=Paper=
{{Paper
|id=None
|storemode=property
|title=A Context Model for Knowledge Workers
|pdfUrl=https://ceur-ws.org/Vol-626/regular2.pdf
|volume=Vol-626
|dblpUrl=https://dblp.org/rec/conf/ekaw/ErmolayevRTJGM10
}}
==A Context Model for Knowledge Workers==
https://ceur-ws.org/Vol-626/regular2.pdf
A Context Model for Knowledge Workers
Vadim Ermolayev1, Carlos Ruiz Moreno2, Marcel Tilly3,
Eyck Jentzsch1, Jose Manuel Gomez-Perez2, Wolf-Ekkehard Matzke1
1
Cadence Design Systems GmbH, Mozartstr 2 D.85622 Feldkirchen Germany,
vadim@ermolayev.com, jentzsch@cadence.com, wolf@cadence.com
2
ISOCO, Pedro de Valdivia 10 28006 Madrid, Spain,
cruiz@isoco.com, jmgomez@isoco.com
3
European Microsoft Innovation Center, Ritterstr 23 52072 Aachen Germany
Marcel.Tilly@microsoft.com
Abstract. The paper presents the model of a context that is further used in the
software tools that deliver proper pieces of information and knowledge to
knowledge workers in their everyday professional activities. The main
contribution of the paper is the upper-level context model which is a part of the
PSI Upper-Level ontology. The formal correctness of the model ic checked
using the OntoClean methodology. The model is further refined in the two
different domain-level knowledge representations – the PSI Environment,
Event and Happening ontology and the ACTIVE Knowledge Process Model.
The adherence of the upper-level model to the common sense is checked by
analyzing if its semantics is aligned with context denotations in several
foundational ontologies. It is also outlined that the presented context model is
effectively used for improving the productivity of knowledge workers in the
software tools developed in both mentioned projects.
Keywords: context, model, ontology, process, object, agent, environment, state
1 Introduction
A friend told me on the phone that she enjoyed “Tequila Sunrise” yesterday evening.
What would be my qualification of the object? The context grasped in the
conversation has been too general for comprehending the message unambiguously.
More knowledge about the situation has been required for disambiguation. If it was at
the bar, then the cocktail 1 was luckily well mixed (not too much tequila). If, however,
it was over the home cinema, then I’d have been quite certain that she was amused by
Mel Gibson 2 . Very similar things happen to us in our professional life. We play
different roles in different situations circumscribed by different contexts. We often
switch contexts in our work that is known to be an obstacle in the way of maximising
1 http://en.wikipedia.org/wiki/Tequila_Sunrise_(cocktail)
2 http://en.wikipedia.org/wiki/Tequila_Sunrise_(film)
productivity, especially for knowledge workers whose processes are to a big
proportion informal – i.e. very loosely defined and often alterable at execution time.
The research described in this paper is being undertaken as a part of ACTIVE 3 , an
EU-funded research project that seeks to remedy some of the defects of current
knowledge worker tools. It addresses the need for greater knowledge worker
productivity by providing more effective and efficient instruments based on the use of
different sorts of knowledge acquired within an organization. One central theme that
drives the research work in ACTIVE is the prioritization of the information and
knowledge delivery through an understanding of the current context of a knowledge
worker. Therefore the development of a rigorously defined context model that is
further operationalized in the ACTIVated software tools has been one of the
important tasks. Architecturally such a model has to be at a high level of abstraction
for being generic enough to cover the requirements and specificities of the project
case studies. It was also important to ensure that the model is harmonized with the
available domain-level knowledge representations of the case study partners. One of
such representations in the form of the Suite of Ontologies is being developed in the
PSI 4 project that is parallel to ACTIVE. It has been decided that the PSI Upper-Level
Ontology (PSI-ULO) would be the right setting for placing the high-level context
model of ACTIVE. The reasons for that are: (i) the PSI-ULO is a well founded upper-
level representation of a descriptive theory of informal processes; and (ii) the PSI
Suite of Ontologies is used as the knowledge representation formalism for one of the
ACTIVE case studies. Thus, providing the abstract context model at the upper level
allows bridging the context models at domain level for different case studies and
technology development.
The remainder of the paper is structured as follows. Section 2 reviews the related
work in modeling and using contexts. Section 3 presents our upper-level context
model that is further used as a conceptual bridge for domain-specific models in PSI
and ACTIVE projects. Section 4 describes these domain-specific models with the
emphasis on the refinement of the semantics of a Context 5 . Section 5 analyses the
alignment of our representation of a Context with foundational theories. Section 6
outlines the use of the presented context model in the software tools developed in PSI
and ACTIVE projects. Section 7 concludes the paper and outlines the planned future
work.
2 Related Work
Apparently the notion of a Context is both: (i) reflecting a mental state that people use
or are aware of quite frequently for determining the pragmatics in their situations; and
(ii) very vaguely defined for machine processing. Indeed, the definitions of a Context
3 ACTIVE – Enabling the Knowledge Powered Enterprise (http://active-project.eu/) is the EU
FP7 Integrating Project.
4 Performance Simulation Initiative (PSI) is the internal R&D project of Cadence Design
Systems GmbH
5 Here and further in the paper a capital letter at the beginning of the word means that a
concept, a model or a tool name is mentioned.
vary substantially depending on a field [16]. Several reviews (for example [8], [9],
[10]) point out to the cross-disciplinary nature of a Context mentioning different
stances in denoting and modeling contexts.
