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Task and Value Oriented Semantics to Improve Content-
and Information-Management in the Enterprise
Karsten Böhm1, Harald Huber2
1FH KufsteinTirol, University of Applied Sciences
Andreas-Hofer-Str. 7
A-6330 Kufstein, Austria
2USU AG
Spitalhof, D-71696 Möglingen, Germany
[email protected] Abstract: The effective and efficient modelling of conceptual models is a critical
aspect content and information-management solutions, because it represents a time
consuming and costly process. Apart from structuring an information domain, such
a model should also provide guidance for the user and adapt to changing
requirements from the informational environment. To achieve these goals, this
article introduces the concept of agile Task and Value Oriented Semantics and
describes its use for the information modelling in the domain of IT-based
Knowledge Management Solutions. The approach will be based on a conceptual
architecture and uses a practical example to illustrate its application with the
current implementation that is used in several projects.
1 Introduction
The domain of IT-based information management systems that should support the access
to and the generation of knowledge are often relying on structures that provide means for
the organization and the access of the content in the underlying information sources.
These structures should represent and structure the content in a meaningful way, thus
carrying the semantics of the information that should be delivered. On the other hand
they should support the information retrieval process beyond keyword search to make
information easier accessible. Different knowledge representation standards, such as
ontologies (e.g. OWL, [1]) or semantic networks (e.g. Topic Maps, [2]) are used to
express these semantics in a formal, machine readable way. Typically these structures
are modelled manually.
However, a major problem of this top-down approach it the fact that due to the needed
abstraction and aggregation of these structures they are created manually, possibly by
external domain experts in many projects. Immediate consequences of this approach are
the incurring costs and the time-consuming labour that is needed to create and to
maintain these structures. Since these efforts have to be taken in the initial steps of the
project they also present a high initial barrier to such projects, as they are creating high
upfront costs for the project.
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As a result, these approaches often lead to knowledge representations that are somewhat
‘detached’ from the information sources and its usage pattern. They can hardly cope with
changes in the content that shifts the topics that are covered in the sources. The
representation might need to be remodelled in part to keep up with the changes, which in
turn requires additional efforts.
Another aspect is the separation of the modelling experts and the users of the knowledge
representation, which leads to difficulties in estimating the borders of the domain to be
modelled and the level of detail at which the model needs to represent the information
sources. These two parameters are difficult to assess in the beginning of the project and
will only emerge during the use phase. Therefore often an iterative approach is
employed.
In order to lower the initial barrier and overcome some of issues mentioned above,
automatic methods for the creation of semantic structures have been proposed. Most of
them are coming from the Text-Mining domain and are targeted at the generation of
semantic structures that should reflect the topics of the underlying information sources.
The advantage of these bottom-up approaches is the fast generation of structures even
from large information collections without the need for manual intervention. New topics
could easily anticipated by recreating the structure. However, the quality of the
generated structures is typically less high than the one of manual models and most of
these models exhibit a low level of abstraction as the rely on the textual information to
be analyzed as the primary source and do easily over-generate if the analysis process is
not parameterized carefully, see for example [3],[4]. Hybrid approaches have been
proposed that combine the bottom-up with the top-down approach. Although this seems
to be promising, these models tend to be complex and are not suitable for every
application [5]. For such models the ratio between automatic and manual approaches has
to be configured very carefully to create an efficient system.
Therefore our research addressed the question how information structuring could be
implemented in a way that is more tightly connected to the application domain, that
adapts to usage pattern and that involves the users in the creation and modification of the
structures. Another research issue was the inclusion of the aspect of guidance in the
knowledge representation to enable a faster identification of suitable documents. This
behavioural aspect is usually covered in a separate representation of the implementation
logic [6], the inclusion in the information model that structures a content domain
represents an innovation beyond the current state of the art.
The application of the new concept in the domain of Enterprise Document Management
will make relevant content more easily accessible to the user, guide him trough the
collection of relevant documents and increase the usage of the enterprise knowledge that
is contained in the documents. The usage information that is captured from the user-
interactions with the system can be used to adapt the information lifecycle of the content
repository in a demand oriented way, by identifying content that is not used anymore but
also information requests (queries) that could not be satisfied from the content base.
