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D. Riaño Knowledge Management 1

Knowledge Management


∗Índex

• Introduction• History• Knowledge Model and Knowledge Life Cycle• Representation• KM technologies• KM tools• Specific purpose technologies


INTRODUCTION


Definition

Knowledge management (KM) is ...• ... the process through which organizations generate value from

their intellectual property and knowledge-based assets. KM involves the creation, dissemination, and utilisation of knowledge.

• ... the strategy, processes, and technology employed to enable an enterprise to acquire, create, organise, share, and make actionable knowledge needed to achieve the vision of the enterprise.

• ... the tools, techniques, and strategies to retain, analyse, organise, improve, and share business expertise.

WAY to perform some TASK aiming to some GOAL through knowledge


Melt of disciplines

InformationRetrieval

ManagementScience

ArtificialIntelligence

OrganizationalBehaviour

KMKMKM


Four Disciplines

• Management Sciences: Management sciences are a range of methods used to assist managers through applying scientific and quantitative approaches to the management of organizations, often involving the construction of computable models of the key features in decision-making.

• Organizational Behaviour:Organizational Behaviour is the study of human behaviour at the individual, group and organizational level.

• Artificial Intelligence: Artificial Intelligence is a branch of science which deals with helping machines find solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way.

• Information Retrieval:Information retrieval is the task of finding information.


Knowledge in Business

OLD VISION• Francis Bacon’s vision• Knowledge is power• Foster individualism & competition• Company output: products• Modernization trough new

technologies.

NEW VISION• KM’s vision• Sharing knowledge is power• Foster grouping & collaboration• Company outputs: services and

products derived from knowledge• Modernization through incorporating

knowledge at decisional level.

FIRM(Blackbox)RESOURCES

RESOURCES

RESOURCES

PRODUCTS

PRODUCTS

PRODUCTS RESOURCES

RESOURCES

RESOURCES

FIRM(Dynamics)

PRODUCTS

PRODUCTS

PRODUCTSSERVICES

SERVICES

SERVICES

“Knowledge itself is worthy of attention because it tells firms how to do things and how they might do them better”

T. H. Davenport, Director of the Accenture Institute for Strategic ChangeL. Prusak, Executive Director of the IBM Institute for KM


Knowledge Worldwide

Scientific & Technical migration.Scientist going back to their born countries.Knowledge migration from richto new emerging countries.Capturing K in rich countries.KM Policies.

People leaving a firm.Lost of Knowledge.Capturing company knowledge.KM tools.


General Objectives of KM

1. Strategic management of the intellectual resources.

2. Efficient K discovery.3. Effective K application:

Utilisation of the Available KKnowledge Sharing and ReuseAccessibility of KnowledgeEmbedding K in the Work Context

Knowledge processes:

1. production2. validation3. integration

• Aspects of the enterprise that KM deals with:

Business StrategiesProducts and ServicesBusiness ProcessesOrganisational StructuresPolicies and ProceduresCulture and ValuesInformation Systems

• The enterprise perspective:

What’s what a company knows?How efficiently it uses what it knows?How it acquires and uses new K?


KM Model: Software Experience Factory

OrganizationOrganizationOrganization

Experience FactoryExperience FactoryExperience Factory

•••StructureStructureStructure•••ResourcesResourcesResources•••NormsNormsNorms•••StrategiesStrategiesStrategies

ProjectProjectProjectteamteamteaminfrastructureinfrastructureinfrastructurework planwork planwork planbudgetbudgetbudget

DecisionsDecisionsDecisions&&&

EvaluationsEvaluationsEvaluations

KMKMKMToolToolTool

KnowledgeKnowledgeKnowledgeBaseBaseBase


Data, Information, Knowledge, Wisdom, …Life Cycles

•Connectivity• Transactions

• Cost• Speed• Capacity

Acq

uire

Rep

rese

nt

Reu

seEngineering

Engineering

Engineering

EngineeringWISDOM

• Informativeness• Usefulness

• Timeliness• Relevance• Clarity

KNOWLEDGE

INFORMATION

DATA

QualitativeEvaluation

QuantitativeEvaluation

KM


Data

• A) set of discrete, objective facts about events. Data is transformed into information by adding value through context, categorisation, calculations, corrections, and condensation.

• B) facts and figures, without context and interpretation.

• The nature of data is raw and without context. It simply exists and has no significance beyond its existence. It can exist in any form, usable or not.

Single value: 90 kg.Multiple value: (green, ugly, biped, grumpy)


Information

• A) a message, usually in the form of a document or an audible or visible communication meant to change the way the receiver perceives something, to have an impact on his judgement and behaviour.

• B) patterns in the data.

• Information is data that have been given a meaning by way of context.

Single value: 90 kg.Multiple value: (green, ugly, biped, grumpy)


Knowledge

• A fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of “knowers”. In organisations, it often becomes embedded not only in documents or repositories but also in organisational routines, processes, practices, and norms.

• Actionable information.• The integration of ideas, experience, intuition, skill, and lessons learned that has

the potential to create value for a business, its employees, products and services, customers and ultimately shareholders by informing decisions and improving actions.

• Knowledge is information combined with understanding and capability; it “lives” in the minds of people. Typically, knowledge provides a level of predictability that usually stems from the recognition of patterns.

• Knowledge is information that has been generalized to increase applicability.

Hero + Fun + Reward = successful road movie


Data + “meaning” = Information

• Sorts of “meanings”:– Contextualization: the purpose of the data gives a meaning.

(ex. Clients that will be emailed)– Categorization: the data are classified / generalized in concepts.

(ex. Company clients vs. Autonomous clients)– Calculation: the meaning is given by a mathematical or statistical

analysis.(ex. Good client = buys ≥ 1$ million)

– Correction: remove errors from data.(ex. Expenses in £ (instead of €) inform about English clients)

– Condensation: data is summarized in a more concise form.(ex. Incentives out of client data gives info about incentive plans)


Information + “something” = Knowledge

• Sorts of “something”:– Comparison: is this information representing something similar to

other situations.(ex. Defining a firm crisis)

– Consequences: implications of the information in company decisions and actions.

(ex. Identify moments in which the firm must invest)– Connections: how the information is related to other information.

(ex. There is a ratio 2/1 between incomes and investment)– Conversations: what people think about some information.

(ex. Useful / useless concepts)

• Something = application (Tobin, 1998)


Knowledge + intuition + experience = Wisdom

• Other upper to Knowledge concepts:– Wisdom:

knowledge + intuition + experience– Expertise:

wisdom + selection + principles + constrains + learning– Capability:

expertise + integration + distribution + navigation


Sorts of Knowledge (i): evidence

• Explicit Knowledge: the kind of knowledge which can be expressed in words and numbers and shared in the form of data, scientific formulae, product specifications, manuals, universal principles, etc. This kind of knowledge can be transmitted across individuals formally and systematically. It can be processed by a computer, transmitted electronically, or stored in databases.

• Implicit or Tacit Knowledge: the kind of knowledge which can be found in the heads of employees, the experience of customers andthe memories of past vendors. It is highly experiential, difficult to document in any detail, ephemeral and transitory.

Socialization ExternalizationInternalization Combination

Tacit ExplicitTO

TacitExplicitFROM


Sorts of Knowledge (ii): purpose

• Declarative Knowledge or know-what: factual assertions an organisation makes about itself, its capabilities, and the marketplace. With this knowledge you know what are the tasks that you have to do.

• Procedural Knowledge or know-how: business and organisational processes and strategies of the company. With this knowledge you know how you are supposed to do the tasks that you have to do.

We do what we do because of of our know-whatWe do what we do the way we do it because of our know-how


Sorts of Knowledge (iii): ownership

• Individual Knowledge: personal skills, expertise, and experience of each employee of a company about the company processes and the company related domains.

• Group Knowledge: understanding of company groups of employees (i.e. collectives) as they collaborate and co-operate. This includes all the individual knowledge of each of the employees in the group and some extra added value.

• Organizational Knowledge: knowledge held by the organization as a whole.


Sorts of Knowledge (iv): format

• Informal Knowledge: natural language oral, textual or graphical representation of the knowledge (ex. *.TXT).

• Semi-Structured Knowledge: informal representation of knowledge enriched with some attributes (ex. *.XML).

• Structured Knowledge: the knowledge is represented according to some attribute-based structures (ex. *.DB2)

• Formal Knowledge: the knowledge is represented by means of knowledge structures as frames, production rules, ontologies, etc.


Crossing Enterprise Aspects and Knowledge Types

explicit implicit know-what know-how

Business Strategies Y N N YProducts and Services Y N Y NBusiness Processes N Y N YOrganisational Structures Y N Y NPolicies and Procedures Y Y Y YCulture and Values Y Y Y NInformation Systems Y Y Y N


∗Data representation and organization

• Matrix representations– Column Heading = typed feature– Row = instance– Cell = (single) data

• Data bases– Relationship: column to column– Cardinality: 1, N– Optionality: 0 allowed Y/N

• Data warehouses

dataarity


∗Information representation

• Information Systems


Knowledge representation and modelling

KR aims at expressing knowledge in a computer manageable way, so that it can be used in an computer intelligence process.

• KR aspects:–Syntactic: structures that support the representation.–Semantic: meaning of the knowledge represented.–Reasoning & Inference: process by which knowledge is used to obtain conclusions.

