Transcript ( Knowledge Management for Learning Organizations ) or Context-Aware, Proactive Delivery of Task-Specific Knowledge Andreas Abecker, Ansgar Bernardi, Knut Hinkelmann Otto Kühn, Tino Sarodnik,

( Knowledge Management for Learning Organizations )
or
Context-Aware, Proactive Delivery of Task-Specific Knowledge
Andreas Abecker, Ansgar Bernardi, Knut Hinkelmann
Otto Kühn, Tino Sarodnik, Michael Sintek
German Research Center for Artificial Intelligence
(DFKI) GmbH, Kaiserslautern
Knowledge Management Group
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The KnowMore Project
–
The KnowMore project was done in the DFKI Knowledge Management Group
as application-oriented basic research in order to promote a better
understanding and tool support for intelligent software solutions in Knowledge
Management and Organizational Learning.
–
The project was funded by the German National Ministry for Research and
Education (bmb+f).
–
KnowMore ran from April 1997 til March 1999, with an extension til October
1999.
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Administrativa
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Introduction
Knowledge Management and Organizational Learning are
emerging paradigms in industry
–
–
–
shorter product life cycles, lean organizational structures, concurrent
engineering efforts, globally dispersed virtual enterprises, enterprise
reengineering, ...
... make knowledge management an urgent need for enterprises
managers are biased towards non-technological issues,
like human resource management, cultural aspects,
organizational changes etc. ...
... which are crucial for KM, anyway
several IT communities recently „discovered“ the area:
workflow systems, CSCW, expert systems, case-based reasoning, intranets,
data mining, document management systems, ...
... are considered to be useful for KM
However, a commonly agreed-upon approach and methodology
is still lacking.
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CURRENT SITUATION
Knowledge Management can be supported by exchange of information
ORGANIZATIONAL LEARNING
Individual Learning
– continued training & experience
knowledge
–
knowledge
socialization
Learning by sharing experiences
– cooperation & observation
management
externalization
information
communication
internalization
IT support
–
Learning through communication
– supply-driven learning
information
– demand-driven learning
storage
retrieval
repository
–
Learning through development of a
knowledge repository
– storing and monitoring lessons
learned
IT people tend to concentrate on either the communication / collaboration,
or the repository aspects (Organizational Memory).
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–
individual
learning
Basically, research on Organizational Memory can concentrate
on knowledge explication, or on knowledge capitalization
–
explication of tacit knowledge:
–
–
–
–
–
the typical expert system approach
[KühnAbecker97]: cost-benefit problems
[Rittel72],[Buckingham Shum 97]: feasibility for “wicked problems”?
[DavenportJarvenpaa+96]: construction and maintenance problems
capitalization on implicit and existing explicit knowledge:
– existing documents and knowledge sources often
severely underutilized
– ease finding, access, and exploitation
– increase utilization potential
The KnowMore approach concentrates on the second goal.
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TWO COMPLEMENTARY APPROACHES
Practical solutions require different degrees of formalization
OBJECTIVES OF AN ORGANIZATIONAL MEMORY
Ensure the utilization of “formal” organizational knowledge:
business rules, design guidelines, standard procedures, ...
... can be formalized to allow automatic processing
–
Enable sharing and reuse of experiences:
lessons learned, best practice reports, case bases, ...
... can be stored as semi-structured electronic documents
–
Ease the exploitation of implicit knowledge, personal knowledge,
and knowledge contained in documents and databases
technical documentation, hypertexts, personal notes, minutes of meetings,
graphics, images, product data sheets, business letters, ...
... must be effectively accessible
How can several kinds of knowledge synergetically interact?
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The KnowMore approach
One solution approach: Knowledge Management oriented on
Business Process Management
BPMS-METHODOLOGY: PRODUCT/PROCESS-PHILOSOPHY [Karagiannis, 1994]
based on
Which products
do we offer?
Strategic
Decisions
Products
created by
How are the
products made?
Processes
Modeling
done by
How are the
processes realized?
What kind of improvement
potential exists?