In theoretical Artificial Intelligence (AI), more specifically in the study of events
and situations, in commonsense reasoning, a Context is understood often
synonymously to a Situation [1]. Different aspects of contexts are highlighted and
used in defining categorizations and formal logical representations [1]: projections,
approximations, ambiguities, mental states. One of the prominently widely accepted
metaphors of a context is the box model [2] that allows analyzing the dependencies of
contexts following the dimensions of partiality, approximation, and perspective. The
idea of this approach is to look at a circumscription of a context and analyze its
placement and dependencies on the “terrain” of the observable contexts structured
alone the mentioned dimensions. Such an analysis allows understanding the situation
circumscribed by the interior of the context. At a very general level of understanding
AI approaches find their root in the definition by Abowd et al [4]: “Context is any
information that can be used to characterize the situation of an entity. An entity is a
person, place, or object that is considered relevant to the interaction between a user
and an application, including the user and applications themselves”. These
“situationalist” approaches of AI find reflection in the “environmentalist” approach
that has been pursued by us in the former work [5] and further in the ACTIVE project.
Our approach differs by the incorporation of processes as possible element kinds
comprising a Context.
In Conceptual Modeling (CM) and Knowledge Representation (KR) more attention
is paid to the structural aspects and categorization of context kinds. One of the well
founded approaches to context modeling [3] is based on the understanding that a
Context is a referential kind per se and therefore has to be separated from the
representation of an Entity. The context kinds are further classified as intrinsic and
relational. Situations are modeled by involving the combinations of contexts of
different kinds. Our approach to modeling contexts is aligned with the referential
character of a Context and its attribution to “MomentUniversals” [3]. In addition to
that, in PSI and ACTIVE we place our model in the setting of loosely defined
dynamic informal processes, environments, and subjective perceptions of what
happens in these environments. The emphasis in the PSI and ACTIVE domain
“serializations” of our upper-level model of a Context are slightly different. In PSI the
model is closer to the situational and referential representation of a Context. In
ACTIVE a Context is modeled in a holistic way as a part of an Environment. Many of
the foundational ontologies contain the formalizations of the semantics of a Context at
a high level of abstraction. Cyc [7] for instance is even structured in contextual parts
for better usability.
Yet another perspective in the vast body of research on contexts and context-
awareness is the use of contexts in software applications. Here as well exist a number
of context definitions in different areas, for example in Ambient Intelligence [4] or
the Semantic Web [22]. The area covered by our work in PSI and ACTIVE is more
related to exploiting user contexts in knowledge-involved daily tasks. The approaches
to context modeling and use around user context models and applications are as
presented below.
In EPOS project [17] a user context is denoted as the things that influence
knowledge work. Following an approach that is similar to the one of ACTIVE, a
context-sensitive application can detect the user's current working context through
user observation and logging and further support the user with suggestions and other
services 6 . As a part of this approach the two ontologies were developed: (i)
UserContextOntology 7 that is a top-level RDF Schema for modeling and processing
user context and also providing operators and comparators for that data; and (ii)
NopOntology (Ontology of Native User Operations) 8 that is an RDF schema to model
the native operations a user does when working. The NopOntology models generic
information objects (file, website, mail) and operations on them (open, close, save).
The work on user context done in EPOS has been further used as a basis in the
NEPOMUK project 9 . NEPOMUK developed a UserContextService as part of their
framework 10 that can be divided in three parts that work together:
(i) Observing user operations via plugins that send messages to the
UserWorkContext service. The observations are expressed using the NopOntology,
but only describes the actions (browse to, open mail), but not the objects of operations
(website, mail) which are well-defined through the NieOntology and PIMO
Ontology 11 [20].
(ii) Gathering user operations in the UserWorkContext service and computing and
updating the user context thread. Then, the work context is represented in the
UserContextOntology describing objects related to user's tasks which attributes are
computed and change constantly (like relevance).
(iii) Using context in the GUI and to support the users
This approach is used as part of NEPOMUK’s implementation 12 in KDE (the
graphical desktop environment for Unix/Linux workstations) 13 .
APOSDLE project enhances knowledge worker productivity by supporting
informal learning and teaching activities in knowledge workers’ everyday work
processes and within their work environments [18]. This is done including the
learner’s current work context for the three roles (called 3spaces) a knowledge worker
performs at the professional workplace: (i) the role of a learner, where APOSDLE
presents contextual knowledge sources as part of learner’s current work context; (ii)
the role of an expert, where APOSDLE enriches artifacts with context information
turning them into contextualized collaboration artifacts; and (iii) the role of a worker,
where APOSDLE enables workers to access content from several diverse knowledge
sources without having to change the environment based on their working context.
APOSDLE characterizes user context [19] by a relevant subset of all surrounding
potentially dynamic (e.g. temporal, environmental) information and external and
internal conditions considering a goal bounded to a knowledge worker. The User
Context is then an abstraction of three context spaces depending on the particular role
6 http://usercontext.opendfki.de/
7 http://usercontext.opendfki.de/wiki/UserContextOntology
8 http://usercontext.opendfki.de/wiki/NopOntology
9 http://nepomuk.semanticdesktop.org/
10 http://dev.nepomuk.semanticdesktop.org/wiki/UserWorkContext
11 http://www.semanticdesktop.org/ontologies/pimo/
12 http://techbase.kde.org/User_talk:Harikrishna
13 http://nepomuk.kde.org/
in each particular moment. The work-related context space is defined by a knowledge
worker’s tasks and the resources necessary to execute tasks. The learning-related
context space is defined by a knowledge worker’s competencies. The knowledge-
related context space is defined by a knowledge worker’s knowledge with regard to a
certain knowledge domain.