• Inference aspects:–Forward chaining (modus ponens): A, A →B B–Backward chaining (modus tollens): ¬B, A →B ¬A

• Knowledge-base aspects:–Completeness: given a KB, the inference process can find B or ¬B, for any correct assertion B.–Soundness: given a KB, the inference process cannot find both B and ¬B, for any correct assertion B.

⊥⊥


Artificial Intelligence Knowledge Models

• Frames (Minsky 1975)• Scripts• Semantic Networks (Michalski 1983)• Rules• Ontologies

• Tools to model knowledge:– commonKADS– Protégé 2000– Unified Modelling Language (UML) - Object Constraint Language (OCL)– Multi-Perspective Modelling


Knowledge Representation: Frames

• Frame: a single know-what knowledge structure containing slots.• Slot: element of the frame that contains one or more facets.• Facets: element that describes something about a slot.• Demons: procedures attached to slots that are fired circumstantially.• Instance: frame example.

• Relationships between frames:– Slot sub-concepts: contains links to other frames which represent sub-concepts.– Slot type: GENERIC or INSTANCE.– Slot with facet other containing another frame.

• Facets may take one of the following forms: – Values: contains the slot (single or multiple) value.– Default: used if there is not other value present.– Range: informs about the kind of information the slot can contain.– if-added: procedural attachment which specifies an action to be taken when a value in the

slot is added or modified (forward chaining, data-driven, event-driven or bottom-up reasoning).

– if-needed: procedural attachment which triggers a procedure which goes out to get information which the slot doesn't have (backward chaining, goal driven, expectation driven or top-down reasoning).

– Other: may contain frames, rules, semantic networks, or other types of knowledge.


Frames: car’s example(frame

(name (values CLASSIC-CAR))(type (values GENERIC))(sub-concepts (range BEETLE SEDAN JEEP TOPOLINO …))(company (range CAR-COMPANY)

(if-needed (search-Co model)))(model (range CAR-MODEL)

(if-added (confirm-exists model company)))(horse-power (range 1..200))(start-prod (default UNKNOWN))(finish-prod (default PRESENT))(color (range {R W B Y DARK OTHER})(factory-price (range NUMBER))(retail-price (if-needed

(add-interests factory-price))(if-added

(check-above-15% factory-price)))(wheels (range NUMBER) (default 4))

)

(frame(name (values BEETLE))(type (values GENERIC))(instances (values John’s-CAR …))(company (values VOLKSWAGEN))(Horse-Power (range 50..90))(start_prod 1938)(color B)(retail-price 8000€)

)


Knowledge Representation: Scripts

• Script: A structure that describes appropriate sequences of events in a particular context. A type of frame that describes what happens temporally (know-how).

• Properties: objects being part of the script (frames or strings).• Roles: agents involved in the script definition (frames or strings).• Starting/Opening conditions: conditions that make the script be

valid (pre-condition).• Scenes: actions in the script.• Results: conditions that are valid after the script is ran (post-

condition).

• Scripts extend frames with complex temporal events.


Scripts: car’s example

(script(name (values BUY-A-CAR))(type (values GENERIC))(props (values SHOP MONEY CAR CATALOG OFFICE))(roles (values CUSTOMER SELLER))(opening (wants CUSTOMER CAR))(results (if-needed (owner CAR CUSTOMER)

(has-less-money CUSTOMER)(increase-sells SHOP)))

(scenes (ENTERING (enters CUSTOMER SHOP)(go-to-scene INSPECTING))

(INSPECTING (observes CUSTOMER CAR)(or (go-to-scene ASKING) (leaves SHOP CUSTOMER)))

(ASKING (look-for CUSTOMER SELLER)(meet CUSTOMER SELLER OFFICE)(asks-for CUSTOMER CATALOG)(informs SELLER CUSTOMER)(or (go-to-scene BUYING) (leaves SHOP CUSTOMER)))

(BUYING (pays CUSTOMER MONEY SELLER)(leaves SHOP CUSTOMER))

))


Knowledge Representation: Semantic Networks(John F. Sowa)

• Definitional networks emphasize the is-a relation between concepts. The resulting network, also called a generalization or subsumption hierarchy, supports the rule of inheritance for copying properties defined for a supertype to all of its subtypes. Since definitions are true by definition, the information in these networks is often assumed to be necessarily true.

• Assertional networks are designed to assert propositions. Unlike definitional networks, the information in an assertional network is assumed to be contingently true, unless it is explicitly marked with a modal operator. Some assertional networks have been proposed as models of the conceptual structures underlying natural language semantics.

• Implicational networks use implication as the primary relation for connecting nodes. They may be used to represent patterns of beliefs, causality, or inferences.

• Executable networks include some mechanism, such as marker passing or attached procedures, which can perform inferences, pass messages, or search for patterns and associations.

• Learning networks build or extend their representations by acquiring knowledge from examples. The new knowledge may change the old network by adding and deleting nodes and arcs or by modifying numerical values, called weights, associated with the nodes and arcs.

• Hybrid networks combine two or more of the previous techniques, either in a single network or in separate, but closely interacting networks.


Knowledge Representation: “Definitional” Semantic Networks


Knowledge Representation: “Assertional” Semantic Networks

A Cb ∃A ∃C: b(A,C)

A Cb¬

∃A ∃C: ¬b(A,C)

Example: “If a person wants a car, he must go to the car dealer”

person carwant

¬go dealer

¬


Knowledge Representation: “Implicational” Semantic Networks

• Semantic network in which arcs represent logic implications.• Sorts of “implicational” Semantic Networks:

– Belief Networks (Judea Pearl, 1988)– Causal Networks (Chuck Riegel 1976)– Bayesian Networks– Truth-Maintenance Systems, TMS (Doyle, 1979)

Example: “A person goes to a car dealer because he needs a car, and buy it if he likes the car and he can pay the price”

Like car

Need car

Can pay

Go dealer

Buy car

deal

Go home

good bad


Knowledge Representation: “Executable” Semantic Networks

• Semantic networks that represent dynamic processes or proceduralknowledge.

• General elements of the networks:– Message passing through the network arcs– Attached procedures to the network nodes– Graph transformations as external triggered actions

• Sorts of “executable” Semantic Networks:– Dataflow diagrams – Petri Nets (Carl Adam Petri, 1962)


“Executable” Semantic Network: examples

DFD: “Retail price calculation” Petri Net: “Car selling”

Factory price

Retail price

DealerProfit margin

ShopProfit margin

Companyprice

Company%

Shop%

Dealer%

+

waiting room

available dealer

car inspecting

asking

entering

buying

going

1 CalculateCompany

profit

2 CalculateShopprofit 3 Calculate

Dealerprofit

4 CalculateCar Price

SHOP PROFIT

Retail price

Factory price

Factory price

Company price

Dealer

bene

fit


Knowledge Representation: “Learning” Semantic Networks

• Semantic networks that can adapt to new incoming evidences.

• These modifications can be at three levels:– Rote memory: the new knowledge is represented by a semantic

network that is appended to the global semantic network.– Changing weights: when the knowledge in the network is

weighted with numerical values (in nodes and arcs), the new knowledge modifies some of the weights in the network.

– Restructuring: new knowledge changes the structure of the semantic network adding or removing nodes and arcs.

• Sorts of “learning” Semantic Networks:– Artificial Neural Networks


Knowledge Representation: Rules

• Selectors• Premise ⇒ Conclusion• Syntactic differentiation

– Conjunctive: [a1 ∧ a2 ∧ … ∧ ak ⇒ b].– Disjunctive: [a1 ∨ a2 ∨ … ∨ ak ⇒ b].– K-term DNF: [(a11 ∧ … ∧ a1k1) ∨ … ∨ (ai1 ∧ … ∧ aiki) ⇒ b], i ≤ k.– K-DNF: [(a11 ∧ … ∧ a1k1) ∨ … ∨ (ai1 ∧ … ∧ aiki) ⇒ b], kj≤ k.– K-CNF: [(a11 ∨ … ∨ a1k1) ∧ … ∧ (ai1 ∨ … ∨ aiki) ⇒ b], kj≤ k.

• Semantic differentiation– Production rules: conceptual rules.– Association rules: the rule indicates the value of b, when the values of

the a’s are known.– M-of-N rules: the rule is fired if M of N selectors in the premise are true.– Ripple-down rules: exceptions to the rules are appended at the end of

the rule as a ripple down rule.


∗Knowledge Representation: Ontologies

• An ontology is a specification of a conceptualization.• An ontology may take a variety of forms, but necessarily it will include a

vocabulary of terms, and some specification of their meaning. This includes definitions and an indication of how concepts are inter-related which collectively impose a structure on the domain and constrain the possible interpretations of terms.

• What does an ontology do?– Captures knowledge– Creates a shared understanding – between humans and for computers– Makes knowledge machine processable– Makes meaning explicit – by definition and context

• Components of an ontology:– Concepts: Class of individuals– Relationships between concepts– Is a kind of relationships: they form a taxonomy– Other relationships: they give further structure –is a part of, belongs to, etc.– Axioms: constrains about the concepts –Disjointness, covering, equivalence, etc.