422
The paper consists of two main parts: The next section describes a new approach to
semantic models together with a conceptual architecture that is describing the main
components and functionalities. The other part is dedicated to a practical application
scenario and describes briefly an existing system that is implementing important aspects
of the concept. A summary and an outlook conclude the paper.
2 The Concept of Task and Value Oriented Semantics
The research is aiming at support of operational Knowledge Management in an effective
and efficient way. A system that provides such an operational support of the knowledge
worker should provide more support than just a passive information provision. It should
actively guide the knowledge worker during the sequence of activities that he or she is
carrying out.
We introduce the concept of Task and Value oriented Semantics to achieve this goal and
describe the main building blocks of a conceptual architecture for implementing this type
of semantics1. This concept should express two main aspects: (1) The additional
provision of direction and guidance in a conceptual model that is currently absent in
current approaches in the semantic web communities. Thus, we go beyond the approach
of semantically structuring the information domain. (2) We address the aspect of
minimal modelling according to the needs of the application domain and the needs of the
user and of dynamic adaptation of the semantic structure the by exploiting user
interactions2. These aspects lead to minimal structures that are suitable for and aligned to
the application domain. These characteristics can be subsumed as Agile Approach, as it
was defined in the area of software engineering [7]. To summarize the agile task and
value oriented semantics should address the following requirements and provide the
needed functionalities:
1. Realize a demand-oriented modelling perspective for the information domain
2. Provide active components for a context-dependant orientation in the model
3. Exhibit agile properties that allow adaption3 of the model and the system behaviour
2.1. The Conceptual Architecture
This section describes a conceptual architecture that illustrates the use of a task and
value oriented semantics. The approach builds on established functionalities of
information infrastructures and adds functionality for guiding the user to the relevant
information as well as a layer to participate in the interaction of this information. This
interaction information is changing the unidirectional interaction between user and
1 We use the German term „Handlungsorientierte Semantik“ for an agile task and value oriented semantics. 2 The role of user interaction in employed the same way as in the Web 2.0 approach. 3 Changes are primary taking place in the in the content repository and in the information demand.
423
machine (information consumption) into a bidirectional channel for the benefit of the
human user and the machine alike4.
Activity Representation
Concept Representation
Integrated Application of the Knowledge Worker
Representation
Document & Information RepresentationInformation
Provision
Function
Interaction Representation
Information Infra-
structure Mgmt.
IT-System
Semantic Model
Management
Constraints &
Activity Mgmt.
User & Interaction
Management
� ��
Proactive
Guidance
Conceptual
Structuring
� Information RetrievalInformation RetrievalInformation RetrievalInformation Retrieval�Search Queries
Relevant Documents
System
Adaptation
� Activity Oriented KMActivity Oriented KMActivity Oriented KMActivity Oriented KM�Context Information
Action Recommendations
� Conceptual StructuringConceptual StructuringConceptual StructuringConceptual Structuring�Concept Creation/Retrieval
Related Concepts
Figure 1: General Architecture of an information system that employs an agile task and value
oriented approach to semantically enriched systems.
The figure above shows the overview of the conceptual architecture, which is unifying
the different layers of an information infrastructure. The architecture is divided into four
layers that build upon each other and are structured into the main components
representation, function and IT-system support for each layer. Three typical use cases are
specified that reflect the typical application scenarios for information intensive work.
2.2. The Document Layer
On the lowest level we find the document collection that the knowledge worker needs
for his information intensive activities. Collections are typically characterized by their
size (typically thousands of potentially relevant documents) and the fact that single
entities of information cannot be statically provided to a certain activity (the information
demand keeps changing). IT-systems can help to improve the efficiency of information
collections that exhibit these characteristics. Classical Information Retrieval Systems
(level 1 in the figure above) create an index on these collections to provide a full text
search capability on the basis of keywords to find the relevant documents. They provide
the function of information provision in an easy usable way.