3 Context Model
The upper-level context model (Fig. 1) has been developed in response to the
requirements of the ACTIVE project in order to enable user context awareness in
ACTIVE software for information delivery [13]. The context model has been made
the part of the PSI-ULO v.2.3 14 that has been developed using the Shaker Modeling
methodology for ontology refinement [6]. The upper-level model has been further
used for developing the domain extensions of context models both in the Suite of PSI
Ontologies v.2.3 that is used in the ACTIVE Case Study on engineering design in
Microelectronics [12] and in the ACTIVE Knowledge Process Model that is used in
the other software developed in ACTIVE, for example in the Context Visualizer.
SUMO: Entity DOLCE: Perdurant DOLCE: Endurant Foundational
temporalParthood
Ontologies
PSI-ULO: Event PSI-ULO: Holon
structuralParthood
R+O+U+D R+O+U-D
1..*
PSI-ULO: Environment
0..* of
0..* PSI-ULO: Context R+O+U-D
contains
isRelevantTo contains +R+O+U+D 1..* 1..* 1..*
isRelevantTo has contains has of
0..*
1..* 0..1
0..* 1..*
PSI
PSI-ULO: Process
has of Upper-Level
PSI-ULO: Object
R+I+U+D
isFocusOf
0..1
of
0..1
1..*
belongsTo
Ontology
R+I+U+D
0..*
1..* 0..* has 1..*
has connects managedBy 0..*
of
worksIn PSI-ULO: Agent PSI-ULO: MaterialObject
0..* 2..*
R+I+U+D R+I+U+D
manages 2..* PSI-ULO: State
connectedBy R+O-U+D
PSI-E2H:Environment
PSI-E2H:Event nestedIn
PSI-E2H:Delay
0..*
timeLag : PSI-Time:Duration
1..* PSI-E2H:Observer PSI-E2H:Context 1..* 0..*
projectedBy
0..* perceives contains
PSI-E2H:Happening 0..* 0..* 0..* 0..*
PSI E2H Core
PerceivedAt : PSI-Time:TimeInstant perceivedBy focusesOn of isViewOfTheProperPartOf Ontology
Legend: (mA…MA), (mB…MB) are the multiplicities denoting the (instance) cardinality of the relationship:
(m,M)A specifies how many (minimally, Maximally) instances of A may be related to the ONE SPECIFIC instance of B;
(m,M)B specifies how many (minimally, Maximally) instances of B may be related to the ONE SPECIFIC instance of A.
DOLCE is the foundational ontology of the WonderWeb ontology library [14]; SUMO is the Suggested Upper Merged
Ontology [15].
Fig. 1: The upper-level context model and its domain-level refinement by PSI E2H Ontology.
14 The reference specification of the PSI Upper-Level Ontology is available on the Wiki at
http://isrg.kit.znu.edu.ua/ontodocwiki/index.php/PSI_Upper-Level_ontology
A Context 15 of an entity (that has the context) is the selection of related things that
facilitate interpreting, using, or performing the entity having this context in a
pragmatic way. For denoting a Context it is therefore required to answer:
(i) The context of what is specified? A Context could be either of a Process or of
an Object. Examples are: the context of a development team affiliated to an
organization, the context of a project, the context of the development process of the
configurable multimedia controller.
(ii) What is relevant for the inclusion in the context? An instance of a Context
may contain the instances of a Process or of an Object as relevant referential
constituents. Examples are: (a) the context of the process of engineering design may
refer to the members of the development team, the manager, the resources used or
consumed, the tools used, the design artifact under development; (b) the context of the
development team (subclass of an object) may contain the design processes performed
by the team, the organization to which the team is affiliated, the tools and the
resources, etc.
An Object 16 in PSI-ULO is a Holon 17 that has Environment, belongs to an
Environment, and may be changed in the execution of an AtomicAction. An Object
may have Characteristics and may be either material or immaterial. As an Object
inherits the structuralParthood relationship of a Holon the context model accounts for
the objects of any structure and complexity. The constraint that is put by the model
onto its object related part is that a Holon is an Endurant. Therefore the compositions
and aggregations of Objects are static in time. However a Context allows for
temporally dynamic aggregations of its referential components, objects in particular.
Therefore a Context, like an Event or an Environment, is a Perdurant.
In contrast to an Object, a Process in PSI-ULO is a Perdurant. A Process 18 is a
specialization of an Event that is stateful and possesses pro-active character. A
Process has its Environment – the part of the world which is changed in the course of
the Process. A Process is pro-actively directed by the Agent who manages it. As the
context model contains the concept of a Process with its relationship to the concept of
a State, the situations can be modeled as the states of the Environment. Moreover, as a
Process is explicitly related to its Environment, the model possesses a clear
environmentalist grounding.
(iii) Who uses the Context? A Context is used by an Agent to define the current
working focus and determine working priorities. For example if a manager supervises
several design projects he has to concentrate on each of them at different time. When
he is focused on a particular project the things relevant to the context of this project
become more important. Therefore we may say that the manager has switched his
work to the context of this project and considers that his actions applied on the items
relevant to the chosen context are of the higher priority than the actions applied to the
other contexts.
15 http://isrg.kit.znu.edu.ua/ontodocwiki/index.php/Context
16 http://isrg.kit.znu.edu.ua/ontodocwiki/index.php/Object
17 A holon is a term that denotes a system (or phenomenon) that is a whole in itself as well as a
part of a larger or a higher-level system (or phenomenon). The term is attributed to A.
Koestler (e.g. [24]).