Ex. Cover (X, Y) <- X member Of interval and Y member Of interval and X.start <= Y.start and X.end >= Y.end

• Instances

D. Riaño Knowledge Management 40weak semanticsweak semantics

strong semanticsstrong semantics

Is Disjoint Subclass of with transitivity property

Modal Logic

Logical Theory

Thesaurus Has Narrower Meaning Than

Taxonomy Is Sub-Classification of

Conceptual ModelIs Subclass of

DB Schemas, XML Schema

UML

First Order Logic

RelationalModel, XML

ER

Extended ER

Description LogicDAML+OIL, OWL

RDF/SXTM

Syntactic Interoperability

Structural Interoperability

Semantic Interoperability

∗


Knowledge Engineering

• Formal methodologies for developing knowledge-based systems.

• KB and KB systems: Expert Systems.• K Life Cycle: problem selection, knowledge acquisition,

knowledge representation, knowledge encoding, knowledge testing and evaluation, implementation and maintenance.

SELECT ACQUIRE REPRESENT

ENCODETEST & EVALUATE

CREATE

SHAREAPPLY

KEKM


Knowledge Acquisition

Knowledge Expert Knowledge Engineer

Domain overview, goals, etc.

Identified concepts, values, etc.Identified sources of information

KnowledgeKnowledgeKnowledgeValidationValidationValidation

KnowledgeKnowledgeKnowledgeVerificationVerificationVerificationIdentified relationships, sequences, etc.

Amendments

Knowledge representation


The KM process

• Determine goals for KM activities• Create an overview of the available knowledge• Structure and Integrate knowledge• Acquire knowledge• Goal oriented disseminate the knowledge• Use productively the knowledge for the company• Storage and Maintain the knowledge• Assess the current knowledge and the compliance with goals


Case-Based Reasoning

A branch of AI that attempts to combine the power of narrative with the codification of knowledge on computers. Involves extraction of

knowledge from a series of narratives, or cases, about the problem.

(Aamodt & Plaza, 1984) The CBR paradigm covers a range of different methods for organizing, retrieving, utilizing and indexing the knowledge retained in past cases.Cases may be kept as concrete experiences, or as a generalization of a set of similar cases. Cases may be stored as separate knowledge units and distributed within the knowledge structure. Cases may be indexed by a prefixed or open vocabulary, and within a flat or hierarchical index structure. The solution from a previous case may be directly applied to the present problem, or modified according to differences between the two cases. The matching of cases, adaptation of solutions, and learning from an experience may be guided and supported by a deep model of general domain knowledge, by more shallow and compiled knowledge, or be based on an apparent, syntactic similarity only.

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Knowledge-Based Systems: Expert Systems

Second generation ESFirst generation ES

USER INTERFACE

EXPLAINATIONFACILITY

SEARCH STRATEGIES

KNOWLEDGE BASE

INFERENCEENGINE

KBS

KNOWLEDGEEXPERT

KNOWLEDGEENGINEER

KNOWLEDGEACQUISITION

KNOWLEDGE CODIFICATIONKNOWLEDGE REPRESENTATION

PROBLEMSELECTION

KNOWLEDGEUSER

KN

OW

LED

GE T

ES

TIN

GA

ND

EV

ALU

ATIO

N

USER INTERFACE

EXPLAINATIONFACILITY

SEARCH STRATEGIES

KNOWLEDGE BASE

INFERENCEENGINE

KBS

KNOWLEDGEUSER

MACHINE LEARNINGDB


Knowledge (Management) Systems

• Specialised systems that interact with the organisation’s systems to facilitate all aspects of knowledge processing.

• They have evolved from Knowledge-Based Systems.• Unlike KB systems, KM systems must fulfil the following requirements:

– Supply a conceptual level– Reuse the existing K– Convenient and save adaptation to individual needs– Intuitive understanding– Support of multiple perspectives– Integration of perspectives

The MEMO Architecture (Frank 97)“Multi-perspective Enterprise MOdeling”


Knowledge Management Architecture Model

“Technology Support for Knowledge Management”, Mikael Lindvall et al.


Knowledge Structures: KM vision

Sustaining & Extendinga K-Sharing Culture

FullImplementation

KM Pilots &Measurement

ChangeManagement

KM Tools &Technologies

KMOrganization

CoP Building& Nurturing

KMAwareness

KMStrategy

KM TargetAreas

KMTaxonomy

KMBenchmark

KM – Knowledge ManagementCoP – Communities of Practice

“Knowledge Management – Learning from Knowledge Engineering” – Jay Liebowitz


Knowledge Structures: Semantic Web vision

“The Semantic Web will globalize KR, just as the WWW globalize hypertext” -- Tim Berners-Lee


PERSPECTIVE


(Pre-)Historical Evolution of KM

• Oral Knowledge Transmission– Story-teller– Mankind traditions

• Textual and Graphical Knowledge Transmission– Documents and File Cabinets– Books

• Computer-Based Knowledge Transmission– Email– Intranets and Internet– Magnetic, laser-based ,etc. file record systems– Information Systems– Knowledge Bases


History of Computer-Based KM

• FGKM: First Generation Knowledge Management– focused on the use of technologies to help users to extract knowledge

and share this knowledge within the enterprise.– vision: valuable knowledge already exists.– tools: technology always seems to provide the answer.– purposes:

• enhance the deployment of knowledge into practice.• knowledge integration.

• SGKM: Second Generation KM or “the new KM”– focused on the use of technologies to generate new valuable

knowledge, validate this knowledge and integrate it in the enterprise business processes and business strategies.

– vision: knowledge is something that is produced.– purposes:

• knowledge production and integration.

• Third Generation KM


First Generation Knowledge Management

• Groupware• Information Indexing and Retrieval Systems• Knowledge Repositories• Data Warehousing• Document Management• Imaging• Data Mining


Second Generation Knowledge Management

• Supply-Side vs. Demand-Side KM• The Knowledge Life Cycle• Knowledge Processes• Knowledge Rules• Knowledge Structures• Nested Knowledge Domains• Organizational Learning• The Open Enterprise• Complexity Theory• Sustainable Innovation


FGKM: Groupware or “electronic collaboration”

“Software that supports the ability for two or more people to communicate and collaborate”

(P. & T. Johnson-Lenz, 1978)ALTERNATIVE TECHNOLOGIES

1. Email and messaging2. Group calendaring and scheduling3. Electronic Meeting Systems (EMS)4. Desktop video and real-time data conferencing (synchronous)5. Non real-time data conferencing (asynchronous)6. Group document handling7. Workflow8. Group utilities and development tools9. Groupware services10. Groupware and KM frameworks11. Groupware applications12. Collaborative Internet-based applications and products


FGKM: Information Indexing and Retrieval

• Information = (data, meaning)• meaning: unique or not• Information systems:

– Disordered lists: slow access, impractical.– Ordered lists as Yellow & White pages: dichotomy fast access.– Hierarchical indices: fast access.– Hash tables: instantaneous access.

H(m)=meaningmeaning

1.Data12.Data2…N. DataN Data1, Data2, …, DataN Data1, Data2, …, DataN


∗FGKM: Knowledge Repositories

• Organisational Memory or Knowledge Repository: computer system that continuously captures and analyses the knowledge assets of an organisation. It is a collaborative system where people can query and browse both structured and unstructured information in order to retrieve and preserve organisational knowledge assets and facilitate collaborative working.

• Knowledge-base: case-based, ontology-based, …• Types (Davenport & Prusak 1998):

– External knowledge (e.g. competitive or business intelligence: selection, collection, interpretation and distribution of publicly-held information that has strategic importance)

– Structured Internal knowledge (e.g. reports & documents)– Informal Internal knowledge (e.g. discussion databases)

• Models– Knowledge network model: person-to-person– Knowledge repository model: person-to-repository-to-person.– Hybrid: combination of both.


∗FGKM: Data Warehousing

• A data warehouse is a copy of transaction data specifically structured for querying and reporting.• DataWarehouse is a data storage with all the historical information that was generated by all the

departments of a firm; it is oriented towards complex queries and high rendimiento. A DataWarehouse pursues that any department can access the information of any other department by means of a unique means, as well as obligar all the terms to have the same meaning for all the departments. A Datamart is a data storage related to a department of a firm

• al almacén de datos que reúne la información histórica generada por todos los distintosdepartamentos de una organización, orientada a consultas complejas y de alto rendimiento. Un DataWarehouse pretende conseguir que cualquier departamento pueda acceder a la informaciónde cualquiera de los otros mediante un único medio, así como obligar a que los mismos términostengan el mismo significado para todos. Un Datamart es un almacén de datos históricos relativosa un departamento de una organización, así que puede ser simplemente una copia de parte de un DataWarehouse para uso departamental.

• Tanto el DataWarehouse como el Datamart son sistemas orientados a la consulta, en los que se producen procesos batch de carga de datos (altas) con una frecuencia baja y conocida. Ambos son consultados mediante herramientas OLAP (On Line Analytical Processing) que ofrecen unavisión multidimensional de la información. Sobre estas bases de datos se pueden construir EIS (Executive Information Systems, Sistemas de Información para Directivos) y DSS (Decision Support Systems, Sistemas de Ayuda a la toma de Decisiones). Por otra parte, se conoce comoData Mining al proceso no trivial de análisis de grandes cantidades de datos con el objetivo de extraer información útil, por ejemplo para realizar clasificaciones o predicciones.

• Star Model: data matrix (facts) + dimension matrices (perspectives).• Metadata.


∗FGKM: Document Management

• Document management is the process of managing documents through their lifecycle. From inception through creation, review, storage and dissemination all the way to their destruction.

