Document

LSO 2002 1

Technology Support for Knowledge Management
Mikael Lindvall1, Ioana Rus1, Sachin Suman Sinha2
1

Fraunhofer Center for Experimental Software Engineering, College Park, MD20742, USA
{mikli, irus}@fc-md.umd.edu
2 Dept. of Computer Science, University of Maryland at College Park, MD20742, USA
sachin@cs.umd.edu

Abstract. Human capital is the main asset of software organizations.
Knowledge has to be preserved and leveraged from individuals to the
organization. Thus, the learning software organization will be able to
continually learn and improve. Knowledge management has various
components and multiple aspects such as socio-cultural, organizational, and
technological. In this paper we address the technological aspect; specifically,
we survey the tools available to support different knowledge management
activities. We categorize these tools into classes, based on their capabilities and
functionality, and show what tasks and knowledge processing operations they
support.

1. Introduction
Software organizations have realized that a large amount of problems are attributed to
un-captured and un-shared product and process knowledge. Specifically they have
discovered the need of knowing ‘who knows what’, the need for distance
collaboration, and the need for lessons learned and best practices. This has led to a
growing call for knowledge management (KM). Knowledge sharing and
organizational learning can happen ad-hoc. It is, however, more efficient if organized.
The amount of information and knowledge that needs to be captured, stored and
shared, the geographic distribution of sources and consumers, and the dynamic
evolution of information makes the use of software tools not an option, but a
necessity. In this paper we characterize and classify software tools that support KM
using two models, 1) knowledge characteristics (the knowledge sharing model) and
2) the evolution of knowledge (the knowledge life cycle model).

2. Knowledge Sharing and Knowledge Life Cycle Model
The knowledge-sharing model is also called the ‘tacit-explicit model’ (Nonaka
and Takeuchi, 1995). Tacit knowledge is knowledge that rests with the employee and
explicit knowledge is espoused knowledge. Conversion of knowledge from one form
to another occurs frequently and often leads to the creation of new knowledge.

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Figure 1: The Tacit-Explicit Model (Nonaka and Takeuchi, 1995)
Explicit-explicit knowledge conversion or ‘Combination’ is the reconfiguration of
explicit knowledge through sorting, adding, combining and categorizing. Explicittacit knowledge conversion or ‘Internalization’ takes place when one assimilates
knowledge acquired from knowledge items. Tacit-explicit knowledge conversion or
‘Externalization’ involves transforming quiet personal knowledge to espoused
knowledge that can be either recorded or unrecorded. Tacit-tacit knowledge
conversion or ‘Socialization’ occurs by sharing experiences, working together on a
team, and by direct exchange of knowledge. A knowledge management system
should support all four types of knowledge conversion.
The knowledge lifecycle (Wiig, 1999) takes the path of knowledge
creation/acquisition, organization/storage, distribution, application/reuse and finally
concludes with creation/acquisition again. Tacit knowledge has to be made explicit in
order to be captured. This is accomplished with the aid of knowledge acquisition tools
or knowledge creation tools. Knowledge acquisition evolves and builds an
organization’s knowledge base. Knowledge organization/storage refers to activities
by which knowledge is organized, classified and stored in repositories. Explicit
knowledge needs to be organized and indexed for easy browsing and searching. It
must be stored efficiently to minimize storage space. A variety of tools have been
developed to distribute or deploy knowledge. Knowledge can be distributed through
various channels such as training programs, automatic knowledge distribution
systems and knowledge-based expert systems. Knowledge application is the process
through which knowledge becomes the basis for further learning and innovation.
Applying knowledge is the payoff for knowledge management. A knowledge
management system should support the entire knowledge flow in the knowledge life
cycle.

2

Software Tools for Knowledge Management

KM should be supported by a collection of technologies for authoring, indexing,
classifying, storing, and retrieving information, as well as for collaboration and
application of knowledge. A friendly front-end and a robust back-end are the basic
necessities of a software tool for knowledge management. Figure 2 shows a layered
knowledge management system architecture based on (Lawton, 2001).

