Knowledge Mobilization: Architectures, Models and Applications Juan Gómez Romero Doctoral Thesis July 2008 Advisor: Miguel Delgado Calvo-Flores Department

Knowledge Mobilization:Architectures, Models and Applications

Juan Gómez Romero

Doctoral ThesisJuly 2008

Advisor: Miguel Delgado Calvo-Flores

Department of Computer Science and Artificial Intelligence

University of Granada

2Knowledge Mobilization: Architectures, Models and Applications

Overview

We have investigated solutions to the problems of building Knowledge-Based Systems that deliver

knowledge obtained from large information sources to nomadic users.

07/17/2008

3Knowledge Mobilization: Architectures, Models and Applications

Overview

• Mobile devices and communication networks have given rise to a shift from desktop applications to mobile systems.

• Mobile or nomadic systems can be accessed from anywhere at anytime by using mobile technologies.

• Intelligent systems can take advantage of mobile technologies and innovative functionalities can be implemented, but problems arise.

• We aim at providing solutions to the problems that appear in systems that deliver ellaborated knowledge to nomadic users.

• Contributions can be applied in non-mobile systems.

07/17/2008

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1. IntroductionThe problem

• Knowledge-Based System (KBS): Software system that manages represented knowledge to solve complex decision problems.

• KBSs provide support for decision-making by supplying the right person with the right information at the right time.

• But nowadays…– the right information has to be obtained by

integrating distributed and heterogeneous information sources.

– the right person can be located at anywhere.– the right time can be any moment.

07/17/2008 Knowledge Mobilization: Architectures, Models and Applications

Mobile Technologies

Knowledge Representation

5

1. IntroductionThe problem

• Use of mobile technologies in KBSs poses several challenges:– Technological issues. Mobile networks and devices have limited

capabilities: screen size, bandwidth, etc.

– Computational issues. Mobile systems have intrinsic features that make them more complex than a simple extension of classical systems:• Delivery of knowledge to distributed and sparse users (nomadic).• Adaptation to the context of the user (context-awareness).

• Knowledge Mobilization (KMob) is a recent approach that tackles computational issues of mobile KBSs with the aim of improving Knowledge Management procedures.


Knowledge Mobilization: Architectures, Models and Applications

Context-awaremodel

Knowledge Mobilization review

IASO system

Conclusions &future work

1.2. Methodology

Design artifacts

State of the art

Prototype

Evaluation

Architecture

7

1. IntroductionStructure of the thesis

Chapter 2. Review and analysis of the state of the art in intelligent mobile systems and Knowledge Mobilization.

Chapter 3. Abstract architecture to support the design of Knowledge Mobilization systems.

Chapter 4. Context-aware knowledge representation model for Knowledge Mobilization.

Chapter 5. Proof-of-concept system (IASO application).

Chapter 6. Conclusions and future works.

Bibliography


outline

• Introduction• The Knowledge Mobilization approach

• Architecture for Knowledge Mobilization

• Representation model for Knowledge Mobilization

• IASO: A Knowledge Mobilization application

• Conclusions and future work

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2. Knowledge MobilizationDefinition

• Keen & Mackintosh (2001). To make “knowledge available for real-time use in a form which is adapted to the context of use and to the needs and cognitive profile of the user”.

• Carlsson (2006). Four main tasks:– Creation of knowledge.

• Semantic Web, Ontologies and Fuzzy Logic.

– Activation of latent knowledge. • Multicriteria Optimization, Evolutionary Computing and Simulation.

– Retrieval of hidden knowledge. • Data and Text Mining and Text Summarization.

– Delivery of knowledge. • Multi-Agent Systems.


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1. Knowledge MobilizationOur proposal

• KMob addresses the challenge of building Knowledge Mobilization Systems, which are:– Ubiquitous. Accesible from anywhere, at anytime, using mobile

technologies.– Proactive. Discover what information is needed.– Declarative. Users do not specify how information has to be obtained,

but which is their situation and what information they need.– Context-aware. Behavior is adapted to context.– Integrative. Heterogeneous information sources, technologies and

devices.– Concise. Summarize and tailor gathered data.