4 Although the type of information that is exchanged between the parties is different, is the basis for an
adaptive system that is the prerequisite for a dialog between the machine and the human user. The user is
gaining benefits from using the information that he is actively searching for, whereas the machine benefits
from the interaction in having means to adopt the way information are provided and structured by the machine.
424
A wide range of mature solutions are available to provide this functionality, that also
cover the aspects of federated search, the support for structured and unstructured
information and the use of descriptive meta-data for the documents. In our framework
these systems subsume the Information Infrastructure Management. These kinds of
systems have proven to be robust and scalable, but the information is accessible only if
the right keywords are provided (plain keyword based information access). No
structuring of the information and the documents is possible at that level.
2.3. The Concept Layer
Information organization, however, is the classical approach to deal with large
information volumes. This task was originally used by libraries at organizational level,
but is also common for the individual user and aims at structuring the information
collection according to the own beliefs and needs and to the requirements of the
activities that they are used in. Computer systems support this information organization
for digital content with manual methods (e.g. folder structures) and automatic techniques
(e.g. clustering and classification algorithms).
A number of representation formats and toolsets have been developed to represent these
structures (e.g. Topic Maps) and to provide an abstract view on the information
collection at the level of interrelated topics or concepts. This function is subsumed as
conceptual structuring in our architecture (represented by level 2 in the figure above).
These semantic models can be used to support the information provision on a more
abstract level and enable the browsing of large document collection to explore their
topics without having to consume every information entity as a whole.
A number of tools are available to manage and use these structures, e.g. to query the
structures, to visualize them and to browse them interactively. We summarize these
systems as Semantic Model Management. The manual creation and maintenance of such
structures can be difficult and costly and might outperform the benefits. Therefore, it is
not generally preferred over classical information retrieval solutions. It also proved to be
difficult to define a clear border for the models; often they become too complex when
trying to cover the whole domain or are incomplete by neglecting aspects that are
relevant for some knowledge intensive activities. Furthermore, the connection between
the concepts is not natural (as with the keywords in an index generated from a document
collection) and need therefore be maintained in a separate process.
Automatic methods have been proposed and used in the past, but their semantic
expressiveness is limited, too. More flexible and efficient methods are needed.
425
The main aspect at this level is to provide means for information organization, but not
for orientation. In this sense it is similar to the structuring aspects of encyclopaedic
volumes but does not have the instructive power of a textbook5.
2.4. The Guidance Layer
While structured information provide without doubt a benefit for the user, this might not
be efficient enough for the knowledge worker that is requiring information to fulfil a
certain information intensive task. In this environment often time constraints demand a
much more focused information provision to be efficient.
This function of proactive guidance (represented by level 3 in our architecture) is
effectively working as a filter on the available information selecting only those concepts
and information sources that are relevant in the current situation. This process needs to
be controlled by relevant parameters. In our framework this parameter is the value or
benefit that a piece of information contributes to a certain activity (value orientation).
This parameter is taken into account when building the initial conceptual model (acting
as a selective force) as well as at operation time, when the models are used by the
knowledge worker. At modelling time the value orientation is achieved by restricting the
model only to those parts that have a direct impact on the number of activities that
should be supported6 and on modelling constraints or requirements under which a
concept becomes relevant. At runtime feedback will be used to measure the value
orientation of the concepts and to adopt the representations on the conceptual and on the
informational level. The Constraint and Activity Management is responsible for
implementing the requirements and the adaptation of the model in our architecture.