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The lowest layer handles sources of explicit knowledge. Explicit knowledge
resides in repositories as documents or other types of knowledge items (e.g., e-mail
messages, and database records). Standard authoring tools (such as word processors)
and database management systems (DBMS) support this layer. File servers, e-mail
browsers etc. support the infrastructure layer. Document and content management
tools with features for search and retrieval as well as analysis and maintenance
represent knowledge repositories. The organization of knowledge is based on a
corporate taxonomy and serves as a “knowledge map” supported by classifying and
indexing tools. KM services are provided using tools for data and knowledge
discovery and collaboration services. Through portals, knowledge can be distributed
to different users and applications, such as e-learning, competence management,
intellectual property management, and customer relationship management.
Business Application
Layer

E-Learning
Management

Competence
Management

Personalized
Knowledge Gateway
KM Services

Intellectual Property
Management

Customer Relationship
Management

Knowledge Portal
Data and Knowledge Discovery

Collaboration Services

Expert Networks

Organizational
Taxonomy

Knowledge Map

Document and
Content Managemnt

Knowledge Repositories

Low level IT
Infrastructure

Web Browsers, Word Processors, E-mail Browsers, File Servers, DBMS, Multemedia Generators,
Messaging Tools, and Internet/Intranet Services

Information and
Knowledge Sources

Bulletin Boards/
Newsgroups

Databases

Electronic
Documents

E-mails

Multimedia
Files

Logged
Chats

Figure 2: KM Architecture Model
It is hard to draw the line between information technology (IT) and tools for KM.
In the architectural model above, we consider KM to be the higher layers, starting at
the “knowledge repository” level, and IT constituting the lower layers, but this
boundary is fuzzy. We concentrate on the upper layers of the architecture and analyze
tools from a knowledge management perspective and discuss the needs of employees
that use knowledge, the types of knowledge conversion that occur and a common set
of features provided by tools from each category. The kinds of tools we analyze are
those that are widely used for supporting the KM layers of the above architecture.
This survey is not intended to cover all kinds of tools on the market, but to give the
reader a sense of the various tools that are available for knowledge management.

2.1 Document and Content Management
In terms of knowledge management, the documents produced by organizations
represent their explicit documented knowledge. New knowledge can be generated
from documents; for example, de facto experts can be identified based on authors of
documents. Document management systems enable explicit-to-explicit knowledge
conversion. One could argue that a form of tacit-to-explicit knowledge conversion

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occurs when experts are determined based on the documents they authored. Expert
identification certainly results in knowledge creation.
Common needs that arise in a document-sharing environment are related to
identifying the latest version of a document, accessing documents remotely and
sharing the documents in workgroups. Document management systems offer features
that include storing/uploading of documents/files; version control; organization of
documents in different ways; search and retrieval based on indexing techniques and
advanced searching mechanisms; and access from any Internet-connected
workstation. Most document management systems also provide some kind of search
for experts based on authorship.
Document management systems can aid learning software organizations that need
to capture and share process and product knowledge. Most artifacts guiding a
software project and being developed during a software project can be represented as
documents and are the main explicit assets of the software organization. Document
management systems thus help support the core business by managing these
invaluable assets and enable transferring knowledge from experts to novices.

2.2 Collaboration Services
Delivering the right information at the right time is one of the major objectives of
knowledge management. To achieve this, employees need to collaborate and
communicate, especially when people work in an environment that is distributed in
time and space. The knowledge conversions supported by this category of tools are
mainly tacit-to-tacit, which occur, for example, when two or more users communicate
using a chat tool or an instant messenger. One can argue that since the conversation
is in an electronic form, a form of tacit-to-explicit conversion also takes place. Some
tools make an effort to capture this conversation so that it can be published and used
for other users. Some even analyze the conversation in order to create new
knowledge, which strengthens the argument for a tacit-to-explicit knowledge
conversion. The basic functionality of tools in this category is to connect employees
by providing a computer-based communication channel. This communication can be
synchronous or asynchronous. Collaboration in terms of chatting or white-boarding
using a chat tool or a messenger tool would be an example of the former, while email, bulletin boards, and newsgroups would be examples of the latter. A common
practice is to use a tool to share a document in real-time so that two or more
geographically distributed groups can see and hear the same presentation. Some tools
are designed to capture communication and work results for further use and
refinement. Some tools are designed to support concurrent co-authoring of documents
over a distance. Other tools support active knowledge sharing in the form of elearning. Some tools support eWorkshops in on-line moderated meetings between
expert participants. The results of the workshop are captured and analyzed in order to
generate new knowledge in a particular area. This illustrates that technology and
process can be used to bring people together and generate new knowledge.
Distance collaboration is the need of the hour for learning software organizations.
Due to globalization, software development working groups are often spread out