Information
Technology
Employees
Implementation
Implementation of
the processes
Execution
Finished
business processes
Evaluation
The BPMS Methodology is supported by the ADONIS system
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Company
Knowledge Management adds a new quality to Business Process
Management
–
Strategic
Decision
–
–
Modeling
–
Conventional business process models
represent procedural knowledge
Business Process Management optimizes
efficiency of the whole process
Knowledge Management improves the result
of the process
It focuses on additional aspects:
–
Implementation
Evaluation
–
Execution
Organizational
Memory
Workflow-Management
Systems
Document-Management
Systems
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Internet
GroupwareProducts
Intranet
etc.
–
Modeling
• identification of required knowledge
• analysis of existing knowledge
Implementation
• structuring and recording of knowledge
• strategies for the elimination of
knowledge deficits
• determining the access points
Execution
• context-based information retrieval
• active assistance
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TOWARDS A KNOWLEDGE-MANAGEMENT METHODOLOGY
Business Process Models represent control flow of business activities
THE DFKI PURCHASING PROCESS
Check
Budget
no
Support
Demand
Reject
supp.?
yes
yes
Hardware or
Software?
no
Specify
HW/SW
Details
price
> 800,- ?
no
Specify
Details
Install
HW / SW
Deliver
Goods
yes
appr. ?
Update
Purchasing
Database
Receive
Goods
Send
Order
yes
price
> 800,- ?
Receive
Delivery Note
no
Sign
Invoice
Approve
Demand
no
yes
Hardware or
Software?
yes
Sign
Invoice
Receive
Invoice
Update
Database
Pay
Invoice
Allocate
Inv.no.
Attach
Inv.no.
The main complexity of this process is hidden in few knowledge-intensive activities.
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Specify
Demand
An ideal Knowledge Management system would answer manifold
questions related to a given knowledge-intensive activity
Check
Budget
Specify
Demand
no
Support
Demand
Reject
supp.?
yes
no
Specify
Details
price
> 800,- ?
yes
Install
HW / SW
Specify
HW/SW
Details
yes
no
Sign
Invoice
Approve
Demand
yes
appr. ?
Send
Order
no
Receive
Goods
Hardware or
Software?
Deliver
Goods
yes
–
Update
Purchasing
Database
yes
price
> 800,- ?
Receive
Delivery Note
Are there general guidelines for
Sign
Receivebuying computer devices?
Invoice
Invoice
– Who bought a graphics card
Update
Database
recently?
Pay
– Are there any experiences with
Invoice
card Matrox Mystique?
Allocate
Inv.no.
Attach
Inv.no.
– Can anyone
recommend a good
graphics card?
Which requirements can be derived for an Organizational Memory Information System
which is able to answer such questions?
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no
Hardware or
Software?
OM technology has to face a number of demanding challenges
–
–
predominance of non-formal knowledge representation (text, drawings, ...)
heterogeneity on all levels (domain conceptualizations, kinds of knowledge,
computer systems, storage formats, ...)
–
–
–
active knowledge supply instead of passive information retrieval
self-adaptivity
task-independence
OM technology grows out of an application-driven integration
of enabling technologies.
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CHARACTERISTICS OF ORGANIZATIONAL MEMORY SETTINGS
Enabling technologies cover the whole cycle of
capturing, storage, and utilization of corporate knowledge
How do the pieces fit together?
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SOME OM CONTRIBUTING FIELDS
KnowMore supports knowledge-intensive tasks (KITs) by
active delivery of context-specific information
OVERVIEW
process
support
information processing
and retrieval
domain
ontology
enterprise
ontology
information
ontology
–
–
–
–
KnowMore concentrates on
knowledge-intensive tasks
the supply of relevant information must
exploit domain and context knowledge
in KnowMore the context is given by
the business process
support is based on
formal knowledge descriptions
based on ontologies
the task at hand is performed by the
user
The cooperative model of active support has been realized in a first
demonstration prototype.
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–
•
•
To purchase a graphics card
details need to be specified:
– name
– price
– supplier
Support is provided by
– computation of suggestions
– presentation of relevant
information
• business rules
• supporting documents
• references to
experienced persons
•
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Process state is taken into
account
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Context-specific information supports the user in
knowledge-intensive activities
When the user decides to buy the Matrox Mystique, the system automatically constrains the
set of supporting documents to the subset dealing with this specific product.
