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To appear in Knowledge Based Systems (Elsevier)' Special Issue
on AI in KM, Vol. 13, 2000. 1. Computer Sciences Corporation
and University of Technology, Sydney, Australia 2. Deakin University,
Australia 3. University of Karlsruhe, Germany
1.
General Ambit of Knowledge Management
Knowledge
Management has emerged as a predominantly management discipline
in the early to mid-90s. The American Productivity and Quality
Centre has identified six common strategic elements [1] among
US firms that have embraced this new field. Two elements are
the formulation of business strategies and the appointment of
Chief Knowledge Officers to better focus on the exploitation
of core intellectual assets by business and Governments, specifically
the need to capitalise on increasingly expensive human resources/process
knowledge to achieve competitive advantage in global procurement
(supply chain management), in product development, in customer
relationship management (CRM) and in value-added services.
It
soon become obvious that to tackle knowledge management well,
contributions from, among others, diversified areas spanning
management, human resources, decision science, marketing, artificial
intelligence and knowledge modelling are needed. For practical
purposes, the guest editors consider knowledge management as
a discipline that encompasses processes and techniques for the
creation, collection, indexing, organisation, distribution,
access to and evaluation of institutional knowledge for improvement
of performance, and more generally, for the exploitation of
intellectual capital, including reuse opportunities. In order
to establish seamless knowledge management processes that cover
the above aspects, cultural and human resource issues should
be considered as well as the development of intelligent (e.g.
knowledge capturing and sharing) systems that enhance the performance
and execution of the ever-increasing, common knowledge-intensive
tasks facing organisations today.
As
for the role and significance of AI in KM, there are two questions
that are commonly encountered by AI researchers moving into
the KM arena. The first question is "After decades of research
in Knowledge Engineering, what exactly is Knowledge Management?
Is it merely another term (or "brand name") for essentially
the same concept?" The business response is a resounding
NO!
While
it would, no doubt, be appreciated that Knowledge Engineering
and Knowledge Management are not disjunctive areas of expertise,
Knowledge Engineering has, by general consensus, a far more
technical focus on knowledge (e.g. representation, organisation,
reasoning, searching etc.). Knowledge Management, in contrast,
is more aligned with the goals of capturing, sharing and reusing
knowledge in an organisation or among organisations. In other
words, techniques developed in Knowledge Engineering are analogous
to "micro" knowledge strategies, whereas approaches
to Knowledge Management are generally considered as "macro"
knowledge strategies for an organisation or organisations. As
such, Knowledge Management projects can proceed without any
Knowledge Engineering efforts (e.g. a people-based KM system),
but the editors are of the view that, ideally speaking, every
Knowledge Management project should embrace some Knowledge Engineering
(or AI or Web-based business rules execution) expertise to (attempt
to) provide the value-added services often needed in knowledge
processing.
The
second common question about AI in KM is "There is still
no AI system that can converse with a human. The technology
is not ready yet. Should one nonetheless attempt to tackle the
even more difficult problems in Knowledge Management?"
The general answer for this question is very simple; namely,
most sophisticated commercial Knowledge Management tools already
embed some form of AI technology: Bayesian reasoning, ontologies,
data mining, intelligent agents to name a few. As a member of
an industrial expert systems team for more than a decade, the
primary guest editor's observation is that the emphasis on developing
fully-fledged AI (or expert) systems has very much shifted.
Due to advances in Web-based technology and component-based
development, there are, in fact, plenty of opportunities for
well-developed/understood AI techniques to be used in various
parts of core business processes e.g. user profiling, personalisation
of human-computer interactions, content management, case-based
retrieval techniques etc.
At
present, three dominant streams of research/applications of
Knowledge Management may be readily identified:
·
The first stream focusses primarily on research into the theory
of knowledge, the knowledge of the firm [2][3][4][5], organisational
culture [6] [7], measurement of intellectual capital [8][9][10]
and learning organisations [11][12][13][14][15][16][17]. These
researchers tackle the theoretical aspects of knowledge management
and some are even challenging Nonaka and Takeuchi's framework
for the socialisation and externalisation of knowledge.
·
The second stream is represented by the work on corporate memories
(also known as organisational memory or organisational memory
information systems) for enhanced decision making. A corporate
memory embraces all forms of institutional knowledge, whether
formally encoded within the current information systems, or
tacit (informal) knowledge used by individuals in professional
practice. (Verbal instructions by supervisors, for example,
are not usually captured at source!) Representative work in
this area includes [18][14][19][20][21] and [22]. This group
has a strong focus on knowledge sharing and on practical applications
of knowledge management.