18 http://isrg.kit.znu.edu.ua/ontodocwiki/index.php/Process
After the introduction of the context model the refined PSI-ULO taxonomy has
been formally evaluated using OntoClean Methodology [11]. The meta-properties
have been assigned to all the concepts (see Fig. 1 for the Context-related part) and the
analysis of the adherence of the taxonomy to formal constraints has been
undertaken 19 . The concept of a Context has been qualified as rigid (+R), supplying
identity (+O), carrying unity (+U), and externally dependent on the other concepts
(+D). A Context is rigid because for any instance of a Context it holds true that it is
always a Context. If x (at time instant t) and y (at time instant t') are Contexts and they
referentially contain the same instances of a Process and an Object (Γ(x,y,t,t')), then
x = y. Hence, a Context supplies (its own) identity. A Context subsumes to DOLCE:
Perdurant and therefore inherits its temporalParthood relationship to self, which is a
unifying relation. Indeed, a Context carries unity condition because it is a whole
comprising other instances of a Context as proper temporal parts. A Context is
externally dependent on other concepts: a Process, an Object, and an Agent. For
instance it is impossible to qualify something (x) as a Context without explicitly
pointing to y which is the process or the object relevantTo the context, or the agent
that worksIn the context.
SUMO: Entity DOLCE: Perdurant DOLCE: Endurant Foundational
temporalParthood
Ontologies
PSI-ULO: Holon
PSI-ULO: Event structuralParthood
0..*
1..*
0..* PSI-ULO: Context
contains PSI-ULO: Environment
of
isRelevantTo contains
isRelevantTo has
1..* 1..* 1..*
0..* contains +has of
1..* 0..1 0..* 1..*
belongsTo
PSI-ULO: Process
has of PSI-ULO: Object
0..*
0..1 0..1 1..*
has
1..* 0..*
+connects managedBy isFocusOf
1..* PSI
of has
Upper-Level
worksIn
PSI-ULO: MaterialObject
Ontology
0..* 0..*
manages PSI-ULO: Agent 2..*
2..*
PSI-ULO: State
+connectedBy +of
KP:Context isSubContextOf
KP:Task KP:Actor
name : String
name : String ID : int 0..*
KP:Resource
performedAt : Time name : String
ID : int
0..* 0..* 0..* ACTIVE
mayBeChangedIn isPartOf
isSuperContextOf Knowledge
KP:NonSystemEvent 0..* KP:Environment Process
0..*
mayTriggerChangeOf
Model
contains
Fig. 2: ACTIVE context model as a domain-level refinement of the PSI-ULO context model.
The upper-level context model (Fig. 1, 2) as a part of the PSI-ULO serves as a
semantic bridge [6] for enabling interoperability between different context-aware
applications in different though overlapping domains. Via this bridging the upper-
level model puts both domain-level models in one semantic context circumscribing
19 The outline of the OntoClean meta-properties, constraints and assumptions is given in
Annex A
the unifying feature of these domains. This feature is the account for the personalized
user context of a knowledge worker performing informal, loosely defined, and
dynamically ramified processes in his everyday work using his tacit knowledge.
4 An Environmentalist View on Contexts in Informal Processes
The domain-level models that are bridged by the context model of PSI-ULO are the
context model of the PSI Environment, Event, and Happening ontology (E2H) and the
ACTIVE Knowledge Process Model.
E2H provides a subjectivist view of an Agent, presented by an Observer concept,
and the model of his individual perceptions of the events that happen in his
Environments [5]. A Context in E2H (Fig 1, and also http://isrg.kit.znu.edu.ua/ontodo
cwiki/index.php/Environment,_Event,_and_Happening_ontology) is the view of an
Observer on the proper part of the Environment. An Observer is a PSI-ULO: Agent
that perceives events as happenings. In positioning itself in an Environment and
adjusting its behavior an Observer focuses on a Context. For example, an Observer
being situated in different environments may have different properties: play different
roles, have different beliefs, have different availabilities, execute different atomic
actions, etc.
An E2H: Environment in the ontology subsumes to PSI-ULO: Environment and
refines the model of environment by specifying the way an environment changes. An
Environment contains objects (an AffectedObject) which characteristics
(AffectedCharacteristic) are affected in the Event changing its AffectedValue. From
the other hand, the Environment containing the AffectedObject is the Environment in
which an InternalEvent affecting this object occurs. One more refinement provided
by E2H: Environment in the property of being a structural aggregate of Environments.
This parthood relationship allows modeling nested environments. Finally, an
Environment could be projected by the Context of an Observer. Such a context is the
view on a particular proper part of the Environment.
A Happening of an Event is the perception of this event by an Observer. Hence, in
terms of PSI-ULO a Happening is a specific sort of an atomic action performed by an
Observer. A Happening is atomic because it can not be split in parts without ceasing
being a happening of this event. An Event could be perceived as a Happening by an
Observer only if the event affects the object belonging to the part of the environment
that is in the context of the observer. A Happening, in difference to the associated
Event, never has duration but occurs instantly – at a TimeInstant when the perception
is recognized by the observer. A Happening may occur with a certain lag in time with
respect to the percept Event. The reasons for such a delay may be: (i) the observer
may take a certain time to notice the change in the value of the characteristic of the
affectedObject, or (ii) the change may become apparent with a delay.
In ACTIVE project the Context Model (Fig. 2) is one of the central parts of the
ACTIVE Knowledge Process Model (KPM) [30]. The Context is contained within the
Environment. The Context itself is defined as a collection of related things of a
specific State of the Environment. The things related to a Context are Actors, Tasks,
Resources, and Events. A Resource is an object which is associated with a Task and
belongs to a specific Context.
The KPM is bridging the common understanding of processes, either tacit or
formal, to the implemented software underneath. It facilitates turning informal
processes into more formal (or semi-formal) processes by defining terms such as
states, state transitions, admissible actions, as well as composing processes in various,
dynamic ways.. The model does not require any concrete and explicitly defined
sequence of action. It puts the actor – the knowledge worker – into the center of
driving his or her knowledge processes.