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Changes in the process state result in refined support
–
–
The KnowMore system is not a hard-wired approach for exactly the support
described before, but merely provides the representation facilities for easily
building such business-process information assistants.
To this end, the following topics must be addressed:
– Question (I): How are task-specific information needs expressed in order to enable
the system for a context-specific, automatic information delivery?
– Question (II): How do BPM enactment (i.e., the workflow engine) and information
assistant cooperate?
– Question (III): How does the information assistant process information needs,
knowledge descriptions, context information, and background knowledge for
precise information retrieval?
– Question (IV): How are knowledge items in the OM described with respect to the
conceptual structures (ontologies) underlying the domain of application?
– Question (V): What about system design and implementation?
– Question (VI): What about modeling support for knowledge item descriptions and
underlying ontologies?
These issues are discussed in the following parts of the talk.
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How to realize such functionalities?
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Technical realization (I)
A detailed description of Information Needs extends the process model
Task
participant
activity
input/output variables
precondition:
parameters:
}
product_type isa
Hardware
product_type,
specification, price
info agent:
retrieval-agent-1
contributes-to:
product_detail
{post-processing rules}
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– what to do, resources ...
– data flow
extensions
{ Info-Need-1,
Info-Need-2,
...
Info-Need-n
The conventional process model provides context information
– referring to
• process status
• input / output variables
– provides the information
– variables influenced by the result
– govern presentation/computation
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•
•
•
Modeling of knowledge-intensive tasks is integrated with
business process modeling (BPM)
•
•
•
ADONIS tool used for process
modeling
additional variables for data flow
Info Needs are modeled
– currently in the ‘comment’ field
– ADONIS could be adapted by
BOC
•
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a parser translates
into the representation of some
workflow engine
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CREATION OF EXTENDED BPMs AT PROCESS DEFINITION TIME
Preconditions and postprocessing rules of information needs
allow to formulate information-seeking strategies
1. Present formally
derived recommendations
result produced
1a. Offer
supporting
information
2. Present relevant
business rules
3. Provide info about
products
important purchase
or unexperienced user
Decisions are based on
•process context
•KIT variables
•results of previous steps
4. Suggest contact
to competent
employees
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STRATEGY FOR PURCHASING SUPPORT
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Technical realization (II)
The prototype system illustrates the key players in a workflow scenario
(which is an extension to the Workflow Mgt Coalition‘s scenario)
WF Control
Data
invokes
Workflow
Engine
Applications
Worklist
Handler
support
WF Relevant Data
+
Extensions
Information
Agent(s)
Information
Sources
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Pars pro toto:
Attribute editor
The KIT model is processed by the extended worklist handler
workflow activity
Worklist Handler
knowledge agent
invoke application
present variables
to the user
suggest
check preconditions
suggest evaluation
initiate
perform Information Retrieval
user fills variables
support by
display / calculation
changes in parameters
return result
process results
observe changes
We realized a communication model for the assistance by the information agent
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Knowledge-Intensive Task
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Technical realization (III)
The information agent uses formal knowledge to retrieve the
information relevant for the task at hand
Parameters:
• instantiated WF
variables
Ontology
Information
Agent
• specification of the
search heuristic
Result:
Description
Frames of
relevant
information
items
Postprocessing
Information
Sources
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From the extended
modeling of KnowledgeIntensive Tasks
(KIT):
• formal
calculate values of variables
from the retrieved information
message type
• informal
create a suitable presentation by
• grouping
• sorting
• html generation
The presentation in a WWW Browser ensures flexibility
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Post-processing governs the presentation of supporting information
From the extended
modeling of KnowledgeIntensive Tasks
(KIT):
Parameters:
• instantiated WF
variables
• specification of the
search heuristic
info agent
formal
inferences
thesaurus
maps NL to
concepts
Domain
Knowledge /
Thesaurus
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computed values
find
relevant
concepts
retrieve
relevant
information
Enterprise
Model
Information
Model
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Play-together of formal inferences and background knowledge
in the information agent
Purchasing a graphics card example: Find competent employees
•determine parameters of heuristic search
consider direct subconcepts
retrieve personal or group expertise
with:
•determine retrieval constraints
demand urgent?