This level thus represents an infrastructure that is able to anticipate the context of an
information intensive activity, but is not yet able to perceive this context from the user
interaction. This is the task of the top-most level in our architecture, the Interaction
Layer that is positioned at the boundary between the user and the IT-system. By
implementing the functionality of the necessary user and interaction management this
layer is able to track the interactions of the user with the system, both on the conceptual
level and on the information level. The aggregated information from this logging
information can be used to improve the search functionality, the structure of the
conceptual layer and the rules and requirements that are used to guide the user. It realizes
the functionality of a system adaptation that enables the system to evolve over time and
to adapt to the real information requirement of the users with respect to the provided
5 KM-projects that rely on IT-support often assume that information structuring already solve the problem of
the knowledge worker. More often than not, this support is not enough as it does not provide enough guidance,
esp. if the user is new to the information domain, or if the domain is very complicated. A detailed information
structure (fine grained hierarchies or heavily inter related graph structures) that is lacking the appropriate level
of abstraction can lead to desorientation by imposing a cognitive load on user in the same way as too much
information leads to an information overflow. 6 For this reason the models that are created are not universal models, they are tailored for a specific
application. On the other hand they include conceptual information from a number of different domains. Thus,
they try to cover the problem space, but do not serve as a universal description model for an information
domain.
426
information sources. The interaction representation delivers the contextual information
to the system that is needed to realize that functionality.
2.5. The Interaction Layer
As illustrated in the architecture overview the knowledge worker should be able to use
different ways to access the system and should not be forced to go through all the layers
that are available in the framework. The way the system is used depends on the current
activity and the knowledge and the information competencies of the knowledge worker.
The only exemption of this is the interaction layer that is needed in any of the illustrated
application cased (1-3 in the figure above). This layer should always be used to enable
the participation of the system and to enable a bidirectional interaction and adaptation of
the information provision. Furthermore this layer is also building the bridge to the
information experiences of the other users of the system and enables a collaborative
search experience.
3 Practical Application
3.1. Scenario Description
This section introduces an example for the application of value oriented semantics that
illustrates the approach and the resulting benefits. A general topic is chosen for the
example to enable easy comprehension, regardless of any specific domain knowledge.
However the general concept has already been successfully applied in several industry
projects in the area of Knowledge Management7.
A typical characteristic of value oriented semantics is the fact that it is always aimed at a
specific target group with a defined context of application. This prerequisite is a limiting
factor, in such that a modification of either of these parameters must results in a change
of the underlying model (need for remodelling).
The following example might highlight this in more detail: The goal is the definition of
the semantic model on a technical system for a helpdesk support system. This model
should not only structure the application domain, but also provide guidance to the agent
in order to enable him to ask the right questions. In particular this task imposes the need
to model only the necessary knowledge to answer the incoming requests – the model
should be minimal to be efficient.
The specific task is to put a projector that does not seem to work back to operation.
Would this situation be modelled with an ordinary semantic network it would take into
account aspects like that the projector consists of several components, these components
7 Note to the reviewer: More detailed application examples from several projects will be provided at the
presentation of the paper at the conference.
427
have dependencies and that activities of the user controls have consequences that change
the status of the device.
In fact a projector consists of a rather large number of components, but only a few are
playing a role for resolving everyday problems. Thus a full model of the device would be
too complex and not efficient; one can concentrate on the most important components
that are directly affected by the user interaction. This restriction the global
expressiveness of the model enables a more extensive elaboration on the aspect of
options for action8 that do provide the intended direction for the user and establish an
interaction with the system, by asking the result of an action that was carried out. This
illustrates the difference to classical modelling approached which usually does not have
a direct action orientation, whereas this is key concept in the domain of value oriented
semantics. It can be said that the value orientation is driving the modelling process. A
model can be constructed as illustrated in the following figure. It can be seen that
different aspects (product model, behaviour model, action domain) of the model are
overlapping and are only selectively modelled.
Product Behaviour Domain
Product Model DomainAction Domain
Option for ActionOption for Action
Removal of Protection
Cap on the Lens
Removal of Protection
Cap on the Lens
ProjectorProjector
Possible StatusPossible Status
Stand-byStand-by OffOff OnOn
Press Stand-by-ButtonPress Stand-by-Button
switches
possible action
related to
Protection CapProtection Cap
has a
possible action
might be
has a
might bemight be
Figure 2: An example for a value oriented semantic model of a projector (only parts
shown). It is interesting to note, that different aspects of the object are overlapping, but
are not extensively represented in the model.