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geographically and work in different time zones. The outsourcing of subsystems to
subcontractors also results in these geographically distributed teams working
together. Collaboration tools will help these groups to communicate, collaborate, and
coordinate independent of time and place.

2.3 Data and Knowledge Discovery
The goal of this category of tools is to generate new knowledge from existing data,
information, and knowledge bases. Examples of tools include visualization and data
mining, as well as analysis and synthesis tools. Data mining tools try to reveal
patterns and relationships between data and generate new knowledge about the
underlying data and what it represents. Knowledge management tools often deal with
raw data and singular data points, yet in order to create theories, these data points
need to be analyzed. Raw data and singular data points are, for example, documents,
frequently asked questions, lessons learned, and other knowledge items stored in
knowledge bases. Data and knowledge discovery can reveal what is hidden in the
data. Such tools can be used to identify patterns in the content and usage of
knowledge. Knowledge discovery also identifies groups of users active, inactive, and
de-facto experts. Such tools can also be used to analyze knowledge bases in order to
generate more complex knowledge items, for example when deriving best practices
based on lessons learned and frequently asked questions. The knowledge conversion
that takes place as a result of data and knowledge discovery is, first and foremost,
explicit-to-explicit due to the fact that all knowledge in the knowledge base is already
explicit and the discovery process creates new explicit knowledge based on it. One
could also argue that a portion of the explicit-to-tacit knowledge conversion occurs in
the process when the analyst looks at the data from different perspectives and gains a
better understanding of it.
Typical features of the tools in this category provide for visualization of data in
different ways. Features for statistical analysis are also common, along with different
features for decision support. These features are sometimes based on artificial
intelligence (AI) techniques that can help in the discovery process. Some tools
analyze multimedia content and transcribe it into text, identify and rank the main
concepts within it, and automatically personalize and deliver that information to those
who need it. Delivery can occur across the Internet or by using other digital channels
such as mobile phones, handheld computers, etc. Other tools are used for organizing
and analyzing knowledge and experience bases. Taxonomy editors enable
classification managers to develop taxonomies for the experience base. Some tools
connect to existing knowledge and experience bases and visualize their content.
Analysis tools can be used to analyze growth trends of knowledge and experience
bases, and for other related analyses.
The discovery of best practices and lessons learned is very important for learning
software organizations. Software engineering has been advocating this for a long
time. Software development teams work on similar kinds of projects without realizing
that results would have been achieved more easily if they followed a practice adopted
by a previous project.

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2.4 Expert Networks
Expert networks provide a forum for people who need to establish knowledge sharing
focused on solving a problem. Expert networks are typically based on peer-to-peer
support. These kinds of systems aid geographically distributed organizations. Expert
networks often emphasize the importance of acknowledging that most knowledge
cannot be made explicit and stored in a computer, but will reside in the brains of
experts. Peer-to-peer support is tacit-to-tacit when experts use a chat tool to
communicate, but it is tacit-to-explicit when solutions are stored for future use and
reference. One could also argue that an explicit-to-tacit conversion occurs when
someone finds a solution to a problem in the knowledge base of stored solutions and
applies it to solve their task. Common features are expertise brokerage and expert
identification. Other features are communication and collaboration between people
and the capture of questions and answers. These tools typically track and rate
expertise, customer satisfaction, and rewards that are handed out to people who
contribute to the success of the system. Expert networks aid learning software
organizations. Software development is a human and knowledge intensive activity
and most of the knowledge is tacit and will never become explicit. It will remain tacit
because there is no time to make it explicit. In this scenario Expert Networks can
provide a solution as they can reduce the time spent by software engineers in looking
for specific domain knowledge.