• The result of a document management system will be an immediate access to information benefiting companies, their partners and their customers:

– Shortened time frames to produce information requested. – Better decisions enabled by accurate, timely and accessible information will improve the quality of work.

• Document management involves:– Authors that create documents, add content, and refine it. – Editors that oversee the documents to ensure that they have relevant content and contain useful search

terms. – Software facilities that enable authors and editors to easily and consistently manage the documents. These

facilities ensure that documents are generated to current digital library standards and so enables better resource discovery.

– Publishing is the process of accepting the authors work, assisting to refine the content, and making the document publicly available.

– Promotion is the process of expose the documents. It involves ensuring that the catalogue itself is well-known and that the documents can be discovered through many avenues.

• Example: The Standard Generalized Markup Language (SGML) is an international standard for the definition of device-independent, system-independent methods of representing text in electronic form. SGML is a meta language, that is, a means of formally describing a language. The Document Type Definition (DTD) defines the metadata elements, and their order, structure, and relationships in the SGML document management solution. The eXtensible Markup Language(XML) defines well-structured documents that conform to a set of rules established by a DTD.


FGKM: The Dublin Core

• The Dublin Core is a set of predefined properties for describing documents.• The first DC properties were defined in Dublin (Ohio) in 1995 and is

currently maintained by the Dublin Core Metadata Initiative.Property Definition

Contributor An entity responsible for making contributions to the content of the resource

Coverage The extent or scope of the content of the resource

Creator An entity primarily responsible for making the content of the resource

Format The physical or digital manifestation of the resource

Date A date of an event in the lifecycle of the resource

Description An account of the content of the resource

Identifier An unambiguous reference to the resource within a given context

Language A language of the intellectual content of the resource

Publisher An entity responsible for making the resource available

Relation A reference to a related resource

Rights Information about rights held in and over the resource

Source A Reference to a resource from which the present resource is derived

Subject A topic of the content of the resource

Title A name given to the resource

Type The nature or genre of the content of the resource


∗FGKM: Imaging

• The capture and storage of electronic information from hard-copy documents.


FGKM: KDD & Data Mining


SGKM: Supply-Side versus Demand-Side

• FGKM: Supply-Side KM(1) “It’s all about capturing, codifying, and sharing valuable knowledge”.(2) “It’s all about getting the right information to the right people at the right

time”.(3) “If we only knew what we know”(4) “Knowledge is something that is there”(5) “We need to capture and codify our tacit and explicit knowledge before

it walks out the door”(6) “The purpose of KM is to enhance the deployment of K into practice”

• SGKM: Demand-Side KM(1) “It’s all about contributing to the knowledge life cycle”(2) “Knowledge is something that we produce in human social systems,

though individual and shared processes”(3) “The purpose of KM is to enhance knowledge production”


SGKM: The Knowledge Life Cycle


“Simplified” Knowledge Life Cycle

KnowledgeClaims

OrganisationalKnowledge

KnowledgeProduction

KnowledgeValidation

KnowledgeIntegration


Alternative KM life cycles (Liebowitz, 2000)

1. Transform Information into Kwlg.2. Identify & Verify Knowledge3. Capture & Secure Knowledge4. Organize Knowledge5. Retrieve & Apply Knowledge6. Combine Knowledge7. Create Knowledge8. Learn Knowledge9. Distribute/Sell Knowledge


Alternative KM life cycles (Liebowitz&Beckman, 2000)

Identify is to determine competencies, sourcing strategy, and knowledge domains.

Capture the existing knowledge is formalized during this phase.

Select consists on assessing knowledge relevance, value and accuracy, and resolve conflicting knowledge.

Store: The knowledge is stored by representing the corporate memory in a knowledge repository with various knowledge schema.

Share: Then, the stored knowledge can be shared and finally applied in making decisions, solving problems, automating or supporting work, job aids, and training.

Create: New knowledge can be discovered (with or without the use of the previous one) through research, experimenting, and creative thinking.

Sell: Apart of applying the knowledge in stage 6, it can be also sell. That’s to say, new knowledge-based products and services can be developed and marketed.

STAGE 1: IDENTIFYSTAGE 2: CAPTURESTAGE 3: SELECTSTAGE 4: STORESTAGE 5: SHARESTAGE 6: APPLYSTAGE 7: CREATESTAGE 8: SELL


Alternative KM life cycles (Marquardt, 1996), …(Marquardt, 1996)

1. Acquisition2. Creation3. Transfer and utilization4. Storage

(Spek & Spijkervet, 1997)1. Developing new knowledge2. Securing new & existing K.3. Distributing Knowledge4. Combining available K.

(Ruggles, 1997)1. Generation:Creation, Acquisition, Synthsis, Fusion, Adaptation2. Codification: Capture, Representation3. Transfer

(Holsapple & Joshi, 1997)1. Acquiring Knowledge: Extracting, Interpreting, Transferring2. Selecting Knowledge: Locating, Retrieving, Transferring3. Internalizing Knowledge: Assessing, Targeting, Depositing4. Using Knowledge5. Generating Knowledge: Monitoring, Evaluating, Producing,

Transferring6. Externalizing Knowledge: Targeting, Producing, Transferring

(Wiig, 1993)1. Creation and Sourcing2. Compilation and Transformation3. Dissemination4. Application and Value realization

(O’Dell, 1996)1. Identify2. Collect3. Adapt4. Organize5. Apply6. Share7. Create

(Dataware Technologies, 1998)1. Identify the (business) problem2. Prepare for charge3. Create the KM team4. Perform the knowledge audit and analysis5. Define the key features of the solution6. Implement the building blocks for KM7. Link knowledge to people

(Van der Spek & Hoog, 1998)1. Conceptualize: Make inventory of existing K., Analyze

strong and weak points2. Reflect: Decide on required improvements, Make plans to

improve3. Act: Secure, Combine, Distribute, and Develop knowledge4. Review: Compare old and new situation, Evaluate achieved

results


The SMART KM life cycle

(Liebowitz, Rubenstein-Montaro, Buchwalter, et al. 2000)

1. Strategize - analysis2. Model - design3. Act - implement4. Revise - test5. Transfer - implantation and update


The SMART KM life cycle: steps

1.1. Perform strategic planning: determine key knowledge requirements and priorities.1.2. Perform business needs analysis: identify business problems and metrics for success.1.3. Conduct cultural assessment and ensure knowledge sharing.

2.1. Perform conceptual modeling: conduct a knowledge audit (list types and sources of K, competencies, weaknesses, organization and knowledge flows, etc.) and do knowledge planning (propose a KM strategy, a K. sharing culture, a cost-benefit analysis, etc.).

2.2. Perform physical modeling: develop the physical architecture.

3.1. Capture and secure knowledge: collect, verify, and evaluate knowledge.3.2. Represent knowledge: define a formal representation “language”, classify knowledge, and encode it in the selected language.3.3. Organize and store knowledge in the KM system.3.4. Combine knowledge: retrieve and integrate knowledge from the entire organization.3.5. Create knowledge: have discussion with customers and interested parties, perform exploration and discovery, conduct

experimentation, etc.3.6. Share knowledge: distribute and make knowledge easily accessible.3.7. Learn knowledge and go to 3.1

4.1. Pilot operational use of the KM system.4.2. Conduct knowledge review: perform quality control (with validity, accuracy, and update metrics) and relevance review (discard

irrelevant K).4.3. Perform KM system review: test and evaluate results.

5.1. Publish knowledge.5.2. Coordinate KM activities and functions: activate action plans for applying knowledge and report where the knowledge is located.5.3. Use knowledge to create value for the enterprise: sell, apply, and use the knowledge.5.4. Monitor KM activities via metrics.5.5. Conduct post-audit.5.6. Expand KM activities.5.7. Continue learning and go to back phases.


∗The SMART KM life cycle: documents and products

1.1. Business needs analysis doc:1.2. Cultural assessment and incentives doc:

2.1. Knowledge audit doc:2.2. Visual prototype:2.3. KM program doc:2.4. Requirements specification doc:

3.1. Knowledge acquisition doc:3.2. Design doc:3.3. Visual and technical KM system prototypes:

4.1. Evaluation methodology and results doc:4.2. KM system prototype:4.3. User’s guide for the KM system:

5.1. Maintenance doc for KM system:5.2. Fully functional KM system.5.3. Post-audit doc:5.4. Lessons learned doc:


Knowledge Audit

Auditing knowledge for a particular target area consists on:“identifying which knowledge is needed and available for that area, which knowledge is missing, who has the knowledge (source), and how it is being used (destination)”.

1. Identify the currently existing knowledge in the targeted area:Determine existing and potential sinks, sources, flows, and constraints.Identify and locate explicit and tacit knowledge.Build a knowledge map of the taxonomy and flow of knowledge.

2. Identify the currently missing knowledge in the targeted area:Perform gap analysis to detect missing knowledge.Determine who needs the missing knowledge.

3. Provide recommendations


Knowledge Auditing Algorithm• What are the categories of knowledge in the targeted area?• Which of them are currently available?• If available,

– How this knowledge is used?– What are the sources of this knowledge?– Who is using this knowledge? How often?– Who are new potential users of this knowledge?– What’s the process or processes to obtain that knowledge?– How is this knowledge adding value or benefit?– What influences this knowledge?– What are the elements that identify, use, or transform this knowledge?– How is this knowledge delivered from? Are there other delivering alternatives?– Who are the experts (in the company) in this sort of knowledge?