Context and domain knowledge

Soft C

omputing

Environmental dataSensor TechnologiesMobile Devices

RFID GPSUltraLaptops Cell phones

Information integration & retrieval

Knowledge Discovery

Multi-agent systems

Knowledge Engineering

Knowledge Mobilization

Healthcare

m-government

Mobile business

m-commerce

SecurityMobile

assistance

Ap

plicatio

ns

Kn

ow

ledg

eIn

form

ation

Data

PDAs

Inference systems

...

Mobile Web

Geo-spatial data

User profiling models

General knowledge

Multimedia representationF

uzzy Log

ic

Semantic Web

User interfaces

On

tolo

gie

s

Wireless Communication technologies

Middleware SystemsWeb Services

SO

As

CORBAJini

Fu

zzy

On

tolo

gie

s

Data: Mobile Technologies

Information: Ontologies

Knowledge: Fuzzy Logic, MAS, Semantic Web

Applications: Healthcare

Know

ledg

e M

obili

zatio

n re

late

d ar

eas

1.3. Related areas

12

1. Knowledge MobilizationUse case

• Nomadic / Ubiquitous Healthcare.– A doctor is attending to a patient outside the hospital.– Patient’s clinical history is stored in the Hospital Information System (HIS).– The doctor uses a portable device to consult the patient’s history, in order

to prescribe a treatment.– The doctor retrieves a bunch of Electronic Health Records (EHRs). – The doctor filters the results manually and grasps interesting information.

– Typical scenario of Knowledge Mobilization.• The mobile device can be unable to process the information obtained, or

maybe the doctor has not enough time to review it (information overload)• It can happen also in non-mobile systems.


outline

• Introduction

• The Knowledge Mobilization approach• Architecture for Knowledge Mobilization




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3. Architecture for KMobRationale

• General software architectures cannot be directly extended to the Knowledge Mobilization context.

• Specific requirements (ubiquitous, proactive, declarative, etc.) and issues (communication, context-awareness).

• Contribution

Meta-architecture, i.e. an abstract schema of the components, relations and operations of a Knowledge Mobilization system.


15

2. Architecture for KMobAML

• The architecture is described with multi-agent terminology (MAS abstractions are used to describe distributed systems).

• Specification of the architecture with the Agent Modeling Language (AML)

• AML is a semi-formal visual language for specifying, modeling, and documenting systems in terms of concepts from MAS theory.

• Extends the UML meta-model.• Advantages: well-documented, supported by visual tools,

practical perspective.


16

2. Architecture for KMobAML


Entities Representation

Agent

Resource

Environment

Role

Service

Ontology

Context

Associations Representation

Social Association

Play Association

Service Provision

Service Usage

Diagrams RepresentationSociety General view of the architecture

Entity Detailed structure of an entity

Protocol Sequence / Communication

Specify communication acts between entities

MAS Deployment Implementation

Knowledge Mobilization: Architectures, Models and Applications3.3. description of the architecture

Knowledge providerSpecial service provider that manages a large knowledge base in the system, as well as incorporates other information sources (which may be external).

Mobile / Nomadic requesterMobile device (cell phone, PDA), which may have very limited computational capabilities.

Service providerImplement the services provided by the system: large database querying, real-time data supply, interface with knowledge bases…

General components of KMob systems


Society diagram ofthe architecture (simplified)

Desktop AgentAgents running on application servers

Nomadic AgentAgents running on mobile devices

Local KnowledgeModel

External KnowledgeModel

ServicesProvided / requested by the agents

RolesSet of actions that an agent acquire to provide or request a service

3.3. description of the architecture

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3. Architecture for KMobFrameworks

• The meta-architecture must be specialized for each specific problem.

• The meta-architecture does not state how systems should be implemented.– Which development platform should be used to implement a service

which provides knowledge about patients’ clinical histories?

• The application designer must decide how the architecture is instantiated and which technologies are going to be used to implement it.


20


• Three possible distributed technology frameworks:– Multi-Agent. Direct implementation with a MAS platform (JADE).

• Pro: Independent components that require complex coordination policies. • Con: MAS platforms require a considerable amount of CPU resources.

– Tuplespace. Use of shared repository of knowledge with an elemental structure (Linda, Javaspaces).• Pro: Simple mechanism to achieve communication and coordination.• Con: Tuplespaces require a considerable amount of network resources.