2.5 Knowledge Portals
Knowledge workers use many different computer-based information sources (sales
results, manufacturing activities, inventory levels, and customer orders). These
information sources need to be integrated and accessed through a common, yet
personalized, interface. Portals create a customized single gateway to a wide and
heterogeneous collection of data, information, and knowledge. They also provide
different kinds of personalization so that content is presented in a manner that suits
the individual’s role within the organization and reflects personal preferences. Both
the organization and the user can control what information is made available and how
it is displayed. Portals pull information together from different sources and display it
in a coherent way, performing an explicit-to-explicit knowledge conversion. Portals
support knowledge distribution, as well as organization of the display of information.
Employees make decisions every day, but not all of them are informed. When
critical data is hard to find, or takes too long to locate, it will not be available when it
is needed for making a decision, and making the best decisions requires current and
relevant information, which is what portals deliver.
Like other organizations, software organizations can benefit from portals. A study
found that people in software organizations spent 40% of their time in searching for
different types of information related to their projects (Henninger, 1997). With
portals providing a one-stop shop for all the information that an individual wants, the
time spent looking for information can be significantly reduced.

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2.6 Customer Relationship Management
A popular area of knowledge management is customer support. There are mainly two
forms of customer support tools: tools that enable customers to help themselves (selfhelp) and tools that help customer support personnel (help-desk). In some cases,
vendors even set up areas for customers to help each other, i.e., to share knowledge
about products and services (peer-to-peer). Customer support personnel might lack
appropriate knowledge and consistency to deliver 24/7/365 support. This can be
offset by systems that assist them with knowledge and support process, continuously
and consistently, while they gain appropriate knowledge through experience. There
are many cases where a high repeatability in the support process can be leveraged by
reusing answers to the most common questions. Over time, support personnel also
acquire a vast amount of knowledge about the products and services the organization
offers, as well as information about customers and their behavior. This knowledge is
a resource for the organization as a whole and should be captured and spread. The
knowledge conversation that takes place in customer support is mainly tacit-to-tacit,
but with customer support systems that use knowledge bases it is possible to turn the
process first into tacit-to-explicit, and then explicit-to-explicit, conversion. When
customers search for (and later apply) knowledge, one can argue that explicit-to-tacit
knowledge conversion takes place.
Applications for customer support are often based on knowledge repositories and,
therefore, support the entire knowledge life cycle. Systems that support help desks
typically have features that automatically direct customer requests to representatives
based on profiles of the customers, as well as on the expertise of the representative.
Past customer behavior and connections to product catalogs are other factors that can
assist in the helping process. Support for self-help is often provided out of a website.
Knowledge bases typically provide an interface to capture new knowledge about the
products, services, and their use so that new cases, new incidents, and new lessons
learned can be captured and shared. On-line customer support often links the selfhelp with the help desk through live chat and software systems that are capable of
answering questions.
Software organization can greatly benefit from customer support systems. Most big
organizations have already employed customer support systems to track bug reporting
and crash histories of software. This helps in maintenance, version update and
customer support for the users of that software.

2.7 Competence Management
Along with document management (DM), we view competence management (CM) as
one of the most basic KM activities. If DM deals with the organization’s explicit
knowledge assets, then CM handles its tacit knowledge. Organizations need to
develop ‘knowledge maps’ in terms of where knowledge items are and who knows
what. Once such a knowledge map is in place it can be used to identify appointed and
de facto experts; to staff new projects based on skills and experience required; to

