (JMS, HTTP, SOAP )

Synchronous and AsynchronousAsynchronous Communication

Complex Message-d i E t

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driven Event Processing (CEP)

Mule Enterprise Service Busu e te p se Se ce us

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Use Case: Symposium Question-Answering(b it Vi t l O i ti )(by its Virtual Organization)

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Use Case: Symposium Question-Answering(b it Vi t l O i ti )(by its Virtual Organization)

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Personal Agentse so a ge ts

A personal agent assists a single person A personal agent assists a single personof an organization, (semi-autonomously) acting on his/her behalfon his/her behalf

The personal agent contains a FOAF*-like profile plus FOAF-extending rules

23* The Friend of a Friend (FOAF) project: http://www.foaf-project.org

Organizational AgentsO ga at o a ge ts

Organizational agents are used to represent Organizational agents are used to represent goals and strategies shared

by each person in the organizationby each person in the organization

Organizational agents contain rule sets that describe their organizations policies, regulations, opportunities, etc.

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External Agentste a ge ts

External agents communicate with the public interface of the organizational agents, exchanging messages that transport queriesexchanging messages that transport queries,

answers, or complete rule sets End users employ a Web (HTTP) interface as an End users employ a Web (HTTP) interface as an

external agent of Rule Responder (currently an API-like browser interface)

Support for multiple external agents (end users) at the same time

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Architecture - Overview

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Rule Enginesu e g es

Prova (Prolog + Java)( g )

OO jDREW (Object Oriented Java OO jDREW (Object Oriented Java Deductive Reasoning Engine for the Web)

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Prova

Prova is used to implement the organizational agents of Rule Responderagents of Rule Responder

Prova is also used for some personal agents Prova is also used for some personal agents

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OO jDREW

OO jDREW is used for implementing personal agents of Rule Responder

Two modes of Rule Execution: Bottom-up (fact-directed forward reasoning) Top-down (query-directed backward reasoning)

Rule Responder primarily uses top-down Supports rules in the following formats:

POSL (Positional Slotted presentation syntax)RuleML (XML syntax, can be generated from

POSL)

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Use Case: Symposium Organizer Use Case Sy pos u O ga e

RuleML-200x SymposiumOne organizational agent acts as the

single point of entry to the symposium Assists with planning, preparing, and

running the symposium Personal agents support chairs of the

symposiumy pProgram Chair, Publicity Chair, Panel

Chair, General Chair, etc.

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Online DemoO e e o

http://responder.ruleml.org/

Use Case Demo Link: http://ibis.in.tum.de/projects/paw/ruleml-2007/

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Ex : Personal Agents Knowledge BaseEx.: Personal Agent s Knowledge Base

% Sample OO jDREW rule in POSL syntax:% p j yperson(?person, ?role, ?title, ?email, ?telephone) :-

mailphone(?person ?email ?telephone)mailphone(?person, ?email, ?telephone),role(?person, ?role),

title(?person ?title)title(?person, ?title).

% Sample OO jDREW facts used by the above rule:% Sample OO jDREW facts used by the above rule:mailphone(John, john@email.com, 1-555-555-5555).

l (J h P l Ch i )32

role(John, Panel Chair).title(John, PHD).

Ex : Organizational Agents Knowledge Base (Abridged)Ex.: Organizational Agent s Knowledge Base (Abridged)

% Sample Prova-like rule in POSL syntax:getContact(?conference part ?info ?contact) :-getContact(?conference_part, ?info, ?contact) :-

person(?contact, ?role, ?title, ?email, ?telephone),... .

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Sample Query to the Organizational Agent: getContact