21


• Three possible distributed technology frameworks:– Client-Server. Request-reply communication (HTTP-based, WS).

• Pros: – The most simple schema.– Allows the client to delegate most of the processing to the server

(very limited devices can participate).• Con:

– Does not allow complex interaction patterns.

• It will be used in our application, since it satisfies our requirements.


outline

• Introduction

• The Knowledge Mobilization approach

• Architecture for Knowledge Mobilization• Representation model for Knowledge

Mobilization



23

4. Representation model for KMobRationale

• Knowledge Mobilization systems require a formalism to easily represent and manage knowledge.

• Ontologies are a representation formalism that promotes knowledge integration, sharing and reuse.

• Ontologies are based on Description Logics (DLs), a family of logics with well-defined semantics specially designed to represent structured knowledge.

• Description Logics are classified in levels (and named) according to their expressivity, which determines the complexity of reasoning with the logic.

• The Semantic Web uses ontologies to represent metadata and offers several tools, such as the standard OWL language.


24


• Knowledge Mobilization formalisms are expected to solve information overload issues.

• To avoid information overload, only significant knowledge must be provided to users.

• What is significant? It depends on user circumstances: location, preferences, previous actions, etc. → Context

• Use of context knowledge to determine what is significant and summarize available knowledge.

• Knowledge Mobilization ontologies must provide support to represent, manage, and reason with context knowledge.


25


• Contribution

Meta-model, i.e. a design pattern to create context-aware ontologies that avoid information overload.

• Significance ontologies to represent which information of the domain is relevant in a given context.

• CDS (Context-Domain Significance) pattern formulated in ALC.• Directly translatable to OWL (≈ SHOIN(D)), the most

expressive DL level considered.

• In several cases, fuzzy knowledge must be considered.• Extension of the pattern using fuzzy Description Logics.


26

4. Representation model for KMobFormulation

• Base ontologies:– Context ontology (KC): vocabulary to describe context situations.– Domain ontology (KD): ontology to represent domain-specific

knowledge.

• New significance ontology: CDS ontology (KS)– Complex contexts (Ci ):

• Concepts created using terms of KC.

– Complex domains (Dj ): • Concepts created using terms of KD.

– s-connection (si,j or Pi,j):

• A concept linking a complex context Ci and a complex domain Dj.

• Denotes that Dj is significant in situation Ci .


27

4. Representation model for KMobExample


Hospital Information System

Emergency Situation Description Model

HIS Abstract Model

Context-Domain Significance Model

1

DrugIntollerances

ProcaineAllergic D

CurrentPrescriptions

ó

ò1

UnconsciousnessC

Hemorrhage

ó1,1 c 1 d 1

P R .C R .D ó

Dom

ain

onto

logy

Contextontology

28

4. Representation model for KMobReasoning


Domain Ontology Context Ontology


CnDm Pn,m

I

EI

• Domain knowledge I significant in a scenario E. • Algorithm (implemented in the CDS API):

– Retrieve the complex contexts Cn more general than E.

– Retrieve the s-connections Pn,m involving Cn.

– Retrieve the complex domains Dm involved in Pn,m.

– Retrieve the concepts I of the domain more specific than Dm.

Complete and decidableComplexity is determined by Ci and Dj (EXPTIME-complete for ALC)

Knowledge Mobilization: Architectures, Models and Applications4.5. Protégé CDS plug-in

Context sideContext ontologyComplex contexts

Domain sideDomain ontologyComplex domains

s-connections

Query tab

30

4. Representation model for KMobFuzzy extension of the CDS pattern

• Limitations of the crisp ontology design pattern:– Imprecise knowledge cannot be represented

• E.g.: A patient is slightly unconscious– Partial similarities between contexts cannot be represented

• E.g.: Anaphylaxis is quite similar to sepsis– Relevance relations cannot hold to a degree

• E.g.: Blood-borne diseases are less relevant than drug intolerances

• Contribution

Fuzzy extension of the crisp meta-model, i.e. a design pattern to create fuzzy context-aware ontologies that avoid information overload and allow vague knowledge to be managed.


31


• The significance ontology is a fuzzy ontology (fCDS) created with an adaptation of the crisp rules of the CDS pattern.

• The fuzzy significance ontology is expressed with the fuzzy Description Logic fALC.