•if demand urgent: expertise owner
immediately available
•relevant concept expertise
expand concept to
related concepts
map value of variables to
domain concepts
„Grafik-Karte“
>> graphics card
graphics card
+ matrox mystique
+ matrox millenium
+ ...
Domain Knowl. /
Thesaurus
results:
<Sintek | graphics card, ...>
<Noack | graphics card,
network card,...>
<ISG
| ...>
Enterprise
Model
Information
Model
Ontologies with domain-specific relations are traversed using
task-specific search heuristics to retrieve relevant information items
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•test for rule applicability
unexperienced user?
-
-
1. ( hasCompete nce 1 )1
" Search for people directly linked to a search concept."
-
-
+
2. ( hasCompete nce 1 )1 o (isSubFieldOf 1 )
" Look for people competent in some subfield."
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1. (hasCompetence 1 )1
2. ( worksIn 1 )1 o (usesTechnology 1 )1
" Look for people working in a project applying the
technology in quest."
-
3. (hasCompetence 1 )1 o (isSubFieldOf )1
" Look for people experienced in the direct superconcept
of the topic in quest."
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Intelligent conceptual information retrieval:
search heuristics describe how to navigate in the ontologies
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Technical realization (IV)
Information retrieval maps information need descriptions
to knowledge item descriptions
–
[vanRijsbergen89] identified the three basic constituents of intelligent
information retrieval:
– a semantic representation of documents
– a semantic representation of queries
– an inference procedure mapping the latter one onto the former ones
–
logic-based formalisms have a clear semantics and powerful processing
mechanisms which can incorporate background knowledge
–
in KnowMore, stable structures of conceptual domain models (ontologies) are
the reference models, all other representations refer to
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LOGIC-BASED INFORMATION RETRIEVAL
Ontologies organize information models and background knowledge
–
information ontology:
–
–
Information
Ontology
context
–
content
–
–
enterprise ontology:
–
–
–
Enterprise
Ontology
Domain
Ontology
–
kinds of information sources
logical structure
-> relevance propagation
meta properties (reliability, message
type, availability, creation context,
intended usage context)
link to information content
provides usage and creation context
basis for BPMs
enterprise organizational structure
domain ontology / thesaurus:
–
–
–
description of information content
natural language expressions linked to
formal concepts
usually incomplete
We extended standard modeling approaches by the context dimension.
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DIMENSIONS OF INFORMATION MODELING
Simplified example of the three information modeling ontologies
information
ontology
department
group
expertise
employee
personal
expertise
enterprise
ontology
content
information
s-p-o
rule
document
book
article
title
section
keyword
message
type
thing
isa
instance of
uses
object link
part of
domain
ontology /
thesaurus
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company
Knowledge representation requirements in the area of
OM conceptual information retrieval
INTERESTING RESEARCH TOPICS
–
–
–
–
–
–
Distinguish between document models and indexing ontologies
– for document models additional requirements like order of chapters, concepts as
attribute fillers, specific links in hypertexts, „higher-order“ expressiveness for
complex content descriptions
For indexing ontologies: some basic object-centered modeling formalism:
concepts, attributes, instances
Some link to thesaurus information
Possibilities to freely define other relationships equipped with special
inference mechanisms (domain and task specific)
Maybe later: special procedures for vague relationships: „has-to-do-with“
Maybe later: a clearer understanding of indexes and the „is-about“ relation
Maybe later: uncertainty and vagueness
In KnowMore, we dealt with the upper four requirements.