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identify knowledge gaps that indicate the need to hire new people; or to develop
training programs. Knowledge maps can also be used to identify core competencies
for organizational marketing. Tools that support competence management become
necessary, especially for large organizations, where people do not know each other.
Their necessity also becomes obvious in any distributed, decentralized, and mobile
organization. The knowledge transformations supported by these tools are mostly
‘explicit-to-explicit’ because they are based on repositories in which information
about knowledge possession is stored. One can argue that tacit-to-explicit knowledge
conversion takes place when people create profile about their own knowledge. One
can also argue that knowledge creation takes place when the CM system analyzes the
stored information and generates knowledge maps showing who knows what, or what
competence the organization has or does not have.
A typical feature is profiling. Profiles of employees, customers, subcontractors,
vendors, partners, projects, and positions can be generated, which also leads to
identification of and searches for experts. Some tools automatically create
competence profiles by mining various sources of information. Profiling mechanisms
extract terms and phrases from e-mail communications and documents produced or
shared by individuals. Each user profile provides a detailed index of an individual’s
knowledge, experience, and work focus. A set of profiles, therefore, represents a
composite ‘snapshot’ of the expertise within an organization. Competence
management addresses a very important problem of learning software organizations,
‘the need of knowing who knows what’. Much knowledge can be recorded for other
organizations, but nevertheless the assets of a software engineering organization are
mainly its employees and their tacit knowledge. As a knowledge intensive industry,
software organizations are heavily dependant on tacit knowledge, which is very
mobile. Competence management can help build structures and frameworks for
capturing key information that can help retain some knowledge when employees
leave or become unavailable.

2.8 Intellectual Property Management
Knowledge management often includes management of intellectual property such
as patents, copyrights, trademarks, and service marks. Organizations that own
intellectual property need ways to automate workflow and support the management
and analysis of inventions, patents and related matters. It often takes a long time to
file and obtained approved rights to intellectual property, and organizations need
support to track this process, more so for international organizations. Intellectual
property regulations require owners of copyrights, trademarks, and service marks to
pay legal fees at specific points in time; otherwise the rights can be lost. For licensing
issues, it is also important to track licensees and royalties. Another aspect of
intellectual property is the protection of digital content covered by copyright.
Intellectual property management is mainly an explicit-to-explicit knowledge
conversion. It is based on knowledge repositories and, thus, deals with all aspects of
knowledge storage, organization and knowledge distribution in a controlled way.

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Typical features include search for patents capabilities, support to file for patents,
searchable knowledge bases with rules and regulations and support for legal help, as
well as collections of forms and standard letters. Other related issues that these tools
support are licensing of patents and tracking of licenses, as well as calculation of fees.

2.9 E-Learning Management Systems
Knowledge management aims to help people acquire new knowledge, as well as
package and deliver existing knowledge through teaching. E-learning is a relatively
new area that includes computer-based and on-line training tools. E-learning is
appealing because it offers flexibility in time and space, as well as collaboration
between students and tutors. Many of the collaboration and communication tools
mentioned before can be used to support this activity. E-teaching supports tacit-toexplicit knowledge conversion in that the teacher’s tacit knowledge is converted to
explicit learning material. E-learning supports explicit-to-tacit knowledge
transformation in that students learn and internalize the explicit material. Both eteaching and e-learning support tacit-to-tacit knowledge sharing when the tutor and
student communicate.
E-teaching involves knowledge creation, knowledge
distribution, storage, and organization, as well as knowledge application (when the
students apply the newly-acquired knowledge to problems).
Common features for tools include reusable learning object libraries; adaptive
web-based course delivery; component-based authoring, scheduling and reporting
tools; student evaluation and progress tracking; and building of skills inventories. Elearning systems often include collaboration tools and support for different types of
content, i.e., video, audio, documents etc. Searching for and matching of tutorials
with student needs and on-line facilitation of these tasks, are often supported.
Software development is becoming a more complex domain to master due to the
constant change and stream of new technologies. Many industries have similar
problems, but the software industry is probably worse than other industries due to the
fact that the pace of change is higher. Newly emerging technologies cannot be
mastered overnight. E-Learning tools can reduce the time and difficulty in learning by
a considerable margin, thus helping software organizations remain competitive.

