Rule Responder is a RuleML-based semantic web service that can be used to implement semi-automated decision support. It uses a middleware architecture with personal agents, organizational agents, rule engines like Prova and OO jDREW, and an enterprise service bus like Mule. A sample use case is a question answering system for a symposium organized by virtual agents.

1. Rule Responder:
RuleML-Based
Semantic Web Ser ices
Services
Talk at Dept. of Computer Science, NCCU
Taipei, Taiwan, 7 March 2008
Harold Boley1, Benjamin Craig1, Adrian Paschke2
y j g
1 National Research Council of Canada
University of New Brunswick, Canada
2 Bioinformatics, BIOTEC Center,
, ,
Technical University Dresden, Germany

2. Introduction
t oduct o
Topic of this talk
Rule Responder: Extending the Semantic Web
towards a Semantic-Pragmatic Web Service
infrastructure for distributed rule-based
human-computer collaboration
Rules employed for semi-automated
decision support
delegation/coordination
negotiation flow
reaction logic
1

3. Towards a Pragmatic Web
o a ds ag at c eb
1. Explicit Meta-data
vCard, PICS, Dublin
Core, RDF IEEE LOM
C RDF,
(Learning Objects
Metadata), Micro
Formats, FOAF, SIOC …
2. Ontologies
RDFS, OWL Lite|DL|Full
3. Logic and Inference
e.g. Logic Programming
Rule/Inference Engines
4. Software Agents and
Web Services
FIPA, Semantic Web
Services, RBSLA, …
2

4. Pragmatic Web with Two Kinds of Collaborative Agents
Interaction
3

5. 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 quot;the relations of signs to their interpreters or
the interpretersquot;
users. “
Eugen Halton, http://www.nd.edu/~ehalton/Morrisbio.htm, 1992
Pragmatic Web
Utilize the heterogenous Semantic Web resources meta data and
resources,
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
e.g. KQML-
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
learning and knowledge adaption / updates
4

6. Pragmatic
Agent Web
R l R
Rule Responder P j t
d Project:
http://responder.ruleml.org
5

7. Benefits of Rule-Based Decision and
Reaction Logic
1. Compact declarative representation of rules
p p
Clear semantics
Global rules which might apply in several contexts (reusability)
Separation of contract rules from the application code
p 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
Automated conflict resolution by rule prioritization
Rules play an important role to automatically transform contextual data,
derive new conclusions and decisions from existing knowledge and
behaviourally act according to changed conditions or occurred events
6

8. Rule Responder Architecture (MDA)
1. Computational independent model (CIM) with rules,
ti
processes, conversational fl
l flows ( in
(e.g. i a natural or visual
t l i l
language)
2. Platform independent model (PIM) which represents the
rules, events and ontologies in a common (standardized)
interchange format (e g a markup language)
(e.g.
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)
7

9. RuleML / Reaction RuleML
Platform Independent
Rule Interchange Format
http://ruleml.org
http://ruleml org
http://ibis.in.tum.de/research/ReactionRuleML/
8

10. 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 Reaction Integrity Transformation
Rules Rules Constraints Rules
RuleML
Translators
Homogeneous Approach
Reaction RuleML
Integrity
RuleML
Derivation RuleML
9

11. RuleML Enables ...
ue ab es
modelling
markup UML
translation
t l ti RDF
Rule interchange in XML
execution ASCII
publication
bli ti
archivingg
10

12. RuleML Language Family – Derivation RuleML
11

13. 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 levels
Benefits:
B fi
- easier to learn the language and to
understand their relationships
- facilitates reusability and complex
language assemblies from modules
- provides certain guidance to
vendors who might be interested
only in a particular subset of the
features
- easier t maintain, manage and
i to i t i d
extend in a distributed environment
12

14. 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>
Implies
<head>
head
<Atom>
Atom
<op><Rel>discount</Rel></op>
op Rel discount
<Var>customer</Var>
Var customer
<Var>product</Var>
Var product
<Ind>5.0 percent</Ind>
Ind 5.0 percent
</Atom>
</head>
<body>
body
<And>
And
<Atom>
Atom
<op><Rel>premium</Rel></op>
op Rel premium
<Var>customer</Var>
Var customer
</Atom>
<Atom>
Atom
<op><Rel>regular</Rel></op>
opr Rel regular
<Var>product</Var>
Var product
</Atom>
</And>
</A d>
</body>
</Implies>
13

15. Scope o Reaction RuleML
of eact o ue
14

16. General Syntax for Reaction Rules
(Reaction R l ML 0 2)
(R ti RuleML 0.2)
<Rule style=quot;activequot; evaluation=quot;strongquot;>
<label> <!-- metadata --> </label>
<scope> <!-- scope --> </scope>
<qualification> <!-- qualifications --> </qualification>
<oid> <!-- object identifier --> </oid>
od objec de e /o d
<on> <!-- event --> </on>
<if> <!-- condition --> </if>
<then> <!-- conclusion --> </then>
<do> <!-- action --> </do>
<after> <!-- postcondition --> </after>
<else> <!-- else conclusion --> </else>
<elseDo> <!-- else/alternative action --> </elseDo>
l D ! l / lt ti ti / l D
<elseAfter> <!-- else postcondition --> </elseAfter>
</Rule>
15