·
The third stream, with a strong contribution from computer scientists
and artificial intelligence researchers in particular, tackles
the areas of intelligent agents [23][24][25][26][27], ontologies
[28][29][30][31][32][33][34][35][36], and computer-mediated
collaborations [37][38]39][40][41].
It
is interesting to note that researchers from the areas of Artificial
Intelligence, Knowledge Management and Organisational Memory
dominate above streams of research not because of the terminological
definition of these fields, but very much attributed to the
underlying common principles and objectives that these researchers
sought to achieve. As a result, the systems developed and/or
concepts proposed by these researchers focus on (i), organization
of knowledge, (ii), formalization of knowledge, and, (iii),
contexts of knowledge.
With
regard to the third stream, publications to 1999 have shown
a strong bias in favour of techniques for searching Intranet/Internet
information/documents, thereby overlooking, in the editors'
opinion, other potential benefits that AI techniques might deliver
for core knowledge management activities like knowledge discovery
(e.g. mining of interest profile, connecting people of common
interest in an organisation), indexing & representation
(i.e. the issues of re-organising and retiring knowledge), and
knowledge fusion (i.e. combining existing knowledge to generate
new knowledge). Furthermore, very few practical applications
of knowledge management systems have been reported. This special
issue is intended to redress such an apparent imbalance by emphasising
the significance and diversity of knowledge processing in the
corporate management of knowledge. The call for papers for this
special issue has been specifically 'crafted' to attract submissions
from industry and academia that address the above opportunities.
2.
KBS's Leadership Role in Knowledge Management
Prior
to the announcement of this Special Issue, at least three other
journals launched special issues on Knowledge Management:
·
Strategic Management Journal - Vol. 17, Winter, 1996. The title
of this special issue is "Knowledge and the Firm".
· Long Range Planning - Vol. 30, No. 3, June, 1997. Papers
published in this special issue are very much on the theory
of knowledge and the measurement of intellectual capital in
an organisation (i.e. the first stream described above).
· Expert Systems with Applications - Vol. 13, No. 1,
September, 1997. This special issue has a well balanced set
of papers, with a strong emphasis on the use of artificial intelligence
techniques in Knowledge Management.
Furthermore,
a plethora of workshops, symposiums and seminars have been conducted
in the area of Artificial Intelligence, Organisational Memory
and Knowledge Management, including:
·
AAAI Fall Symposium on "AI Applications in Knowledge Navigation
& Retrieval" in 1995
· AAAI Workshop on "Using AI in Electronic Commerce,
Virtual Organisations and Enterprise Knowledge Management to
Re-engineer the Corporation" in 1997
· Conference on "Practical Aspects of Knowledge
Management" (PAKM) in 1996, 1998
· AAAI Spring Symposium on "AI in Knowledge Management"
in 1997
· AAAI Spring Symposium on "Ontological Engineering"
in 1997
· ECAI Workshop on "Interdisciplinary Workshop on
Building, Maintaining, and Using Organisational Memories"
in 1998
· Conference on "Practical Applications in Knowledge
Management" (PAKeM) in 1998, 1999, 2000
· Workshop on "Knowledge Acquisition, Modeling and
Management" (KAW) in 1999
· AAAI Workshop on "Synergies of Knowledge Management
and Case-based Reasoning" in 1999
· ES99 Workshop on "Using AI to enable Knowledge
Management" in 1999
· IJCAI Workshop on "Knowledge Management and Organisational
Memory" in 1999
· AAAI Spring Symposium on "Bringing Knowledge to
Business Processes" in 2000
· Dagstuhl Seminar on "Organisational Memory: An
Interdisciplinary Approach" in 2000
· Workshop on "Intelligent Systems in the Knowledge-Driven
Economy" (ISKDE) in 2000
The
announcement in 1999 of a Special Issue on Artificial Intelligence
in Knowledge Management by the editors of the Knowledge Based
Systems (KBS) journal stimulated considerable interest from
academics and practitioners, reflecting a growing business awareness
of the need to leverage knowledge assets. The need for leadership
in KM arises partly from the multiple interpretations, sometimes
conflicting, which characterised the early experimental models.