• What categories of knowledge do you need to do your work better?• For all of them,

– How much your work can be improved from it?– What are the potential sources of this knowledge?– What are your unanswered questions? For each one,

• What is the sort of knowledge missed?• Which departments/people could answer these open questions?• Which departments/people are looking for similar answers? For each one,

– What level in the organization this department/person has?– If a person, how old is this person in the company?– Why did they ask these questions similar to yours?– Is someone in the organization putting barriers to this sort of KM?– What are the main reasons to make errors/mistakes concerning this knowledge?


Introducing KM practice in an enterprise: actions

1. Obtain management buy-in.2. Survey and map the knowledge landscape.3. Plan the knowledge strategy.4. Create/define K-related alternatives and potential initiatives.5. Portray benefit expectations for KM initiatives.6. Set KM priorities.7. Determine key knowledge requirements.8. Acquire key knowledge.9. Create integrated knowledge transfer programs.10. Transform, distribute, and apply knowledge assets.11. Establish and update KM infraestructure.12. Make knowledge assets.13. Construct incentive programs.14. Coordinate KM activities and functions enterprise-wide.15. Facilitate knowledge-focused management.16. Monitor Knowledge Management.

(J. Liebowitz, 2000)

time


Introducing KM practice in an enterprise: 8-step agenda

1. Develop a broad vision of the KM practice and obtainmanagement buy-in.

2. Pursue targeted KM focus.3. Allow team members to focus full time on KM and

build KM professional teams.4. Install KM impact and benefit evaluation methods.5. Implement incentives to manage knowledge.6. Teach metaknowledge to everyone.7. Ascertain that implemented KM activities provide

opportunities, capabilities, motivations, andpermissions for individuals and the enterprise to actintelligently.

8. Create supporting infraestructure.

(J. Liebowitz, 2000)

relevance


Evaluating the performance of KM in an enterprise

• Knowledge work is the work producedas a result of the use of knowledge.

• Knowledge work metrics:– project management: measures of size,

effort, and duration of a KW project• Productivity: amount of effort required

to produce a KW project of a given size.• Delivery: time required to develop a

KW project.– quality control or defect density:

number of defects or errors in a KW project of a given size.

• The PNR model (Putnam, Norden, Rayleigh, 1963)

– B: skills factor– PP: productivity parameter– MBP: manpower buildup parameter– Effort: person-year units– Size o SLOC: source lines of code /K

Software Cost Estimation Theory

sizeproductdefectsofnumberdensitydefect

weekselapsedsizeproductdelivery

hoursworksizeproduct

inputoutputtyproductivi

=

=

==

71

3

31

3

4

31

1

1

⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜

⎝

⎛

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡⋅

⋅=

=

⎟⎠⎞

⎜⎝⎛⋅

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡⋅

=

PPBsize

MBPtime

timeeffortMBP

timePPBsizeeffort


∗SGKM: Knowledge Processes

• Knowledge processes: any of the processes involved in the KM life cycle.

• Knowledge processing: act of applying some knowledgeprocess (ex. knowledge production or integration).

• Knowledge Management is about an action that seeks tohave an impact on knowledge processing (ex. to designa portal to enhance knowledge sharing).


Knowledge Map

• Representation of the knowledge inside the company• Purposes:

– Generate ideas– Design a complex structure– Communicate complex ideas– Aid learning by integrating new and old knowledge– Assess understanding– Diagnose misunderstanding

• Sorts of knowledge maps:– Organizational Maps– Expertise Maps– Concept Maps


Organizational Maps

They are used to show the interactions between company members.

Albert

Bernard

Charlotte

Donald

EveFrancine

GuyHelen

Ian

JohnKeith

Liz

MaryNora

Oscar

Peter

Marketing Manufacturing

H. ResourcesManagement

Collaborations:CloseDistantIsolationsUnidirectionalHierarchiesEtc.

Ex: by the analysis of the emails/internal callsbetween members inthe company.


Expertise Maps

They are used to show who knows things in the company.

Albert

Bernard

DonaldEve

Francine

GuyHelen

KMKE

DB

DSS

Finances

Project Management

Area 1

Area 2

Dept 1

Dept 2

Charlotte

Marketing

AIExpertise:

Someone/nobodyWho/What dept.Working teamPeople selectionEmployee formationEtc.

Ex: by the analysis of peopleparticipating in projects, papers,Reports, meetings, etc. andTheir role/responsibility in thatactivities.


Concept Maps

They are used to know the relationships between company concepts.Concepts can be: objects, resources, products, etc.

AI KM

DM

KE

DSS

SoftEng

DB ExpSyst


∗Semantic Knowledge Maps

• The links in the Map have a meaning.• Meanings:

– Descriptive LinksC – characteristicP – part ofT – type or subpart of

– Dynamic LinksI – influencesL – leads toN - next

– Instructional LinksA – analogyS – side remarkE - example

EXAMPLE


Constructing Semantic Knowledge Maps

(Newbern & Dansereau, 1993)1. Make a list of important concepts or main ideas.2. For each concept or idea,

2.1. Add a node in the map, labeled with the concept.2.2. Ask the following questions and draw links on the map,

2.2.1. Can this concept be broken down into sub concepts (T-link)?2.2.2. For each sub concept or concept type,

2.2.2.1. What are the features of that type (C-link)?2.2.2.2. What are the important parts of that type (P-link)?2.2.2.3. For a each part, what are the features (C-link)?

2.2.3. What led to the starting node (L-link)?2.2.4. What does the starting node lead to (L-link)?2.2.5. Which things influence the starting node (I-link)?2.2.6. What does the starting node influence (I-link)?2.2.7. What happens next (N-link)?2.2.8. Does anything require an analogy, remark or example (A,S,E-links)?

3. Review the map


SGKM: Nested Knowledge Domains

• Enterprises have different levels of abstraction: the whole company, the departments, the working groups, the individuals, etc.

• Each member of a level can have its own competencies and therefore its own knowledge life cycle.


∗SGKM: The Open Enterprise


SGKM: Organizational Learning (OL)

• introduced by Peter Senge in 1990.• it is “the ability to learn faster than your competitors”.• It is a corporate culture that cherishes continuous

improvement.• SGKM is an implementation strategy for OL.


∗SGKM: Complexity Theory

• Complex adaptive systems (CAS) theory: individuals in a colony self-organize and continuously fit themselves, individually and collectively, to changing conditions in their environment.


∗SGKM: Sustainable Innovation


Summary of Terms in KM

• Balanced Scorecard System (BCS): method of measuring performance of a firm beyond the typical financial measures. Links corporate goals and direct performance measures in a framework specific to a firm, and is one method of measuring the impact of knowledge management. (2)

• Best Practice: those practices that have produced outstanding results in another situation and that could be adapted for our situation. (2)

• Calculated Intangible Value: an "elegant way to put a dollar value on intangible assets" uses a measure of the company's ability to outperform an average competitor that has similar tangible assets as the firm's value of intangible assets. Uses the following steps: 1. Calculate average pretax earnings for three years; 2. Go to the balance sheet and get the average year-end tangible assets for three years; 3. Divide earnings by assets to get the return on assets. 4. For the same three years, find the industry's average ROA; 5. Calculate the "excess return" by multiplying the firm's assets by the industry ROA and subtracting them from the firm's pretax earnings; 6) calculate the three year average income tax rate and multiply it by the excess return, this results in the premium attributable to intangible assets; 7) calculate the net present value of the premium by dividing the premium by the company's cost of capital. (7)



• Collaborative Tools: tools such as groupware that enable both structured and free-flow sharing of knowledge and best practices. An example is Lotus Notes. (2)

• Communities of Practice: aka affinity groups; A) informal networks and forums, where tips are exchanged and ideas generated. (7) B) a group of professionals, informally bound to one another through exposure to a common class of problems, common pursuit of solutions, and thereby themselves embodying a store of knowledge. (8)

• Core Capabilities: A) constitute a competitive advantage for a firm; they have built up over time and cannot be easily imitated. They are distinct from both supplemental and enabling capabilities, neither of which is sufficiently superior to those of competitors to offer a sustainable advantage. (6); B) bestow a competitive advantage on a company . . . distinctive, firm-specific, or organizational competencies; resource deployments; or invisible assets. (6)

• Core Rigidities: refers to the idea that a firm’s strengths are also – simultaneously – its weaknesses. The dimensions that distinguish a company competitively have grown up over time as an accumulation of activities and decisions that focus on one kind of knowledge at the expense of others. Companies, like people, cannot be skilful at everything. Therefore, core capabilities both advantage and disadvantage a company. (6)

• Customer Capital: the value of an organization's relationships with the people with whom it does business, or the value of its [the companies] franchise, its ongoing relationships with the people or organizations to which it sells. (7)



• Enabling Capabilities: necessary [to enter a market] but not sufficient in themselves to competitively distinguish a company.

• Enablers of Knowledge Management: systems and infrastructures which ensure knowledge is created, captured, shared, and leveraged. These include culture, technology, infrastructure, and measurement.

• Experience: refers to what we have done and what has happened to us in the past.

• Explicit Knowledge: formal/codified . . . comes in the form of books, documents, white papers, databases, and policy manuals.

• Human Capital: the capabilities of the individuals required to provide solutions to customers.

• Intellectual Capital: refers to the commercial value of trademarks, licenses, brand names, formulations, and patents.