3

Summary

This paper focused on software tools for knowledge management. We have surveyed
the commercial market for such tools, divided them into functional categories and
described them from different perspectives. We provided two models that we used to
describe the tools. All knowledge cannot be made explicit and recorded; thus a
knowledge management solution must address both tacit and explicit knowledge. All
repository-based software systems support a majority of the phases in the knowledge
life cycle, while few systems actually deal with the analysis and synthesis of new
knowledge. We conclude that most of the available tools are specialized, and not

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complete systems. This is a result of the many facets of KM that need to be addressed
through a KM system. Another finding is that not all tools that are labeled ‘KM tools’
are indeed KM tools; their vendors attempt to make them more attractive by attaching
this buzzword to them. Impressions from KMWORLD2001 and other conferences
indicate that the market is steadily growing. To implement an efficient KM system,
organizations must identify their main problems, priorities, and strategy, and then
select appropriate tools. Knowledge management relies heavily on technology, but it
is important to realize that technology alone will never be the solution to knowledge
management. There are socio-cultural and organizational components that need to be
addressed in a KM system implementation to assure its acceptance and success.

References
Henninger, S., "Case-Base Knowledge Management Tools for Software Development,"
Automated Software Engineering, vol. 4, pp. 319-340, 1997.
Lawton, G., "Knowledge Management: Ready for Prime Time?," IEEE Computer, vol. 34, no.
2, pp. 12-14, 2001.
Nonaka, I. and Takeuchi, H., The Knowledge Creating Company, Oxford University Press,
1995.

Wiig, K., Comprehensive Knowledge Management - Working Paper,
Knowledge
http://www.knowledgeresearch.com/downloads/compreh_km.pdf,
Research Institute, Inc., 1999.

Resources
Web resources
Document and Content
Management

Collaboration Services

Data and knowledge Discovery

http://www.microsoft.com/sharepoint
http://docushare.xerox.com
http://www.lotus.com/home.nsf/welcome/km
http://www.hyperwave.com
http://www.documentum.com
http://www.microsoft.com/windows/netmeeting
http://www.lotus.com/home.nsf/welcome/sametime
http://www.lotus.com/home.nsf/welcome/quickplace
http://www.groupsystems.com
http://fc-md.umd.edu/eworkshop
http://www.autonomy.com
http://www.spotfire.com
http://www.digimine.com

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Expert Networks
Knowledge Portals

Customer Relationship
Management

Competence Management
Intellectual Property Management
E-learning Management Systems

http://fc-md.umd.edu/vqi/
http://www.abuzz.com
http://www.sopheon.com/
http://www.plumtree.com
http://www.OptimalView.com
http://www.ascentialsoftware.com
http://www.lotus.com/home.nsf/welcome/kstation
http://www.askit.com
http://www.askmecorp.com
http://www.remedy.com
http://www2.xerox.com/go/xrx/knowledgest/knowledge_secti
on.jsp?id=19238
http://web.skillscape.com
http://www.tacit.com
http://www.patentcafe.com
http://www.contentguard.com
http://www.hyperwave.com/e/products/els.html
http://www.wisdomtools.com
http://www.firstdoor.com
http://www.tutor.com

Further reading
Agresti, W., "Knowledge Management," Advances in Computers, vol. 53, pp. 171-283, 2000.
Basili, V. R., Tesoriero, R., Costa, P., Lindvall, M., Rus, I., Shull, F., and Zelkowitz, M. V.
"Building an Experience Base for Software Engineering: A report on the first CeBASE
eWorkshop", Bomarius, Frank and Komi-Sirviö, Seija, Springer, In Proceedings of Profes
(Product Focused Software Process Improvement), pp. 110-125,2001.
Lindvall, M., Rus, I., Jammalamadaka, R., and Thakker, R., Software Tools for Knowledge
Management, DACS State-of-the-Art-Report, 2001.
Rus, I. and Lindvall, M. Knowledge Management in Software Engineering. IEEE Software
19[3], 26-38. 2002.
Rus, I., Lindvall, M., and Sinha, S., Knowledge Management in Software Engineering, DACS
State-of-the-Art-Report, 2001.

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