17. Messages in Reaction RuleML
essages eact o ue
<Message mode=quot;outboundquot; directive=quot;ACL:informquot;>
<oid> <!-- conversation ID--> </oid>
<protocol> <!-- transport protocol --> </protocol>
<sender> <!-- sender agent/service --> </sender>
<content> <!-- message payload --> </content>
</Message>
@mode = inbound|outbound – attribute defining the type of a message
@directive – attribute defining the pragmatic context of the message, e g one
message e.g.
or more FIPA ACL performatives, KQML, OWL-QL, Standard Deontic Logic
norms, …
< oid > – the conversation id used to distinguish multiple conversations and
g p
conversation states
< protocol > – a transport protocol such as HTTP, JMS, SOAP, Jade,
Enterprise Service Bus (ESB) ...
< sender >< receiver > – the sender/receiver agent/service of the message
< content > – message payload transporting a RuleML / Reaction RuleML
query, answer, or rule base 16

18. Enterprise Service Bus
Communication Middleware
+
Service Object Broker
17

19. Mule Enterprise Se ce Bus
u e te p se Service us
Mule ESB Open
Source
Message Pl tf
M Platform and
d
distributed Object
Broker
Staged Event Driven
Architecture (SEDA)
> 30 Protocols (JMS,
(JMS
HTTP, SOAP …)
Synchronous and
Asynchronous
Communication
Complex Message-
driven E
di Eventt
Processing (CEP)
18

20. Mule Enterprise Se ce Bus
u e te p se Service us
19

21. Use Case: Symposium Question-Answering
(by it Vi t l Organization)
(b its Virtual O i ti )
20

22. 21

23. Use Case: Symposium Question-Answering
(by it Vi t l Organization)
(b its Virtual O i ti )
22

24. Personal Agents
e so a ge ts
A personal agent assists a single person
of an organization, (semi-autonomously) acting
on his/her behalf
The personal agent contains a FOAF*-like profile
plus FOAF-extending rules
* The Friend of a Friend (FOAF) project: http://www.foaf-project.org
23

25. Organizational Agents
O ga at o a ge ts
Organizational agents are used to represent
goals and strategies shared
by each person in the organization
Organizational agents contain rule sets that
describe their organizations’ policies,
regulations, opportunities, etc.
24

26. External Agents
te a ge ts
External agents communicate with the public
interface of the organizational agents,
exchanging messages that transport queries
queries,
answers, or complete rule sets
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
25

27. Architecture - Overview
26

28. Rule Engines
ue g es
Prova (Prolog + Java)
( g )
OO jDREW (Object Oriented Java
Deductive Reasoning Engine for the Web)
27

29. Prova
Prova is used to implement the organizational
agents of Rule Responder
Prova is also used for some personal agents
28

30. 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)
29

31. Use Case Sy pos u O ga e
Case: Symposium Organizer
RuleML-200x Symposium
One 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
y p
symposium
Program Chair, Publicity Chair, Panel
Chair, General Chair, etc.
30

32. Online Demo
O e e o
http://responder.ruleml.org/
Use Case Demo Link:
http://ibis.in.tum.de/projects/paw/ruleml-2007/
31

33. Ex : Personal Agent’s Knowledge Base
Ex.: Agent s
% Sample OO j
p jDREW rule in POSL syntax:
y
person(?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:
mailphone(John, john@email.com, 1-555-555-5555).
role(John, P
l (J h Panel Ch i )
l Chair).
title(John, PHD). 32

34. Ex.:
Ex : Organizational Agent’s Knowledge Base (Abridged)
Agent s
% Sample Prova-like rule in POSL syntax:
getContact(?conference_part, ?info,
getContact(?conference part ?info ?contact) :-
person(?contact, ?role, ?title, ?email, ?telephone),
... .
33

35. Sample Query to the Organizational Agent: getContact
<RuleML xmlns=quot;http://www.ruleml.org/0.91/xsdquot;
xmlns:xsi=quot;http://www.w3.org/2001/XMLSchema-instancequot;
xsi:schemaLocation=quot;http://www.ruleml.org/0.91/xsd
http://ibis.in.tum.de/research/ReactionRuleML/0.2/rr.xsdquot;
htt //ibi i t d / h/R ti R l ML/0 2/ dquot;
xmlns:ruleml2007=quot;http://ibis.in.tum.de/projects/paw#quot;>
<Message mode=quot;outboundquot; directive=quot;query-syncquot;>
g q y y
<oid><Ind>RuleML-2007</Ind></oid>
<protocol><Ind>esb</Ind></protocol>
<sender><Ind>user</Ind></sender>
<content>
<Atom>
<Rel>getContact</Rel>
<Ind>ruleml2007_Challenge</Ind>
<Ind>ruleml2007 Challenge</Ind>
<Ind>update</Ind>
<Var>Contact</Var>
</Atom>
</content>
</Message>
34
</RuleML>