• Knowledge Interrogators: aka corporate librarian and knowledge integrator; person responsible for managing the content of organizational knowledge as well as its technology. [they] keep the database orderly, categorize and format documents and chucking the obsolete, and connect the users with the information they seek.



• Knowledge Management: A) make an organization’s knowledge stores more accessible anduseful; B) a business activity with two primary aspects: treating the knowledge component of business activities as an explicit concern of business reflected in strategy, policy, and practice at all levels of the organization making a direct connection between an organization’s intellectual assets — both explicit [recorded] and tacit [personal know-how] — and positive business results; C) conscious strategy of getting the right knowledge to the right people at the right time and helping people share and put information into action in ways that strive to improve organizational performance.

• Knowledge Map: representation of the knowledge that exists inside a company.

• Learning Organization or Organizational Learning: term popularized by Peter Senge's the Fifth Discipline meaning a corporate culture that cherishes continuous improvement.

• Market-to-Book Ratio: common method of valuing knowledge intensive companies. Equal to (price per share X total number of shares outstanding) divided by book equity, which is the equity portion of a company's balance sheet.

• Rules of Thumb: shortcuts to solutions to new problems that resemble problems previously solved by experienced workers.



• Signature Skill: an ability by which a person prefers to identify himself or herself professionally.

• Structural Capital: A) legal rights to ownership: technologies, inventions, data, publications, and processes [that] can be patented, copyrighted, or shielded by trade-secret laws. B) strategy and culture, structures and systems, organizational routines and procedures - assets that are often far more extensive and valuable than the codified ones.

• Supplemental Capabilities: those that add value to core capabilities but that could be imitated.

• Technological Capability: term used to encompass the system of activities, physical systems, skills and knowledge bases, managerial systems of education and reward, and values that create a special advantage for a company or line of business.

• Value Proposition: the logical link between action and payoff that knowledge management must create to be effective. Customer intimacy, product-to-market excellence, and operational excellence are examples.


Summary of Relevant Concepts in KM

Organizational Knowledge (OK): knowledge that is shared among organizational members. This includes, knowing which informationis needed (know what), knowing how information must be processed(know how), knowing what information is needed (know why), knowing where information can be found (know where), and knowing when which information is needed (know when).

Organizational Learning (OL): “the ability to learn faster than your competitors”.

Knowledge Map: representation of the knowledge that exists inside a company.

Knowledge Life Cycle (KLC): knowledge processes as acquisition, representation, or validation that interact in order to produce new knowledge.

Knowledge Auditing: identifying which knowledge is needed and available for that area, which knowledge is missing, who has theknowledge (source), and how it is being used (destination)”.

Knowledge Process: SGKM term indicating any of the processes involved in the KM life cycle.


KNOWLEDGE STRUCTURES


KM Modelling

• Ontologies

• CommonKADS• Protégé 2000• UML-OCL• Multi-Perspective Modelling• Others


Ontologies

• Concepts or classes– Properties or slots– Facets

• Relationships– Inheritance

• Hierarchy / Network• Constraints• Instances

ClassProperty

Facet

SuperClass

SubClassProperty

Instance

ConstraintsOntology

Class


∗CommonKADS

http://www.commonkads.uva.nl• CommonKADS is a methodology to support structured

knowledge engineering. It is a European de facto standard for knowledge analysis and knowledge-intensive system development.

• CommonKADS gives tools for corporate knowledge management, provides the methods to perform a detailed analysis of knowledge-intensive tasks & processes, and supports the development of knowledge systems that support selected parts of the business process.

• CommonKADS uses UML notations: use case diagrams, class diagrams, activity diagrams and state diagrams.


∗Protégé 2000

Stanford Medical Informatics, http://protege.stanford.edu• Java-based standalone application.• Knowledge Model:

– Classes: concrete or abstract, hierarchy, multiple-inheritance.– Slots: template or own, hierarchy.– Facets:– Instances:– Meta-classes: classes whose instances are classes.– Forms: screen layouts to edit instances of a class.– Queries: interface for querying the knowledge-base.– PAL constraints:


∗Other ontology development tools

• APECKS• Apollo• CODE4• Co4• DUET• GKB-Editor• KAO• OilEd• OntoEdit• Visual Ontology Modeler• Unicorn• WebODE


Web-based Standards for “Knowledge” Representation

W3C


XML: eXtensible Markup Language

• XML specifies the structure and content of a document.• Extensible: to create a wide variety of document types.• Markup: to increase the description power.• XML is to structure, store and to send information.

Program the Web

XML

Browse the Web

HTMLTCP/IP

Connect the Web

Technology

Innovation

Connectivity Presentation Connecting ApplicationsFTP, E-mail, Gopher Web Pages Web Services


HTML …

• HTML was designed for formatting text on a Web page.• HTML limitations:

– Cannot deal with the content of a Web page.– Cannot be used to describe or to catalog data in the web.– It is not extensible.– “Standard” representation but browser-dependent appearance.

• HTML browsers supporting XML:– Microsoft Internet Explorer 5.0 – Netscape Navigator 6 (option “View Page Source”)

• XML reserved symbols: &, <, >, ’, ”, ;.


DTD-XML car’s example

<?xml version="1.0"?><!DOCTYPE car SYSTEM “car.dtd">

<company ID=“WolksWagen”><country>Germany</country>

</company>…

<car name=“BEETLE” company=“WolksWagen”><model>1500</model><horsepower>

<HPmin>50</HPmin><HPmax>90</HPmax>

</horsepower><production>

<start>1938</start><finish>1989</finish>

</production><production>

<start>2000</start></production><color name=“B”></color><price>

<factory_price>8000€</fectory_price><price><wheels>4</wheels>

</car>

<car>

<!ELEMENT car(model?,horsepower,production+,color?,price,wheels)

><!ATTLIST carname (BEETLE,SEDAN,JEEP,TOPOLINO,…) #REQUIREDcompany IDREF #REQUIRED

><!ELEMENT model (#PCDATA)><!ELEMENT horsepower (HP|(HPmin?,HPmax?))><!ELEMENT production (start?,finish?)><!ELEMENT start_prod (#PCDATA)><!ELEMENT finish_prod (#PCDATA)><!ELEMENT price (factory,retail?)><!ELEMENT color EMPTY><!ATTLIST color name (R|W|B|Y|DARK|OTHER) #REQUIRED>

<!ELEMENT factory_price (#PCDATA)><!ELEMENT retail_price (#PCDATA)><!ELEMENT wheels #PCDATA>

<!ELEMENT company (country?)><!ATTLIST company ID CDATA>


http://www.w3schools.com/default.asp

• eXtensible Stylesheet Language (XSL): transforms XML into HTML before it is displayed by the browser.

• Document Type Definition (DTD): XML document that defines the content structure of other XML documents.

• XML Path Language (XPath): locates information in XML documents.

– Ex. xmlDoc.selectNodes(“//company") selects all the company elements.– Ex. xmlDoc.selectNodes(“//company[0]") selects the first company element.– Ex. xmlDoc.selectNodes(“/car") selects all the elements the first company

element.– Ex. xmlDoc.selectNodes(“/car[color=‘R’]") selects the red car elements.– Ex. xmlDoc.selectNodes(“/car[@name=‘Beetle’]/horsepower/HPmin”) selects the

HPmin element of all the car with attribute name Beetle.


RDF: Resource Description Framework

• RDF was designed for describing resources on the web.• RDF is to be read and understood by computers • RDF is not for being displayed to people • RDF is written in XML

• RDF uses Uniform Resource Identifiers (URIs). • RDF basic concepts: P(R)=V

– Resources: anything that can have a URI.– Properties: Resource that has a name.– Property values: the value of a Property.

• RDF statements: P(S)=O– Subject (S): the resource of the statement.– Predicate (P): the property of the statement.– Object (O): the property value of the statement.

• Example: “The webmaster of http://invented.page is John Smith”Webmaster(http://invented.page)=John Smith

http://invented.page/

http://invented.page)=john/


RDF Elements

• <rdf:RDF> is the root element of an RDF document.• <rdf:Description> is the statement constructor that identifies a

resource with the about attribute and contains elements that describe the resource.

• <rdf:Bag> describes a list of values that is intended to be unordered.Ex. <rdf:Description rdf:about="http://www.old_cars.org">

<car> <rdf:Bag> <rdf:li>Beetle</rdf:li><rdf:li>Sedan</rdf:li><rdf:li>Jeep</rdf:li>…<rdf:Bag>

</car></rdf:Description>

• <rdf:Seq> describes a list of values that is intended to be ordered.• <rdf:Alt> describes a list of alternative values.

Car (www.old_cars.org)=(Beetle, Sedan, Jeep, …)

http://www.old_cars.org/


RDF Schema (RDFS)

• RDFS provides the framework to describe application-specific classes and properties.• Classes in RDFS allows resources to be defined as instances of classes.