More recently, the overarching drivers of global trade dynamics,
asset management and technological change has challenged organisational
responsiveness in new ways, often demanding new business models
better suited to market leadership and trading partner requirements.
While there is general agreement that KM has a significant role
to play in facilitating the design and introduction of new business
processes, the mapping of knowledge processing practices and
corporate memory requirements to business intelligence and competitive
advantage benefits has not, to our knowledge, been attempted
before in a scholarly journal.
Guest
editors for the Special Issue are Eric Tsui (Computer Sciences
Corporation & University of Technology, Sydney), Brian Garner
(Deakin University) and Steffen Staab (University of Karlsruhe).
Eric Tsui is CSC's Chief Research Officer, Asia Pacific. He
has designed and delivered the first KM course for the University
of Technology, Sydney. Brian Garner is the Professor of Computing
at Deakin University and is a member of the United Nations CEFACT
D9/T9 Work Groups. Steffen Staab is a world-renowned researcher
in the areas of ontology and organisational memory. He was also
the coordinator for the AAAI Spring Symposium on "Bringing
Knowledge to Business Processes" held in March, 2000.
3.
Statistics & summary of papers accepted
The
Special Issue attracted 18 submissions in total from the following
countries: Australia, Canada, Germany, Hong Kong, Korea, Portugal,
United Kingdom, United States and Switzerland. This response,
an outstanding international effort, was most gratifying. Each
paper was carefully examined by at least 3 reviewers. Out of
the 18 submitted papers, 5 have been accepted as full papers
together with 4 short papers.
Among
the 9 papers accepted for publication, 3 papers are on the use
of an organisational memory to support business processes, 1
paper addresses an integrated framework that supports business
processes, 2 papers address technologies for intelligent search
agents, 2 papers describe the use of agents in the defense industry,
and there is 1 paper on people finder KM systems. Given that
up to now, publications on AI in KM are typically biased towards
intelligent agents, ontologies and computer-mediated collaborations,
it is especially pleasing to see the compositional balance of
the papers and the strong application focus shown by all the
papers in this Special Issue.
Abecker,
Bernardi, Maus, Sintek and Wenzel have demonstrated an integrated
framework that encompasses document analysis, workflow, and
knowledge modelling to support knowledge-intensive business
processes. In their KnowMore project, relevant and goal-specific
information is automatically delivered to the knowledge workers.
Staab and Schnurr discuss a desk support agent that predicts
the critical knowledge needs in a loosely structured business
process. They have illustrated a very good example of the use
of an organisational memory to support business processes. Reimer,
Margelisch and Staudt have, on the other hand, outlined their
EULE system that is basically an organisational memory information
system (OMIS) that maintains and ensures that relevant business
rules are applied in the various tasks carried out by office
workers in an insurance company. Boury-Brisset and Tourigny
outlines an organisational memory, that with the use of case-based
reasoning techniques, supports the overall goal of road safety
analysis.
AI
and KM have a strong role to play in the defense arena too.
Liebowitz, Adya, Montano, Yoon, Buchwalter, and Imhoff elaborated
a Multi-Agent system for coordinating defense procurement and
contracting. Lang and Burnett's paper is on a health intelligence
system that uses metadata to analyse, manage and merge large
sets of documents in support of Australian defense operations.
Li,
Zhang and Swan's paper is on information filtering techniques
to classify new documents on the WWW, based on user needs. A
separately-developed and simpler variant of the system has been
customised to locate, match and retrieve job vacancy data from
prominent Australian Job sites. Pierre, Kacan and Probst, on
the other hand, addresses an intelligent search agent that,
by considering individual interest profiles, integrates the
needs of multiple users in conducting its search and delivers
customised responses to users concurrently.
While
most KM tools on the market overlook the importance of connecting
people, by focusing almost entirely on searching information/documents
on Intranets/WWW, Becerra-Fernandez's paper has made a distinctive
contribution to the field by identifying the role of Artificial
Intelligence in several people finder KM systems.
This
Special Issue concludes with an extensive bibliography on AI
in KM and various KM applications. It is hoped that the bibliography
will serve as a starting point for researchers and practitioners
interested in furthering the role of AI in KM, and KM applications
in general. This is a subset of the full bibliography maintained
by Dr. Tsui.
4.