The example shows that the modeling is focused on those sectors from the real world
that are connected to effective action options, which have a value for the intended
application (in this case problem resolution). A drawback of the representation of such
models as Graphs is, that they become complicated very fast when a certain number of
concepts or relations is exceeded. Moreover, the relation of the projector and its
8 In German we use the term “Handlungsoption“ for this term, which expresses the intended meaning more
precisely.
428
components is not relevant in most cases, as only a single component is involved in the
interaction.
It is therefore enough to connect these environment factors with the relevant action
options that the user can choose from. In the example in question these are the following
environment conditions (abbreviated list):
• Is the LED green, red or is there no light?
[Could be: “red”, “green” or “ no light”]
• What kind of problem do you have?
[Could be: “can see nothing, there is no light at all”, “can see only the default-
screen of the beamer” or “other”]
For this example the following action options are taken into account (abbreviated list):
• Please press the "ON" button for at least 5 seconds (standby-mode).
• Please connect Computer and beamer with the VGA Cable
• Please remove the cap in front of the projection lamp.
Instead of modeling these conditions in a graph or tree-structure as it is common for so
called decision trees we might use a frame-like table presentation, which describes
options for actions on the basis of chained conditions (or constraints). This is following
the schema depicted above, in which the option is derives from the set of conditions that
are inquired from the user or from the application context:
[option for action]
[request for conditionn]:[status of conditionn]
[request for condition1:[status of condition1]
Please note that the sequence of the conditions is not fixed in this definition. Also, in the
modeling the usual modeling sequence is inverted; we start from a possible action and
define the precondition that have to be checked, not the other way around. This way a
selective (minimal) modeling is ensured that starts from the initial question: “What could
be a possible (valuable) action that contributes to the resolution of the problem”. in this it
is not taken into account what steps have to be diagnosed. Traditional modeling
approaches for decision tree focus on the modeling of conditions that have to be checked
and arrive at the solution at the last step. In the given example the action options and the
environment conditions could be mapped into the following set of expressions
(abbreviated list):
[Please press the "ON" button for at least 5 seconds]
[Is the LED green, red or just dark]:[red]
[What kind of problem do you have]:[I can see nothing,
there is no light at all]]
[Please remove the cap in front of the projection lamp]
[Is the LED green, red or just dark]:[green]
[What kind of problem do you have]:[I can see nothing,
there is no light at all]
429
Having modeled the action options and the environment conditions, the question arises
how this model can be integrated into the business process that it should support. A
necessity is the integration into an information infrastructure that allows the delivery of
structured and unstructured information to the user, but this is not enough.
Since the preparation of the data is defined by a clear value orientation towards the
process is straightforward to identify an algorithm that is bringing the information into
an optimal tree-representation. The requirement is to define an interaction that finds the
most suitable solution in the fastest way over the average of all possible cases. In order
to achieve this goal the algorithm creates a decision tree (or dialog tree), which obtains
for each option for action the shortest way from the initial situation (the root of the
structure). The result of this calculation for our example can be seen in the next figure.
For the calculation of the decision tree the algorithm assumes for the initial creation that
all action options have the same probability of being relevant, thus assigning the same
weights for the transition between the elements. During the operation of the system the
interactions with the system are monitored and the tree is readjusted in order to shorten
the way to action options that have a high resolution probability. If for example the
selection of the wrong input channel in our example proves to be a frequent problem this
action option is tried first. In this respect the behavior of the tree mimics the heuristic
problem solution strategies of humans that most often use a similar approach.
Question to the
user
Question to the
user
Suggestion for
an action
Suggestion for
an action
Node-Type:
Options For actions
(Machine ����Human)
Node-Type:
Options For actions
(Machine ����Human)
Node-Type:
Request for information
(Machine ����Human)
Node-Type:
Request for information
(Machine ����Human)
Figure 3: Example of a dynamic decision tree. The boxes with an exclamation sign are
options for action. If an action was successful and the existing problem can be solved a
relocation to the end of the tree is done (not shown for the sake of simplicity).