Ex. <?xml version="1.0"?>

<rdf:RDFxmlns:rdf= "http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"xml:base= "http://www.old_cars.org/cars#">

<rdf:Description rdf:ID="car"><rdf:type

rdf:resource="http://www.w3.org/2000/01/rdf-schema#Class"/></rdf:Description>

<rdf:Description rdf:ID="old_car"><rdf:typerdf:resource="http://www.w3.org/2000/01/rdf-schema#Class"/>

<rdfs:subClassOf rdf:resource="#car"/></rdf:Description>

</rdf:RDF>

CAR

OLD_CAR

Subc

lass

Of


∗RDFS constructors

• rdfs:subPropertyOf• rdfs:Class• rdfs:Type• rdfs:domain• rdfs:range• rdfs:literal• rdfs:Container• rdfs:ConstraintResource• rdfs:ConstraintProperty• rdfs:seeAlso• rdfs:isDefinedBy• rdfs:label• rdfs:comment


∗DAML


∗OIL


∗DAML-OIL


∗OWL: Web Ontology Language

• OWL is built on top of RDF and written in XML.• OWL is for processing information on the web • OWL was designed to be interpreted by computers and not for being read by people • OWL has three sublanguages

– OWL full OWL syntax + RDF (complete expressiveness without computational guarantees)– OWL DL restricted to FOL fragment (computational complete & decidable reasoning K)– OWL Lite is “easier to implement” subset of OWL DL (hierarchical K)

• Semantic layering– OWL DL ≈ OWL full within DL fragment– DL semantics officially definitive

• OWL DL based on SHIQ Description Logic– In fact it is equivalent to SHOIN(Dn) DL

• OWL DL Benefits from many years of DL research– Well defined semantics– Formal properties well understood (complexity, decidability)– Known reasoning algorithms– Implemented systems (highly optimised)


OWL: Class Constructors and Axioms


OWL: Example

<owl:Class><owl:intersectionOf rdf:parseType=" collection"><owl:Class rdf:about="#Person"/><owl:Restriction><owl:onProperty rdf:resource="#hasChild"/><owl:toClass><owl:unionOf rdf:parseType=" collection"><owl:Class rdf:about="#Doctor"/><owl:Restriction><owl:onProperty rdf:resource="#hasChild"/><owl:hasClass rdf:resource="#Doctor"/>

</owl:Restriction></owl:unionOf>

</owl:toClass></owl:Restriction>

</owl:intersectionOf></owl:Class>

Person Π∀hasChild. (Doctor ∃ hasChild.Doctor)Π


∗KM-CMM

• Knowledge Management Capability Maturity Model.• CMM: According to the Carnegie Mellon University Software Engineering Institute, CMM is a

common-sense application of software or business process management and quality improvement concepts to software development and maintenance. Its a community-developed guide for evolving towards a culture of engineering excellence, model for organizational improvement. The underlying structure for reliable and consistent software process assessments and software capability evaluations.

• The Capability Maturity Model for Software (CMM) is a framework that describes the key elements of an effective software process. There are CMMs for non software processes as well, such as Business Process Management (BPM). The CMM describes an evolutionary improvement path from an ad hoc, immature process to a mature, disciplined process. The CMM covers practices for planning, engineering, and managing software development and maintenance. When followed, these key practices improve the ability of organizations to meet goals for cost, schedule, functionality, and product quality. The CMM establishes a yardstick against which it is possible to judge, in a repeatable way, the maturity of an organization's software process and compare it to the state of the practice of the industry. The CMM can also be used by an organization to plan improvements to its software process. It also reflects the needs of individuals performing software process, improvement, software process assessments, or software capability evaluations; is documented; and is publicly available.

• Intro CMM: http://www.dis.wa.gov/portfolio/tr25/tr25_o2.html• P-CMM: http://www.sei.cmu.edu/cmm-p/• CMMI:

http://bpmtutorial.com/bpm/BPM.aspx

http://www.dis.wa.gov/portfolio/tr25/tr25_o2.html

http://www.sei.cmu.edu/cmm-p/


KNOWLEDGE TECHNOLOGIES


∗KM technologies & tools

Google Directory TOOLS• Brainstorming (10)• Business Intelligence (13)• Classification (18)• Collaboration (31)• Concept Mapping (6)• Data Mining (89)• Information Retrieval (119)• Knowledge Discovery (34)• Online Training Systems (93)• Topic Maps (93)

TECHNOLOGIES• Management Sciences• Artificial Intelligence• Information Retrieval• Organizational Behaviour


∗KM Technologies

• Management Sciences• Artificial Intelligence

– Case-Based Reasoning– Ontology-Based KM– Metadata-Based KM– Knowledge Discovery– Knowledge Acquisition– Data-, Text-, and Web- Mining– Intelligent Agents

• Information Retrieval– Information Retrieval & Extraction– Visualisation Techniques

• Organizational Behaviour


Information retrieval & extraction

IR DBMSImprecise Semantics Precise Semantics

Keyword search SQL

Read-Mostly. Add docs occasionally Expect reasonable number of updatesUnstructured data format Structured data

Page through top k results Generate full answer

• Boolean retrieval: terms in a query are linked together with AND, OR and NOT.• Weighted search: terms in a document can be weighted according to their frequency.• Word stemming: is the process of removing suffixes from words to make common words equal.• Thesaurus: used to broaden the meaning of terms or narrowing it down or finding related terms.• Relevance feedback: use the terms in relevant documents to change the initial user keyword query.• Document vector: description of the contents of a document in the form of a vector in a available-keyword-space in

order to reduce its complexity and to make it easier to handle.– Document indexing: content bearing terms are extracted from the document text.– Weighting the indexed terms: calculate the weight of the indexed terms within the document.

• Recall: percentage of relevant documents retrieved with respect to the total number of relevant documents in the corpus.• Precision: percentage of relevant documents retrieved with respect to the total number of irrelevant and relevant docs.

– Ranking the document: calculate the similarity between the weighted doc vector and the query vector.• Probabilistic retrieval: the probability that a document is relevant to a specific query, is based on the assumption

that the terms are distributed differently in relevant and non relevant documents.• Problem 1. Synonymy: an object can be referred by many words.• Problem 2. Polysemy: words having more than one specific meaning.


Case-Based Reasoning

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Ontology- and Metadata-Based KM

Ontology is taken as the paradigm to structure knowledge

An XML-like language is taken as the paradigm to structure knowledge

Information-Retrieval Based KM

Case-Based KM

Ontology-Based KM Metadata-Based KM

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∗Data Mining

• Corpus• KDD: Pre-processing + Data Mining + Analysis• Sort of data:

– Structured data: databases.– Semi-structured data: HTML-like documents; XML, RDF, OWL, etc.– Non-structured data: Textual documents.

• Data mining technologies– Artificial Neural Networks: non-linear models that learn to make predictions by means of a training process

and which are alike a biological neural net. – Decision Trees/Graphs/Tables: structures in the form of tree/graph/table that represent sets of

decisions.These decisions generate rules for the classification of a data set. – Bayesian Networks: graphical representation of the joint probability distribution for a set of discrete

variables.– Influence Diagrams: Bayesian network extended with utility functions and with decision variables.– Genetic Algorithms: optimizing techniques that use operations as genetic combinations, mutations or

natural selection in evolutionary-based concepts. – Nearest Neighbour: technique that classifies elements in a set of data which is based on the combination of

the classes of the k nearest elements. – Inductive Rules: process by which a set of if-then rules is generated as a generalization of a set of

elements. • http://www.etse.urv.es/~drianyo/teaching/Data Mining.zip• Other sorts of mining: Text mining & Web mining.


Text Mining

• Authority file: list of important words in a domain or area of expertise.• Equation (1): Inverse document frequency (Spark Jones, 1970).• Equation (2): Weight of a term that appears in n out of N documents.• Equation (3): Relative weight of a term being p the probability that the terms

appears in a relevant document, and q the probability that it appear in an irrelevant document.

• Equation (4): Relative weight of a term that appears in r out of R relevant documents, and in n out of N non relevant documents. ε=0,5 is defined to avoid divide-by-zero problems.

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Web Mining: metrics (1) ∗(Dhyani, Ng & Bhowmick, 2002)

• Graph properties– Centrality– Global– Local

• Significance– Relevance– Quality

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• Similarity– Content– Link

• Search– Effectiveness

• Precision• Recall

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Intelligent Agents

• Internal features: Reactivity, pro activity, reasoning and learning abilities, autonomy, social ability

• External features: Communication, co-operation, character, mobility.

• Sorts of agents:– Nature: reactive vs. deliberative.– Use: collaborative vs. interface vs. information & Internet– Technology: stationary vs. mobile

• http://www.etse.urv.es/~drianyo/teaching/Multiagent Systems.zip


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Visualization techniques

• Goals:– exploration/exploitation of data and information – enhancing understanding of concepts and processes – gaining new (unexpected, profound) insights – making invisible visible – effective presentation of significant features – quality control of simulations, measurements – increasing scientific productivity – medium of communication/collaboration


KNOWLEDGE TOOLS


∗KM Tools

• Knowledge capture: Clementine• Knowledge access: AQUAINT• Knowledge mining:• Knowledge summarization:• Knowledge mapping:• Knowledge visualization:• Others:

– Collaborative Filtering: Grapevine– Groupware: Lotus Notes, Open Text, Exchange, Intraspect– Document management: Fulcrum, InQuery, LARS– Test summarization: Prosum, USU Value Base– Brainstorming– Business Intelligence– Classification– Collaboration– Concept Mapping– Data Mining– Database solutions: Wincite, Dataware, Agentware– Information Retrieval: Excalibur, Verity– Knowledge Discovery– Online Training Systems– Topic Maps


Clementine

• www.spss.com/clementine• Visual Programming Interface • builds a discovery model • performs learning task • Uses neural networks and rule induction • Data sources: ASCII file format, Oracle, Informix, Sybase and Ingres• Clementine has many useful facilities:

– Data Manipulation - construct new data items derived from existing ones, and breaking the data down into meaningful sub-sets

– Browsing and Visualization - displaying aspects of the data using interactive graphics

– Statistics - confirming suspected relationships between factors in the data

– Hypothesis testing - constructing models of how the data behaves and verifying them

http://www.spss.com/clementine


∗AQUAINT

• Advanced Question Answering for Intelligence• www.ic-arda.org/InfoExploit/aquaint


Excalibur

• Excalibur RetrievalWare delivers advanced knowledge retrieval solutions for the full spectrum of digital information. Excalibur's semantic networks and Adaptive Pattern Recognition Processing provide highly fault-tolerant fuzzy searching and plain English meaning-based searching for text, and powerful query-by-example searching for multimedia.