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–
The KnowMore knowledge representation language: OCRA
(object-centered relational algebra)
• classes
from OOP
• relations
from logic programming languages and RDBs
• types
from functional and imperative programming languages
• classes, inheritance, objects, methods
• set orientation
• rules
inherited from OOP
from RDBs
from logic languages
– Designed for the Intelligent Fault Recording project
– Designed for efficient processing on an RDBMS
– Allows special inferences for specific link types
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The KnowMore representation formalism integrates the object-oriented
and the relational paradigms by unifying
The OCRA is strictly typed
human( name
age
father
mother
)
:
:
:
:
string,
number,
human,
human
// string and number are
// built-in classes
man : human()
woman : human(maidenName : string)
Alternative definition of human with set type:
human( name
: string,
age
: number,
children : {human}
)
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Class declarations:
human(
name : string,
...,
competences : {competence} / strength
)
competence(name : string, ...)
ann() // the top class of all annotations
strength : ann(value : string) // e.g. "good", "medium", "bad"
human
name competences
strength
name
good
John
competence
name
English
bad
Mary
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medium
classes
objects
French
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OCRA: Annotations allow complex semantic nets to be modeled
woman(
name = "Mary",
competences = {competence(name = "French") /
strength(value = "medium")}
)
User-defined object identifiers:
man(
name = "John",
competences = {english / good, french / bad}
)
english : competence(name = "English")
french : competence(name = "French")
good : strength(value = "good")
bad : strength(value = "bad")
Note: predicate symbols and function symbols are not
distinguished-they are both class names.
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OCRA: Objects have a textual representation
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Technical realization (V)
KnowMore fits in the WfMC general workflow system architecture
EXTENSIONS TO THE WORKFLOW MANAGEMENT COALITION’S SYSTEM ARCHITECTURE
WF Control
Data
invokes
Business
Process
Model
+
Extensions
for
knowledgeintensive
Tasks
interpreted by
Workflow
Engine
WF Relevant Data
+
Extensions
Applications
Worklist
Handler
support
Information
Agent
Information
Sources
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WF Application
Data
Business Process
Definition Tool
WF Application
Data
WF Control
Data
generates
Enterprise
interpreted by
•units
•employees
•roles
•business
processes
Workflow
Engine
Worklist
Handler
invokes
Applications
WF Relevant Data
DBs & KBs
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Server
Clients
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The client / server architecture builds upon WfMC standards
Extended BPMs and background knowledge allow access
to various supporting information sources
Adonis
generates
Enterprise
interpreted by
•units
•employees
•roles
•business
processes
+ KITs
Competences
Product Data
Sheets
Test Reports
Rules
WF Application
Data
WF Control
Data
Workflow
Engine
Worklist
Handler
invokes
Applications
WF Relevant Data
provide
terminology
Enterprise O.
Information O.
Domain O.
Knowledge
Item
Descriptions
DBs & KBs
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Business Process
Definition Tool
The extended variable blackboard bridges between
workflow applications and KnowMore support
Adonis
generates
Enterprise
interpreted by
•units
•employees
•roles
•business
processes
+ KITs
Competences
Product Data
Sheets
Test Reports
Rules
WF Application
Data
WF Control
Data
Workflow
Engine
Worklist
Handler
invokes
Applications
WF Relevant Data
provide
terminology
Enterprise O.
Information O.
Domain O.
Information
Agent
request infos
provide infos
Knowledge
Item
Descriptions
DBs & KBs
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Excel
Netscape
Java Var. Editor
Inference
Engine
Server
Clients
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Business Process
Definition Tool
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Technical Realization (VI)
KnowMore envisions a comprehensive toolbox
to create the Organizational Memory
ADONIS © — Business
Process Modeling Tool
KIT Modeling Facilities
–
Modeling of the business process using the ADONIS
–
process
support
information processing
and retrieval
domain
ontology
enterprise
ontology
information
ontology
–
Creating the KIT descriptions
–
select from a library of info agents
–
specify relevant WF variables
–
specify search heuristics
(a) Arbitrary knowledge items can be integrated and
manually annotated
–
Knowledge Item
Editor
TCW — Text
Classification
Workbench
Knowledge Management
Research Group
Ontology
Editor
(b) A learning text classification tool automatically
creates meta information for text documents
–
(c) The Ontology Editor employs thesaurus
information to support the construction of ontologies
TRex — Similarity
Thesaurus Generator
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BPM tool (seen earlier)
Two examples: annotating a personal homepage and a conference paper with ontology concepts
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(a) The knowledge item description relates OM content
to concepts from the domain ontology
–
–
–
–
–
The Text Classification Workbench
TCW is trained with manually
categorized example documents
The system learns characteristic
complex text patterns
After training, new documents can be
categorized automatically
This can be applied to all text
documents added to the OM
TCW originated from the READ and
Virtual Office document analysis
projects
Automatic creation of more detailed formal descriptions
will be investigated using information extraction techniques.