36. Architecture - Execution
35

37. Architecture - Execution
36

38. Architecture - Execution
37

39. Architecture - Execution
38

40. Architecture - Execution
39

41. 40

42. 41

43. Sample Query to the Publicity Chair: sponsor (1)
<content>
t t
<Atom>
<Rel>sponsor</Rel>
<Expr>
Expr
<Fun>contact</Fun>
<Ind>ben</Ind>
<Ind>nrc</Ind>
</Expr>
</E >
<Ind type=quot;integerquot;>500</Ind>
<Expr>
<Fun>results</Fun>
<Var>Level</Var>
<Var>Benefits</Var>
<Var>DeadlineResults</Var>
</Expr>
<Expr>
<Fun>performative</Fun>
<Var>Action</Var>
</Expr>
</Atom>
42
</content>

44. 43

45. Sample Query to the Publicity Chair: sponsor (2)
<content>
t t
<Atom>
<Rel>sponsor</Rel>
<Expr>
p
<Fun>contact</Fun>
<Ind>ben</Ind>
<Ind>nrc</Ind>
</Expr>
<Ind type=quot;integerquot;>5000</Ind>
<Expr>
<Fun>results</Fun>
<Var>Level</Var>
<Var>Benefits</Var>
<Var>DeadlineResults</Var>
</Expr>
/E
<Expr>
<Fun>performative</Fun>
<Var>Action</Var>
Var Action /Var
</Expr>
</Atom>
44
</content>

46. 45

47. Conclusion ( )
Co c us o (1)
Rule Responder can be used to implement
a wide range of use cases that require an
intelligent, semi-automated decision layer
semi automated
The middleware of Rule Responder allows
deployment of multiple use cases running
concurrently
Reusability on all levels:
Symposium Organizer, Rule Responder,
POSL, RuleML, OO jDREW, Prova, Mule
j
46

48. Conclusion ( )
Co c us o (2)
Develops an effective methodology and an efficient infrastructure to
interchange and reuse knowledge on the Web (
g g (ontologies and rules)
g )
and communicate contextual actions
Adds a pragmatic rule-based layer (Pragmatic Web),
defines the rules for using information resources and ontologies to support
g g
human agents in their decisions and react partially self-autonomously by
means of automated agents or services
Blends and tightly combines the ideas of multi-agent systems,
distributed rule management systems, and service oriented and event
systems
driven architectures
Addresses real-world software engineering needs for a highly
distributed, open, interoperable,
distributed open interoperable efficient and scalable Semantic Web
service and agent infrastructure
Design Science Research Approach
47

49. Conclusion (3)
Co c us o
Demonstrates the interoperation of various distributed platform-specific
rule execution environments based on Reaction RuleML as a platform-
p
independent rule interchange format interchanged over an enterprise
service bus as transport middleware
Applies rule-based technologies to the management of virtual
organizations and collaborative teams
Applies negotiation and distributed coordination mechanisms of rule-
based complex event processing and rule-based workflow like reaction
rule patterns
l tt
Demonstrates the integration and interoperation of rule standards
(RuleML), Object-Oriented programming (Java) and Semantic Web
(RDF, RDFS
(RDF RDFS, OWL) and metadata standards (e g iCal vCard FOAF)
(e.g. iCal, vCard,
48

50. Applications and Next Steps
Applications of the Pragmatic Agent Web and the Rule Responder
middleware in different projects
Rule Responder: http://responder.ruleml.org/
Reaction RuleML: http://ibis.in.tum.de/research/ReactionRuleML/
Prova Agent Architecture: http://www.prova.ws/
Prova Workflow Patterns: http://www.prova.ws/csp/?q=taxonomy/term/11
Rule Based Service Level Agreements: http://ibis.in.tum.de/projects/rbsla/
Next steps
Integration of further rule engines into the middleware (Rule Responder project)
Extending the Pragmatic Foundations of Rule Responder
Syntactical standardization issues (Reaction RuleML, Rule Interchange Format)
Running projects:
FungalWeb project with Rule Responder agents to answer queries on chemical
compunds (Chebi), enzymes (Mesh, EC classificaiton), species; Chebi and enzyme
classification (EC)
DILP (Distributed Inductive Logic Programming) for Mining Multi-Relation Data in
Multi Relation
Bioinformatics
DoReMoPat: Domain-specific Reference Models for Complex Event Patterns
49
Rule Responder: Symposium Organizer, Expert Finder, ...