430
3.2. Implementation of Task and Value Oriented Semantics
The described application of the adaptive decision trees as an instance of value oriented
semantics is implemented as a module in the Knowledge Management Suite of the
USU AG. The tight integration with this information infrastructure (see figure 4 below)
ensures, that the new semantic model can be implemented in an assistive way and not as
a new information system. It works side by side with the classical information and
knowledge procurement processes that are implemented in the Knowledge Miner Suite
and that are comparable to other solutions in the field (see [8] for a more detailed
description of the features).
The important aspect to keep in mind is the fact that the primary functions of the
value oriented semantics – guidance of and interaction with the user – are building upon
the existing functionalities and have an amplifying aspect for the knowledge creation
process on the basis of the provided information.
Solution for the adaptive task
& value oriented system
Typical Application
Scenarios
Basis Information
Infrastructure
Knowledge
Base
Knowledge
Guide
Figure 4: Overview of the architecture of the USU Knowledge Center Solution that
includes the aspects of a Value Oriented Semantics.
The implemented functionalities have been challenged in several industry projects
mostly in the Call-Center and User-Help-Desk (UHD) application domain. The results of
these projects have shown that the initial creation of the decision trees is drastically
reduced (up to 90%) and that the interactivity of the system enables an agile adoption on
changing properties of the environment.
431
4. Summary and Outlook
This paper introduced the concept of Task and Value Oriented Semantics that builds on
existing approaches and adds the aspect of proactive guidance to support the user in
information intensive tasks. The work originated from practical experiences with an
existing Knowledge Management System that is successfully used in complex and
information intensive application scenarios. The purpose of the presented conceptual
architecture was to shape the main building blocks for realizing the value orientation and
the guidance aspects in IT-based KM-Systems. Further works needs to be done in the
formalization of the framework and the derivation of indicators that might serve as
parameters when implementing such a KM-system.
This work should be understood as a first basis for the discussion on the introduction of
agile and active properties in IT-systems for demand oriented information provision. It
should move these systems beyond the functionality of consuming devices for
information, but provide the basis for an interaction with the machine that will gradually
lead to a better information quality. First practical experiences indicated that this
behavior can lead to the aspect of trust and reliability being attributed to the system by
the user, which in turn might be an important factor for the acceptance of IT-based KM-
systems.
References
1. Hendler J., Horrocks I. et al., OWL web ontology language reference, W3C
Recommendation, (2004), available at http://www.w3.org/TR/owl-ref
2. Park J., Hunting S., XTM Topic Maps: Creating and using Topic Maps for the Web,
Addison Wesley, Boston, p.81-101, (2003).
3. Böhm, K.; Heyer, G.; Quasthoff, U.; Wolff, Chr.: Topic Map Generation Using Text
Mining. Extended Version. J.UCS – Journal of Universal Computer Science
Springer, (2002).
4. Amardeilh, F., Laublet, P., and Minel, J. 2005. Document annotation and ontology
population from linguistic extractions. In Proceedings of the 3rd international
Conference on Knowledge Capture (Banff, Alberta, Canada, October 02 - 05, 2005).
K-CAP '05. ACM Press, New York, NY, 161-168.
5. Böhm, K.; Engelbach, W.; Härtwig, J.; Wilcken, M.; Delp, M.: Modelling and
Implementing Pre-built Information Spaces. Architecture and Methods for Process
Oriented Knowledge Management, J.UCS – Journal of Universal Computer
Science, Volume 11, Nr. 4, pp. 605-633, Springer, April 2005
6. Rinderle, S.; Weber, B.; Reichert, M. & Wild, W.: Integrating Process Learning and
Process Evolution - A Semantic Based Approach. In: BPM 2005 (2005), pp. 252-
267.
7. Williams, L. Cockburn, A.: Agile software development: it's about feedback and
change. IEEE Computer, Volume: 36, Issue: 6, page(s): 39- 43, 2003
8. Documentation of the USU KnowledgeBase:
http://www.usu.de/business_solutions/knowledge_solutions/answerbase.html
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