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(b) The TCW Tool for learning text classification has been integrated to
automatically create meta information for text documents in the OM
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The TCW automatically suggests to the user ontological categories
as potential indices for text documents
(c) Ontology development: Our approach for acquisition +
maintenance is based on automatic thesaurus generation
–
–
–
thesaurus generator
TRex
documents
–
automatic thesaurus generation
–
similarity thesaurus
–
interactive knowledge
acquisition and update
tool
–
Knowledge Management
Research Group
efficient processing of large document
corpora
extract important terms and relations
based on frequency and co-occurrence
interactive knowledge acquisition
–
–
ontology thesaurus
+ knowledge base
routinely created during work processes
contain relevant terms in task contexts
perform a semantic classification of
identified similarity relations
update knowledge base and ontology
thesaurus
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documents
Documents are a plentiful source of information available
in any application domain
Example from FAKT project:
similar terms to ‘backup’
tape
mount
device not ready
restore
data safety
Example from ‘Die WELT’ Articles
on the German spelling reform
similar terms to ‘Rechtschreibung’
Reform
Kultusministerkonferenz
Duden
Regeln (112 Regeln)
Orthographie
Knowledge Management
Research Group
–
–
–
–
–
In an Organizational Memory it is important to
handle large amounts of knowledge
First results confirm our expectations that
thesaurus generation methods may be
profitably exploited for knowledge acquisition
Even a rough analysis of word frequencies
and correlations ...
... identifies core topics in a new domain
... offers guidance for subsequent knowledge
acquisition
An analysis of term similarities points out
interesting relationships and dependencies
More sophisticated analyses based on
additional knowledge are needed
to separate meaningful from spurious results
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EXPLOITING THESAURUS GENERATION FOR KNOWLEDGE ACQUISITION
documents
- schemata
- stopwords
- non-text
important
terms
(from
ontology)
–
document parsing
term and phrase
generation
generation
parameters
–
construction of
term-context-matrix
analysis of
term-context-matrix
computation of
term-similarities
similarity thesaurus
Knowledge Management
Research Group
term-context
matrix to be
updated
–
TRex can be easily adapted to domainspecific document collections
– specification of document schemata,
stopwords and non-text
– adjustment of term and phrase generation
parameters
TRex offers a variety of techniques for
computing term-similarities
– different term contexts (document, window)
– various weighting schemes
– singular value decomposition of termcontext matrix
– numerous similarity scores
TRex can exploit lists of important terms
(extracted from an ontology)
– for focussing term and phrase generation
– for weighting of term similarities
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The thesaurus generation tool TRex was extended and enhanced
An integrated Ontology/Thesaurus is constructed or updated
semi-automatically from the term associations generated by TRex
newly found
associations
for term t
explanation by known
relations for t from the
Ontology/Thesaurus
t
t
t
t
t
t
t
t
t isa a
t haspart p, p haspart b
t antonym c
t 0.25 d
?
t isa u, f isa u
?