A Knowledge Process (KP) is denoted as a loosely defined and structural ramified
collection of actions. The structure of such a process and the order of action are not
fully defined at the start of a KP. Many actions require a decision by an actor about
the follow-up action. At such a decision point the actor uses his (tacit) knowledge and
the current working context to decide about the successor action.
To complicate matters, as circumstances change, the actor may decide to work in a
different context, rather than follow the normally expected pattern. In this way the
actor drives and carries out the KP. The context in the KPM is the part of the working
environment in which the KP is carried out. The context is composed of the elements
of the working environment that have to have a priority treatment by the actor in this
KP. Therefore the constitution of a context influences the decisions taken by the
Actor. Those decisions have to been taken during execution time over the process
development path and lead to emerging structural ramification constituted by
admissible alternatives. The outlined higher level denotation of knowledge processes,
contexts, actions, and actors is leading to a more formal mapping and alignment with
the PSI-ULO.
A Task (subclass of PSI-ULO: Process) is managed by an Actor (subclass of PSI-
ULO: Agent) containing subtasks. The AdmissibleAction is a PSI:AtomicAction and
therefore wrapped by a Task. A KnowledgeProcess is a specialized Task. The Actor
as a sub class of a PSI:Agent uses either tacit or explicit Knowledge, to decide about
FollowupActions and to drive the KP. The Decision about the FollowupAction is
driven by the Goal (subclass of PSI-ULO: Goal).
A PrimitiveEvent is an evidence of an action, e.g. something which a system is
aware of, and which forms an input to the event mining for event pattern detection.
We distinguish between system and non-system events. System events are observable
by the system, such as ‘file opened’ or ‘file closed’. Non-system events are not
observable by the system because the happen outside the system itself, such as talk to
a colleague. An Environment has States. The transition from one State of the
Environment to another State is caused by a PrimitiveEvent.
ACTIVE is trying to combine top-down and bottom-up approaches to transform
informal knowledge processes into more formal processes. Top-down means that we
start from a descriptive level where the knowledge worker defines his tasks and
activities (or derives these from a business- or master process). Those definitions are
shared within the organization and evolve over time. The bottom-up approach is about
mining and learning from event and action patterns to predict follow-up actions and
recommend alternatives to the knowledge worker. Patterns in general are Rules as
defined in PSI-ULO. An EventPattern is a regulatory on the event log which could be
connected to a defined ActionPattern. In the KPM EventPattern, TaskPattern, and
ActionPattern are the patterns that will be identified by the underlying mining
services (bottom-up).
Thus, the ACTIVE KPM provides concepts to express stateful creative dynamic
processes, actors, context and tasks situated in nested dynamic environments based on
the formal representation of time, events, and actions.
5 Checking Context Model with Foundational Theories
Several foundational ontologies have been reviewed to check if the proposed context
model is aligned with their top-level definitions of the semantics of a Context: CYC
[25], the D&S extension to DOLCE [14], SUMO [15] and WordNet [26], and BFO
[14].
In CYC a Context is a specialization of CYC: AspatialInformationStore and CYC:
AbstractIndividual. An individual context is the representation of the corresponding
instance of a CYC microtheory that is an atemporal, aspatial, informational thing,
though its components may possess temporal or spatial extent. Each context serves to
group together a set of assertions that share some common assumptions; the assertions
in a microtheory constitute the content of that microtheory. Hence, contexts are used
for circumscribing corresponding microtheories to enable adding new assertions and
facilitating reasoning with less effort. A Context is understood as a region in a
knowledge space having several dimensions (projections) as outlined in Fig. 3 in a
simplified way. It is mentioned that the order of analyzing the “footprint” of an
assertion onto these projections is important – so the dimensions of the knowledge
space are not orthogonal. However, no recommendations on the order of applying
projections are given, probably because of the foundational nature of CYC. In
essence, CYC context model is the abstract collection of assertions that hold true
within a particular region circumscribed by a combination of dimensional constraints.
AspatialInformationStore AbstractIndividual
0..*
TemporalRegion Context
projectionOf
0..*
0..*
SpatialLocation contains
projectionOf
0..*
CulturalDistinction structuralParthood
projectionOf
0..* subContextOf
SophisticationSecurity
projectionOf
0..* 0..*
TopicRegion
projectionOf 1..*
0..* contains belongsTo
GranularityRegion 1..*
projectionOf
0..* Assertion
ModalityDispositionEpistemology
projectionOf
0..*
ArgumentPreferenceRegion
projectionOf
Fig. 3: A simplified context model of CYC reconstructed from [25].
A primary distinction of the proposed model is that contexts in PSI-ULO contain
components that belong to environments and are not abstract, while the constituents
of CYC contexts are disembodied. Therefore a PSI-ULO: Context could be
considered a subclass of a CYC: Context only to a limited extent – both are
circumscribed using several dimensions. The primary dimensions for a PSI-ULO:
Context are temporal regions, structural compositions, and attribution to an Agent.
Contexts differ temporally as they are Perdurants. So, there is a correspondence to a
CYC: TemporalRegion dimension in the proposed model. Contexts differ in their
structures because both Processes and Objects inherit temporal or structural parthood
properties respectfully by subsumption. The structural dimension of the PSI-ULO
context model corresponds roughly to the Topic/Granularity projections of SYC.
Contexts in PSI-ULO are associated to an Agent who has this Context as his working
focus. This dimension corresponds to the CulturalDistinction projection of CYC as it
is also called “restrictions on the AgentType” [25].
