=Paper=
{{Paper
|id=None
|storemode=property
|title=A Linked Knowledge Base for Simulation Learning
|pdfUrl=https://ceur-ws.org/Vol-717/paper3.pdf
|volume=Vol-717
|dblpUrl=https://dblp.org/rec/conf/esws/CelinoD11a
}}
==A Linked Knowledge Base for Simulation Learning==
https://ceur-ws.org/Vol-717/paper3.pdf
A Linked Knowledge Base for
Simulation Learning
Irene Celino and Daniele Dell’Aglio
CEFRIEL – Politecnico of Milano, Via Fucini 2, 20133 Milano, Italy
{irene.celino,daniele.dellaglio}@cefriel.it
Abstract. Simulation Learning is a frequent practice to conduct near-
real, immersive and engaging training sessions. AI Planning and Schedul-
ing systems are used to automatically create and supervise learning ses-
sions; to this end, they need to manage a large amount of knowledge
about the simulated situation, the learning objectives, the participants’
behaviour, etc.
In this paper, we explain how Linked Data and Semantic Web tech-
nologies can help the creation and management of knowledge bases for
Simulation Learning. We also present our experience in building such a
knowledge base in the context of Crisis Management Training.
1 Introduction
Traditional research on Semantic Web in e-learning [1, 2] are aimed at promoting
interoperability between training systems, thus usually the core investigation
targets are standards and schemata to describe learning objects [3, 4].
Our research is focused on a different kind of e-learning system, i.e. Simu-
lation Training to improve soft skills [5]. In this context, not only it is needed
to describe learning objects, but also to fully plan simulation sessions; those
sessions should be interactive and engaging to challenge the trainees to improve
their skills. Simulation Learning systems generally re-create a near-real environ-
ment for training sessions, in which learners are subject to stimuli: they have
to learn how to deal with the simulated situation and how to react to it.. Such
simulations need to be effective and engaging, so that the learners do not simply
memorise notions about the specific matter, question or theme, but they actively
and permanently acquire skills, practice and knowledge.
The scenario production is therefore the core and critical activity when build-
ing a Simulation Learning system. Knowledge technologies are needed to model
and manage all the required information, often generated and managed by dif-
ferent and independent sources: scenario descriptions, events and stimuli for the
trainees, storyboards for the learning sessions, multimedia assets, supporting
documents and guidelines, trainees description and behaviour/decisions, learn-
ing session monitoring, etc. Such a wealth of information makes the Simulation
Learning a knowledge-intensive context, which requires smart solutions.
We decided to adopt Linked Data and Semantic Web technologies to address
the requirements of Simulation Learning. The knowledge diversity and scale
2 Irene Celino and Daniele Dell’Aglio
calls for a solution which provides interlinking between different datasets while
preserving possibly independent information sources; moreover, the knowledge
coherence and consistency must be assured to guarantee the significance, mean-
ingfulness and correctness of simulation scenarios and storyboards presented to
trainees.
In this paper, we present our current effort in exploiting Linked Data and
Semantic Web technologies to build a Knowledge Base for a Simulation Learning
environment. We explain why we believe that the selected technologies not only
offer a suitable means to knowledge representation and management, but they
are specifically required to address the challenges of such an environment.
Section 2 introduces the basic concepts of Simulation Learning systems and
a concrete scenario in Crisis Management Training; Section 3 details our ex-
ploration in the use of Linked Data and Semantic Web to build a Simulation
Learning Knowledge Base illustrating the gained benefits; Section 4 specifies
our modelling choices, while Section 5 suggests that such modelling could bene-
fit from provenance tracking; finally, Section 6 concludes the paper.
2 Simulation Learning
Learning should be relevant to people’s workplace and lives: learning content
should be truly understood, remembered and applied to actual practices. Only
in this way, by actively engaging participants in experiential training, learners
can apply their knowledge and learn the best practices [5]; more and more often,
indeed, it is not enough to read information and listen to a frontal lecture.