t haspart v, v synonym g
0.65 a
0.47 b
0.33 c
0.31 d
0.26 e
0.19 f
0.17 g
0.16 h
performed
updates
t 0.28 d
t haspart e
(ignore)
Besides building/updating an integrated Ontology/Thesaurus, the identified
term associations can also be used for updating the knowledge base
Knowledge Management
Research Group
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EXAMPLE
TEST 1: DFKI competencies
TEST 2: Computer technology
–
–
–
–
67 documents created by DFKI
knowledge management group
rudimentary ontology of names and
competencies
text-window contexts of size 100
–
2685 documents from a computer
magazine on CD
no previous ontology
–
document contexts
Terms associated with
Unternehmensgedächtnis
Terms associated with
Prozessor
wissen unternehmen
informationsueberflutung
wissensverarbeitung
mitarbeiter
informationstechnische
arbeitsablauf
arbeitsprozess
ug
wissen
unternehmen
intel
mhz
pentium
cpu
amd
cyrix
mmx
k6
kbyte
sockel
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A first evaluation shows the utility of automatic thesaurus generation
techniques for building and maintaining Organizational Memories
The KnowMore ontology editor supports the cumbersome task of
ontology construction & maintenance
An automatically created similarity
thesaurus provides
correlations between terms
–
Terms indicate possible concepts
–
Term correlations indicate possible links
–
The editor visualizes these relations
–
Identification of concepts and
creation of links is done manually
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–
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The user is responsible for the final decision about
concepts and link types
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KnowMore 3/98
–
IT support for Knowledge Management and Organizational Learning is an
emerging, still open research topic.
–
We propose intelligent assistance for knowledge-intensive tasks which is
based on context-sensitive, active information supply.
–
Information supply essentially amounts to a demanding multimedia and
hypermedia retrieval task.
–
We propose ontology-based information modeling with special focus on the
context dimension and information meta properties.
–
Though the framework still provides many basic research questions,
pragmatically designed prototypes already yield promising results.
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OVERALL SUMMARY
The KnowMore research mainly investigated three research areas
workflow-oriented knowledge
management
–
–
•
processes
integrated processing and retrieval
of knowledge
–
–
KnowMore
–
inferencing
& retrieval
ontology/
thesaurus
•
a knowledge item description schema
has been developed
a representation formalism has been
defined and implemented
ontology-based retrieval with search
heuristics was investigated
integrated ontology and thesaurus
–
–
Knowledge Management
Research Group
a model for knowledge-intensive tasks
has been defined
an activation method has been specified
a prototypical approach for the conjoint
construction of a domain ontology and a
thesaurus has been developed
a system for learning a similarity
thesaurus has been implemented
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•
The KnowMore system prototype illustrates key ideas
of a three-layered OM approach
KnowMore realized key concepts of the
– workflow integration for active
support
information processing
and retrieval
domain
ontology
enterprise
ontology
information
ontology
information supply
– handling of informal knowledge
(“documents”)
by reasoning on formal knowledge
descriptions
– interplay of several ontologies and
specific search heuristics
– support tools for knowledge input
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Organizational Memory idea:
process
KnowMore results spawned several application projects
active information delivery,
integrated modeling of processes & information need,
information transfer between different contexts
CLOCKWORK
(EU)
Help Me Find Person
(USU AG)
un-intrusive ontology acquisition,
text categorization, retrieval heuristics
Process representation,
OM realization
ENRICH
(EU)
KONARC
Configuration support
(TELEKOM)
Thesaurus-based ontology construction,
text categorization support
Ontologies,
search heuristics,
information agent
KnowNet
(EU)
ESB
Electronic Fault Recording
(Saarbergwerke)
Representation formalisms,
search heuristics
Knowledge Management
Research Group
KnowMore
Knowledge description formalism,
information ontology,
info agent for push mechanism
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DECOR
(EU)
An OM technology requires further research on all three levels
of the KnowMore conceptual architecture
SOME FUTURE WORK
Towards business-process oriented knowledge management
–
a methodology is missing for integrated business process and knowledge modeling
–
workflow notion: „a smooth transition from ad-hoc cooperative work of humans and
standardized, automated interaction between autonomous information systems“ [DeMichelis et
–
–
al. 97], i.e. more flexible workflow concepts are required to describe knowledge work
Precise-content retrieval for OM knowledge items
–
investigate XML and RDF / Schema for representation of ontologies
–
discuss uncertainty processing in conceptual information retrieval
–
consider metadata & retrieval constraints for
–
ontology mapping for distributed knowledge sources
Continous, self-adaptive knowledge capture and organization
–
exploit document analysis and understanding techniques to automatically extract content and
meta-level descriptions from text documents
–
context-enriched document storage
Agent technology could provide the software basis for an OM middleware.
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The show goes on ...