Google


EuroSpider

• The EUROSPIDER system is an Information Retrieval (IR) system which searches very large and complex data collections for relevant information. It is a commercial version of the IR system SPIDER,developed by the Swiss Federal Institute of Technology.

• EUROSPIDER can be used in various ways:– 1. as a standalone IR system.– 2. as an add-on to a World-Wide Web server which makes data

collection accessible through a private or public network– 3. added to a commercial database (DB) system to access possibly very

dynamic and structured data.• The EUROSPIDER retrieval system provides advanced Information

Retrieval (IR) functions such as relevance ranking, feedback searches, linguistic document analysis, and automatic indexing. Document analysis and indexing optionally includes fuzzy term matching to cope with recognition errors of OCR-devices.


hTechSight


GATE

• GATE is open source Java software under the GNU library license, and is a stable, robust, and scalable infrastructure which allows users to build and customize language processing components, while mundane tasks like data storage, format analysis and data visualization are handled by GATE. The system is bundled with components for language analysis, and is in use for Information Extraction (IE), Information Retrieval (IR), Natural Language Generation, summarization, dialogue, Semantic Web, Knowledge Technologies and Digital Libraries applications. GATE-based systems have taken part in the all the major quantitative evaluation programs for Natural Language Processing since 1995.


SHOE: Simple HTML Ontology Extensions

• SHOE is intended to provide user agents with easy access to machine readable semantic knowledge on the Web. It does this by adding to HTML a simple knowledge-representation language.

• SHOE provides the ability to:– Define ontologies using HTML, which lay

out classifications and entity relationship rules.

– Create new ontologies which extend existing ontologies.

– Declare entities for both whole documents and for document subsections.

– Declare relationships between entities.– Declare entity attributes.– Classify entities under an “is a”

classification scheme.

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URL="http://www.ont.org/orgont.html"><ONTDEF CATEGORY="Person" ISA="org.Thing"><ONTDEF RELATION="lastName“ ARGS="Person

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OntoBroker

• Core elements: – a query interface for formulating queries, – an inference engine used to derive answers, and – a webcrawler used to collect the required K from the web

• Query construction:– Instance:Class [Attribute ->> value] represents unit of information– FORALL R <- R:C [A ->> v]; all instances of class C with A=v.– FORALL R,R’ <- R:C [A ->> v] * R’:C’ [A’ ->> v’] where * stands for AND,

OR, -> (imply), <- (implied), or <-> (equivalent).FORALL C, O <- C:CAR[Owner->>O;HorsePower->>200] AND O:PERSON[Name->>”John”]


The End


∗Knowledge Validation, Verification, and Testing

• Validation• Verification• Testing automated systems: wizard-oz experiment,

simulation.


∗DM tools

• SPSS - Clementine – http://www.spss.com/clementine/

• Oracle - Darwin– http://www.oracle.com/ip/analyze/warehouse/datamining/

• SGI - MineSet– http://www.sgi.com/software/mineset/

• IBM - Intelligent Miner– http://www-4.ibm.com/software/data/iminer/fordata/

• http://www.kdnuggets.com/software/index.html


KNOWLEDGE ENGINERING


Knowledge Engineering Life Cycle

Purpose: generate K bases and knowledge-based systems

1. Problem selection2. Knowledge acquisition3. Knowledge representation4. Knowledge encoding5. Knowledge testing and evaluation6. If more refinement is required, then go to 27. Implementation and maintenance


The SWOT Analysis

• Methodology for scanning the internal and external environment of a firm or process.

• Used as an important part of the strategic planning process and also for analyzing KM systems.

• Strengths, Weaknesses, Opportunities, and Threads analysis.– Strengths (S): company resources and capabilities that can be used as a basis

for developing a competitive advantage. For example, patents, strong brand names, good reputation among customers, cost advantages from proprietary know-how, exclusive access to high grade natural resources, favorable access to distribution networks.

– Weaknesses (W): The absence of certain strengths may be viewed as a weakness. For example, lack of patent protection, a weak brand name, poor reputation among customers, high cost structure , lack of access to the best natural resources , lack of access to key distribution channels.

– Opportunities (O): new opportunities for company profit and growth. For example, an unfulfilled customer need, arrival of new technologies, loosening of regulations, removal of international trade barriers.

– Threads (T): changes in the external environmental that may represent threatsto the firm. For example, shifts in consumer tastes away from the firm's products, emergence of substitute products, new regulations, increased trade barriers.


The SWOT matrix: strategies

MATRIX Strengths Weaknesses

Opportunities S-O strategiespursue opportunities that are a good fit to the companies strengths.

W-O strategiesovercome weaknesses to pursue opportunities.

Threats S-T strategiesidentify ways that the firm can use its strengths to reduce its vulnerability to external threats.

W-T strategiesestablish a defensive plan to prevent the firm's weaknesses from making it highly susceptible to external threats.


GOAL-ORIENTED METHODOLOGIES


Goal-Oriented

• Decision Making• Semantic Web• Agents


Decision Making in the context of KM

• Decision Trees• Decision Graphs• Decision Tables• Rules• Influence Diagrams• Bayesian Networks


∗Decision Trees

• (Hunt, Marin & Stone, 1966) A decision tree is a tree structure consisting of decision nodes and leaves. Decision nodes specify an attribute to test upon an object, with the arcs out of the decision node specifying the possible values that attribute can take. Each leaf of the decision tree specifies a category in the set of possibledecisions.

• Example:

• Properties:– Intuitive and easy to use, implement, automate, etc.– Production rules equivalent– The replication and the fragmentation problems.

• (production) Decision tree induction: ID3, C4.5, etc.


∗Decision Graphs

• (Oliver, 1993) A decision graph is a generalization of a decision tree having decision nodes, decision leaves, and joins. A join is represented as a set of nodes having a common child.

• Example:

• Properties:– Do not have the replication and fragmentation problems.– Difficult to make it equivalent to decision rules.– Difficult to automate.


∗Decision Tables

• Definición• Extended decision tables• Example:• Guías de práctica clínica


Rules

• IF condition THEN conclusion• Example: if a project on market analysis is required, then make the

project manager to have a marketing profile.• Sorts of rules:

– Production rules: IF condition THEN concept(ex. IF sales > 1$ million THEN 1st_class_seller)

– Association rules: IF (x1,…,xk)=(v1,…,vk) THEN (y1,…,yj)=(w1,…,wj)(ex. IF (sort,seniority)=(1st_class_seller,15 years) THEN salary_incr=15%)– Ripple down rules (RDR): IF condition THEN conclusion EXCEPT RDR

(ex. IF seniority=15 years THEN salary_incr=10% EXCEPT IF sort=1st_class_seller THEN salary_incr=15% ELSE salary_incr=5%)

• (production) Rule Induction: AQ algorithms, CN2, etc.• (use) Inference Engine: forward & backward chainning.


∗Influence Diagrams

• Definition• Node types:

– Utility– Decision– ?


∗Bayesian Networks

• Definition• Bayesian Probability Theory• Example


∗Semantic Web in the context of KM


∗From Syntactic Web to Semantic Web

• What is new with semantics?– Complex queries involving background knowledge

• Find information about “animals that use sonar but are not either bats or dolphins”

– Locating information in data repositories• Travel enquiries• Prices of goods and services• Results of human genome experiments

– Finding and using “web services”• Visualise surface interactions between two proteins

– Delegating complex tasks to web “agents”• Book me a holiday next weekend somewhere warm, not too

far away, and where they speak French or English


∗Agent Architecture

• BDI

communication

KnowledgeBase

KnowledgeReflection

BDI


∗Multi-Agent Systems


Multi-Agent System Engineering

(DeLoach, 1999)1. Identify the sort of agents2. Identify the interaction between these agents3. Define the coordination protocols for each interaction4. Map the actions fired during the conversations into

agent internal components.5. Define the input, flows, and outputs of the agents6. Select the sorts of agents required.7. Determine the physical location of the agents and other

possible parameters of the agents.


∗Multi-Agent Platforms

• JADE


Multi-Agent Knowledge Networks: an example.

H-TechSight


References

• Davenport TH, Prusak L. Working Knowledge. Harvard Business School Press, 2000.• Liebowitz J. Knowledge Management. CRC Press, 2001.• Liebowitz J. Knowledge Management Handbook. CRC Press 1999.• McElroy MW. The New knowledge management. Butterworth-Heinemann, 2003.• Bañares-Alcantara R. KM and AI course. University of Oxford, 2004.• Dhyani D., Ng W.K., Bhowmick S.V. A Survey of Web Metrics. ACM Comp. Surveys

24(4), 2002.

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