• Fuzzy DLs extends DLs to the fuzzy case (Straccia, 2006).– Concepts are fuzzy sets – Axioms hold to a degree (inclusion!)

– Roles are fuzzy relations – Interpretation has fuzzy semantics

• Reasoning can be performed with a fuzzy DL reasoner or by reducing the fuzzy ontology to an equivalent crisp DL ontology and using a crisp inference engine (Bobillo, Delgado & Gómez-Romero).


32



Hospital Information System

Emergency Situation Description Model

HIS Abstract Model


1

DrugIntollerances

D ProcaineAllergic

CurrentPrescriptions

d

ó

ô ò1

UnconsciousnessC

Hemorrhage c

óô

1,1 c 1 d 1P R .C R .D 1,1

ô ó

33


• Domain knowledge I a-significant in a scenario E.– Knowledge significant and degree of significance


Domain Ontology Context Ontology


Cn

E

Dm

I

Pn,m

I

knl

k,l

i,j

aggregation: min t-norm a b

greatest lower bound: glb = sup{a : K < t ≥ a>}

Complete and decidableComplexity is determined by Ci, Dj, and the glbs to be calculated

p

m

outline

• Introduction






5. IASO applicationDescription

•IASO (Intelligent ASisstant for Outdoors Healthcare).•KMob system to solve the Nomadic Healthcare problem for the HIS of the Hospital Clinico San Cecilio of Granada.

•Client-server application accesible from an intranet.

•The system is effective, but problems arise when:

– It has to be accessed from outside the intranet.

– The doctor has not enough time to review and filter patient registers to find interesting information.

07/17/2008 35Knowledge Mobilization: Architectures, Models and Applications

Representationmodel

Architecture

IASO

36

5. IASO applicationKnowledge base

• Three OWL ontologies have been created:• Context ontology:

– Based on Galen medical ontology.– Concepts (Hemorrhage) and relations (galen:hasSymptom) .

• Domain ontology:– Created from scratch (specific for San Cecilio database).– Concepts (Patient, Register) and relations (hasRegister).

• Significance ontology.

– The significance ontology is crisp. Since the IASO application is a verification proof of the pattern, the crisp version has been firstly used.


Knowledge Mobilization: Architectures, Models and Applications5.3. IASO architecture

Client agentRequires knowledge (with a mobile device)

Client roleRequest data functions

Query serviceQuery solving service

Server roleProvide knowledge functions

CDS KnowledgeModel

HIS data

Server agentProvides knowledge

Knowledge Mobilization: Architectures, Models and Applications5.4. IASO implementation

SQL BridgeLinks the ontological and the relational models, avoiding to import all the HIS database into the domain ontology.Implemented with D2RQ (Bizer & Seaborne, 2004).

39

5. IASO applicationInput form


Query vocabularyPatient description

vocabulary

In-construction queryPartial (conjunctive)

query

Patient IDPatient identification (name)

40

5. IASO applicationOutput form


ResultsRelevant registers and contents

Further informationRegister relevant to a more specific situation that may be considered

outline

• Introduction




• IASO: A Knowledge Mobilization application• Conclusions and future work

42

6. Conclusions and future workSummary and conclusions

• Overall objective: – Provide integral solutions for the

challenges that arise when developing mobile systems for the delivery of knowledge retrieved from large information sources to nomadic users.

• Operational objectives:– Distribution of knowledge in

mobile systems.– Solving of information overload

by summarization of available data.


Architecture for Knowledge Mobilization

Context-aware (fuzzy) representation model

IASO application

43

6. Conclusions and future workFuture work

• Future work:– Apply proposals in other problems and areas (new fields of study and

domains of application!)– Architecture:

• Specify in detail orchestration and choreography.• Introduce Semantic Web Services to describe service features.

– Representation model:• Compare with other Logics (non-monotonic logics).• Further studies on the fuzzy extension: simplification and better support.

– IASO system:• Reliable deployment.• Support security.• Extend supporting ontologies, particularly to the fuzzy case.


end

Knowledge Mobilization:

Architectures, Models and Applications

Juan Gómez Romero

gracias

thank you

Documents

Knowledge Mobilization: Architectures, Models and Applications Juan Gómez Romero Doctoral Thesis July 2008 Advisor: Miguel Delgado Calvo-Flores Department