Future intelligent systems for Knowledge Management (KM)
Contemporary
progress has been reported in knowledge processing, and more
generally artificial intelligence (AI), in building institutional
memories as the basis for business intelligence (competitive
advantage) and for leveraging knowledge assets in large organisations.
Functional KM roles have been identified in intelligent workflow
applications, in collaborative filtering during information
retrieval, in knowledge integration processes (cf. Intranets/Extranets)
and in the use of intelligent agents for knowledge discovery.
From
an academic perspective, new paradigms are now required to meet
the exacting requirements demanded by business and Government
in the following areas:
·
Human-computer interaction e.g. in natural language understanding
(particularly in diverse cultures), real-time mining of user
profiles and various personalisation strategies. Significant
work is still required to integrate information from diverse
sources and in the seamless presentation of combined results
(e.g. assets, people contacts, interest profiles, activities
log etc.) derived from searching corporate intranets and open
source material (e.g. Web sites).
· Automatic categorisation and indexing of (Web-based)
documents (i.e. the so called "Semantic Web") is of
immediate concern for Knowledge Management and a hot research
focus for AI. The necessary techniques that need to be developed
range from knowledge structuring (knowledge engineering, semi-automatic
acquisition of knowledge structures, ontologies in particular,
intelligent information integration, emergent ontologies), to
knowledge representation on the Web (i.e. formats like RDF,
extensible and integrable frameworks)
· The above research will, in turn, spawn development
of integrated tool environments for ontological engineering
(e.g. knowledge fusion processes), for intelligent indexing
mechanisms and for organisational learning.
· The link between Data Mining/Knowledge Discovery (DM/KD)
and Knowledge Management. Although there is plenty of research
on Data Mining/Knowledge Discovery and Knowledge Management,
the "link" between DM/KD and KM is very much under-explored.
For instance, from the perspective of a researcher in DM/KD,
the end result is, typically, the findings of (a series of)
experiments. This is too often seen as the "finishing line"
for the DM/KD researchers, as evidenced by DM/KD publications.
However, in the corporate environment, such findings are then
communicated to business analyst(s) and, upon further analyses,
often result in the creation/alteration of business rule(s).
In the entire process (i.e. from data gathering to data mining
to the encoding of business rules), there are lots of intermediate
decisions being taken by the DM/KD researcher(s) as well as
the business analyst(s). Such decisions and the context (e.g.
situational factors, assumptions, justifications etc.) in which
these decisions were made are rarely recorded. The result -
organisations often cannot even reproduce the same result even
if there is no change to the data or the staffing! Current commercial
AI/KM systems typically fail to support context. Different implementations
of context-tracking mechanisms are needed in order to (actively)
provide the right knowledge to the right person at the right
time - the so-called "active corporate memory" [19].
In the longer term, intelligent and dynamic model generation
systems are definitely needed to bridge the gap between expert
systems, decision making and data mining tools.
· CRM processes, based on Internet/Mobile Commerce business
models, are increasingly the starting point for enterprise modelling.
AI researchers are thus faced with new challenges arising from
the holistic approach required, rather than the current undue
reliance on individual techniques and methods.
5. Acknowledgments
The
guest editors would like to thank Professor Ernest Edmonds,
General Editor of Knowledge-Based Systems, for reviewing and
approving this Special Issue, and also, Dr. Linda Candy, Managing
Editor of Knowledge-Based Systems, for her valued support, advice
and encouragement at all stages of the paper submission/review
process and in relation to the production logistics.
Special
thanks also go to the following reviewers who have contributed
their valuable time to assist in the review process, as well
as providing constructive comments on all submissions: Andre
Spijkervet, Robert de Hoog, Norman Foo, John Davies, Dickson
Lukose, Mark Maybury, John Debenham, Setsuo Ohsuga, Chengqi
Zhang, Rose Dieng, Gertjan van Heijst, Geoff Webb, Doug Skuce,
Asun Gomez Perez, Rudi Studer, Daniel O'Leary, Klaus-Dieter
Althoff, Ian Watson, Brigitte Bartsch-Spörl, Hugo Zaragoza,
Dieter Fensel, Heinz-Jürgen Müller, John Edwards,
Stefan Decker, Andreas Abecker, Ulrich Reimer, Jay Liebowitz,
Wayne Wobcke, John Edwards, and Andries P Engelbrecht.
Without
their support, it would not have been possible to compile this
Special Issue!
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