The ontology of Descriptions and Situations (D&S) has been developed [14] as an
extension to DOLCE. D&S is a descriptive theory that outlines the distinctions
between the states of affairs (flux-like things), logical theories structuring these fluxes
denoted as Situations, and the Descriptions of these structures satisfied by the
corresponding structuring theories in the terms of a foundational ontology (DOLCE).
The UML model of D&S ontology reconstructed from the description in [14] is given
in Fig. 4.
DOLCE:Entity
locationOf participantIn
0..* 1..* 1..* 1..* partOf 0..*
DOLCE:Region DOLCE:Endurant DOLCE:Perdurant D&S:Situation
partOf partOf partOf
1..*
0..* 1..* 0..* 0..* 0..* 0..* 0..* 0..* 0..* 0..* 1..* 1..* 1..* 0..*
locationOf plays sequencedBy settingFor settingFor
settingFor
D&S:Description 1..*
D&S: S-Description
satisfiedBy
valuedBy 1..* 1..* 1..*
1..*
0..* D&S: FunctionalRole
0..* requisiteFor t_componentOf
1..* 0..*
D&S: Parameter
t_componentOf
0..*
0..* modalityTargetOf
requisiteFor 1..*
D&S: CourseOfEvents
0..* t_componentOf
0..*
Fig. 4: Descriptions and Situations ontology – extension of DOLCE.
D&S ontology follows a “situationalist” approach in defining contexts. Contexts
are the descriptions of Situations (S-Description) specified in terms of the sequences
of events (D&S: CourceOfEvents) that essentially are PSI-ULO: Processes, D&S:
FunctionalRoles played by DOLCE: Endurants that are participants in processes, and
D&S: Parameters describing constraints on the characteristics of the modeled state of
affairs. Hence, a PSI-ULO: Context can be mapped to a D&S: S-Description, but
again to a limited extent.
One important difference of the PSI-ULO model is that it involves both objects
(Endurants) and processes (Perdurants) as the components belonging to a Context
while the model of D&S contains process descriptions only. Another difference that
allows positioning our approach as “environmentalist” is that contexts in PSI-ULO
are situated in environments as the containers of Process and Object instances that
have their environments and belong to their (possibly different) environments.
Finally, in the proposed model agents (specialization of an Object) are not only the
components of contexts, but also the active users of contexts as their working foci.
SUMO only states that a WordNet: Context subsumes to a Proposition that is a
subclassof an Abstract (thing). In WordNet the relevant definition of a Context states
that a Context is “the set of facts or circumstances that surround a situation or event”
– Fig. 5.
Fig. 5: The definition of a Context in WordNet.
Like in CYC a WordNet: Context is a disembodied abstract thing but in difference
to CYC it subsumes directly to an Environment. However, an Environment in
WordNet is denoted as a hyponym to a Situation or a State of Affairs. Such a
definition makes our model disaligned with WordNet.
In BFO [27] only processual contexts (settings) are defined as Occurrent entities
consisting of a characteristic spatial shape inhering in some arrangement of other
occurrent entities. Processual contexts are the entities at or in which other Occurrent
entities can be located or occur. Hence, contexts in BFO are situations or states of
affairs attributed to spatial regions. The PSI-ULO model is very partially aligned with
the BFO model of a Context – only in the sense that in both models a Context is a
Perdurant and circumscribes a state of affairs.
6 Context Model in Use
The proposed context model has been used in software tool implementations for PSI
and ACTIVE projects. In PSI it is the basic model for representing structural contexts
within an ontology for knowledge engineers in the Ontology Difference Visualizer
(ODV) software tool [28]. In Active the model has been used as a basic upper-level
formal knowledge representation for knowledge workers in the Context Visualizer
front-end of the ACTIVE Knowledge WorkSpace (AKWS) software [29].
A proof of concept prototype of ODV has been implemented in PSI as an ontology
engineer plug-in for Cadence ProjectNavigator software. The composition of a
context of an ontology concept, as implemented in the ODV, could be formed by
specifying the radius of the neighborhood of this concept. Further it could be fine-
tuned by manual inclusion or exclusion of the concepts, object properties,
subsumption relationships. The analyzed ontological context may be placed on the
wafer of the source (old) ontology. The context may be also altered by considering or
filtering out the concepts belonging to the imported ontology modules. All the
mentioned constituents of a context are Objects in terms of the context model. Finally
the “owner” filter may be employed for concentrating on the changes that have been
introduced by a particular ontology engineer in the team. These “owners” are the
instances of an Agent in terms of the context model.
A key factor in ACTIVE is to help knowledge workers manage and understand the
context (elements, boundaries, and environment) of their daily collaborative
processes. This visualization can be addressed from different perspectives. Moreover,
as each complex collaborative process is performed in some context, the visualization
of such a context helps understand the underlying relationships within collaborative
processes in which those contexts are shared.
Contexts in the enterprise environments have some specifics that should be
considered in user-tailored representations: (i) there are different views on context,
varying by the observation perspective (the aspect in focus) and the purpose of using
that context – particularly in terms of the range of possible entities to be considered,
e.g. tags as a new interesting resource; (ii) the relationships within a context among
files, people, tasks, and any type of resource are multiple and complex, where
different items have different relevance to the context; (iii) an easier comprehendible
interpretation of a context for non-expert users has to be provided – for example by
visualizing contexts in the terms that are native for knowledge workers.
Consequently, the outlined specifics lead us to accounting for the following set of
features for the ACTIVE context visualization tool: (i) use a human understandable
visualization paradigm of the underlying context model; (ii) identify graphically
different entities relevant to the visualized context as well as their relationships;
(iii) identify the relevance (importance) of an item in the context to the user (the
larger the icon is, the more relevant or important the item is to the user – for example
the more time the user spends working on this item; (iv) provide easy to use facilities
for filter the context elements; (v) deal appropriately with the size and complexity of
context visualizations by providing the features for reducing the inherent complexity
and facilitating understanding of the complex relationships within a context.
Component-wise the Context Visualizer comprises the: Context Mapper, Context
Representation Model, Context Presentation Engine. The Context Mapper receives
the incoming knowledge from the AKWS Services [23] (in particular, the User,
Resource, Task, and Mining services) in terms of the KPM. For a better
comprehendability by non-specalist users it maps the received context instances and
consolidates them into the internally used Context Representation Model in terms of
the resources within a context in ACTIVE platform [21].
7 Conclusions
The paper presented the context model developed in a parallel effort of ACTIVE and
PSI projects in response to the requirements of the ACTIVE case studies. The upper-
level context model, as a part of the PSI-ULO, serves as a semantic bridge for
enabling interoperability between different context-aware applications in different,
though overlapping domains. Through this bridging the upper-level model puts both
domain-level models in one semantic context circumscribing the unifying feature of
these domains. This feature is the account for the personalized user context of a
knowledge worker performing informal, loosely defined, and dynamically ramified
processes in his everyday work using his tacit knowledge. It has been also shown how
the upper-level model is refined for the needs of finer-grained domain-level
descriptive theories in the contexts of PSI (the E2H ontology) and ACTIVE (the
KPM). Finally the paper outlines that the context model is used for developing
software tools for knowledge workers. One such tool – the Context Visualizer – is the
front-end part of the ACTIVE knowledge worker desktop, another is the tool for
contextualized visualization of structural differences in ontologies developed in PSI
for knowledge engineers.
Acknowledgements
The work described here has been funded as part of the IST-2007-215040 EU project
ACTIVE and the internal R&D project PSI of Cadence Design Systems GmbH.
References
1. Guha, R., McCarthy, J.: Varieties of Contexts. In: P. Blackburn et al. (eds.): CONTEXT
2003, LNAI, vol. 2680, pp. 164--177, Springer, Heidelberg (2003)
2. M. Benerecetti, P. Bouquet, C. Ghidini, On the dimensions of context dependence:
Partiality, approximation, and perspective. In: Akman, V. et al. (eds.): CONTEXT 2001,
LNAI vol. 2116, pp. 59--72, Springer, Heidelberg (2001)
3. Dockhorn Costa, P., Guizzardi, G., Almeida, J.P.A., Ferreira Pires, L., van Sinderen, M.:
Situations in Conceptual Modeling of Context. In: EDOC 2nd International Workshop on
Vocabularies, Ontologies and Rules for the Enterprise, IEEE Press, NY (2006)
4. Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., Steggles, P.: Towards a
Better Understanding of Context and Context-Awareness. In: Gellersen, H.(ed.): HUC 1999,
LNCS, vol. 1707. 304—307, Springer, London, (1999)
5. Ermolayev, V., Keberle, N., Matzke, W.-E.: An Ontology of Environments, Events, and
Happenings. In: 31st Annual International Computer Software and Applications Conference,
pp. 539-546, IEEE Press, NY (2008)
6. Ermolayev, V., Keberle, N., Matzke, W.-E.: An Upper-Level Ontological Model for
Engineering Design Performance Domain. In: Li, Q., Spaccapietra, S., and Yu, E. (eds.) ER
2008, LNCS, vol. 5231, pp. 98--113, Springer, Heidelberg (2008)
7. Lenat, D.B., Guha, R. V.: Building Large Knowledge-based Systems: Representation and
Inference in the Cyc Project. Addison-Wesley (1990)
8. Bettinia, C., Brdiczkab, O., Henricksenc, K., Indulskad, J., Nicklase, D., Ranganathanf, A.,
Riboni, D.: A survey of Context Modelling and Reasoning Techniques. Pervasive and
Mobile Computing, 6(2), 161--180 (2010)
9. Baldauf, M., Dustdar, S., Rosenberg, F.: A survey on Context-Aware Systems. Int. J. Ad
Hoc Ubiquitous Comput. 2(4), 263—277 (2007)
10.Bradley, N.A., Dunlop, M.D.: Toward a Multidisciplinary Model of Context to Support
Context-Aware Computing. Human–Computer Interaction, 20, 403--446 (2005)
11.Guarino, N., Welty, C.A.: Ontological Analysis of Taxonomic Relationships. In: Laender,
A., and Storey, V. (eds.) ER 2000, LNCS, vol. 1920, pp. 210--224, Springer, Heidelberg
(2000)
12.Ermolayev, V., Dengler, F., Jentzsch, E., Matzke, W.-E.: Articulation and Sharing of
Distributed Design Project and Process Knowledge. In: IDC 2010, Springer, (2010)
13.Gomez-Perez, J. M., Grobelnik, M., Ruiz, C., Tilly, M., and Warren, P.: Using Task Context
to Achieve Effective Information Delivery. In: 1st Workshop on Context, information and
ontologies, pp. 1--6, ACM Press, New York (2009)
14.Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A.: Ontology Library (final),
WonderWeb Deliverable D18 (2003)
15.Niles, I., Pease, A.: Towards a Standard Upper Ontology. In: Int. Conf. on Formal
Ontologies in Inf. Systems, pp. 2--9. ACM Press, New York (2001)
16.Bazire, M., Brézillon, P.: Understanding Context Before Using It. In: Dey, A. et al. (eds.)
CONTEXT 2005, LNCS, vol. 3554, pp. 29--40, Springer, Heidelberg (2005)
17.Schwarz, S.: A Context Model for Personal Knowledge Management Applications. In:
MRC 2005, LNCS, vol. 3946, pp. 18--33, Springer, Heidelberg (2005)
18.Lindstaedt, S., Mayer, H.: A Storyboard of the APOSDLE Vision. In: Nejdl, W.,
Tochtermann, K. (eds.) EC-TEL 2006, LNCS, vol. 4227, pp. 628-633, Springer, Heidelberg
(2006)
19.APOSDLE Scope and Boundaries, APOSDLE Project Deliverable D6.1, 2006
20.Sauermann, L., van Elst, L., Dengel, A.: PIMO - a Framework for Representing Personal
Information Models. In: I-Semantics' 07 (2007)
21.Mladenic, D., Getoor, L., Ferlež,, J., Grobelnik, M.: Context definition and representation
within enterprise environments. Deliverable of EU-FP7 project ACTIVE. D2.3.1 (2009)
22.Bouquet, P., Giunchiglia, F., van Harmelen, F., Serafini, L., Stuckenschmidt, H.: C-OWL:
Contextualizing Ontologies. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003,
LNCS, vol. 2870, pp. 164--179 (2003)
23.Warren, P., et al.: Improving Knowledge Worker Productivity -- the ACTIVE Integrated
Approach, BT Technology Journal, 26, pp 165--176 (2009)
24.Koestler, A.: Beyond Atomism and Holism -- the concept of the holon. In: Koestler, A.,
Smithies, J.R. (eds.) Beyond Reductionism: New Perspectives in Life Sciences, pp. 192—
216, Hutchinson, London (1969)
25.Lenat, D.: The Dimensions of Context-Space. Cycorp (1998)
26.Fellbaum, C. (ed.): WordNet: an Electronic Lexical Database. The MIT Press (1998)
27.Spear, A. D.: Ontology for the Twenty First Century: An Introduction with
Recommendations. IFOMIS, Saarbrücken, Germany (2006)
28.Ermolayev, V., Copylov, A., Keberle, N., Jentzsch, E., Matzke, W.-E.. Using Contexts in
Ontology Structural Change Analysis. In: Ermolayev, V., Gomez-Perez, J.-M., Haase, P.,
Warren, P, (eds.) CIAO 2010, CEUR-WS (2010)
29.Warren, P., Gomez-Perez, J.-M., Ruiz, C., Davies, J., Thurlow, I., Dolinsek, I.:. Context as a
tool for organizing and sharing knowledge. In: Ermolayev, V., Gomez-Perez, J.-M., Haase,
P., Warren, P, (eds.) CIAO 2010, CEUR-WS (2010)
30.Tilly, M.: Dynamic Models for Knowledge Processes – Final Models and Evaluation.
ACTIVE Deliverable D1.2.2 (2010)
Annex A. Meta-Properties, Constraints and Assumptions
As suggested by the OntoClean methodology for the formal evaluation of the
ontological adequacy of taxonomic relationships [11]:
Definition 1: rigidity, non-rigidity, and anti-rigidity. A rigid property (+R) is a
property that is essential to all its instances, i.e. a property φ such that:
∀xφ (x) → □ φ (x) . A non-rigid property (–R) is a property that is not essential to
some of its instances, i.e. ∃xφ (x) → ¬ □ φ (x) . An anti-rigid property (~R) is a
property that is not essential to all its instances, i.e. ∀xφ (x) → ¬ □ φ (x) .
Definition 2: identity. An identity condition (IC) is a formula Γ that satisfies either
(1) or (2) below, excluding trivial cases and assuming a predicate E for actual
existence at time t or t':
E(x,t) ∧ φ(x,t) ∧ E(y,t') ∧ φ(y,t’) ∧ x = y → Γ(x,y,t,t') (1)
E(x,t) ∧ φ(x,t) ∧ E(y,t') ∧ φ(y,t’) ∧ Γ(x,y,t,t') → x = y (2)
An IC is necessary if it satisfies (3) and sufficient if it satisfies (4). Based on this,
two meta-properties are defined:
Identity (I): Any property carries an IC (+I; –I otherwise)) iff it is subsumed by a
property supplying that IC (including the case where it supplies the IC itself).
Own Identity (O): A property φ supplies an IC (+O; –O otherwise) iff: (i) it is
rigid; (ii) there is a necessary or sufficient IC for it; and (iii) the same IC is not carried
by all the properties subsuming φ.
Definition 3: unity. An object x is a whole under ω iff ω is an unifying relation
such that all the parts of x are linked by ω, and nothing else is linked by ω. A property
φ carries a unity condition (+U; –U otherwise) iff there exists a single unifying
relation ω such that each instance of φ is a whole under ω. A property has anti-unity
(~U) if every instance of the property is not a whole.
Definition 4: dependence. A property φ is externally dependent (+D; –D
otherwise) on a property ψ if, for all its instances x, necessarily some instance of
ψ must exist, which is neither a part nor a constituent of x:
∀x □ (ϕ ( x) → (∃yψ ( y ) ∧ ¬P ( y, x) ∧ ¬C ( y, x)) , (3)
where P ( y, x) is a parthood relationship; C ( y, x) is a constitution relationship.
Assignment of OntoClean meta-properties imposes several constraints on
taxonomic relationships. If φ and ψ are two properties then the following constraints
hold:
φ~R can't subsume ψ +R (4)
φ+I can’t subsume ψ -I (5)
φ+U can't subsume ψ -U (6)
φ~U can't subsume ψ +U (7)
φ+D can't subsume ψ -D (8)
Properties with incompatible ICs/UCs are disjoint. (9)
Finally, the following assumptions regarding identity are made. Sortal
Individuation: every domain element must instantiate some property carrying an IC
(+I). Sortal Expandability: if two entities (instances of different properties) are the
same, they must be instances of a property carrying a condition for their identity.