In this section, we introduce the theme of Simulation Learning for Decision-
making, we draw a generic architecture of a system to support Simulation Learn-
ing and we describe a concrete scenario that we will use throughout the paper
to exemplify our approach.
2.1 Simulation for Decision-making
Training plays an important function in the preparation of professional prac-
titioners. Currently, there are two main modalities for such training: table-top
exercises and real-world simulations. Table-top exercises are low cost and can
be easily and frequently organised. However, they cannot create a believable at-
mosphere of stress and confusion, which is prevailing in real-life situations and
is crucial to the training of timely and effective decision making. On the other
hand, training through simulation exercises on the field can be very effective [6],
but it is considerably more expensive, it can require specialist equipment and it
can be difficult to organise.
Simulation exercises require an Exercise Director (or trainer ) who plays a
key role in every form of exercise: the trainer has access to the whole exercise
programme, ensures that it proceeds according to a plan, often feeds information
to the “players” (the trainees) to let them make informed decisions in response
(verbally or by written messages). Sometimes information fed to the trainees
A Linked Knowledge Base for Simulation Learning 3
is timed in advance at pre-set intervals, regardless of the previous responses.
However, flexibility allows a trainer to use judgement and experience in timing
the inputs: his/her role should be aimed to facilitate rather than orchestrate the
exercise, thus intervention should be minimal and trainees should be given time
to recognise and correct problems. Nevertheless, usually it is up to the trainer
to decide, for example, how much advice to give to trainees.
2.2 Architecture of a Simulation Learning System
The architecture of a Simulation Learning System is depicted in Figure 1. In the
picture, we can identify the involved actors, which are the trainees – the learning
participants engaged in the simulation – and the trainer – who activates the
exercise and monitors the progress of actions during the training session.
The figure also shows the four main modules of such an architecture, the first
three following the usual AI sense-think-act cycle:
– Behaviour Sensing: this module is aimed to create and update a model of
each trainee from sensors information (e.g. heart rate, blood pressure, res-
piration); the model represents trainee’s future and actual behaviour and
provides indications on how to personalise the training path.
– Simulation Planning: this module is aimed to create and simulate a training
scenario and its evolution, by combining the information in the behavioural
model with knowledge about the learning scenarios; the output of this mod-
ule is the actual simulation storyboard presented to the trainees.
– Learning Delivery: this module is aimed to effectively represent the simu-
lation storyboard in the learning environment, including the rendering of
audio-video inputs or Non-Player Characters (NPC, cf. Section 4.3).
– Simulation Learning Environment: this is the “place” where the training is
conducted; the location can be a physical room or a virtual environment
where the trainees interact and receive stimuli during a learning session.
The core of such system is therefore the Simulation Planning module, which
contains the basic engine for creating active exercises for classes of trainees. The
module is responsible for deciding which stimuli are sent to trainees and how
they should be coordinated to create a meaningful and effective lesson plan. In
broad terms, it is responsible for allocating over time the set of lesson stimuli in-
dexed according to differences in presentation media, emotional characterization,
personalization needs, etc.
2.3 Crisis Management Training Scenario
There is increasing recognition for the need to train non-technical skills like con-
trol and decision making for Crisis Management in national emergencies, high-
reliability industries, as well as in industrial workplaces [7, 8]. In the happening of
a catastrophic event, it is human behaviour – and often human behaviour alone
– that determines the speed and efficacy of the crisis management effects [9].
4 Irene Celino and Daniele Dell’Aglio
Fig. 1. High-level architecture of a Simulation Learning System (from the classical
sense-think-act cycle of AI)
The Pandora project1 aims to provide a framework to bridge the gap between
table-top exercises and real-world simulation exercises for Crisis Management,
providing a near-real training environment at affordable cost. Its training sys-
tem captures the good practice tenets of experiential learning but with greater
efficiency and focuses on real, rather than abstract learning environments. The
effective use of integrated ICT reduces the high dependence upon the trainer
that is currently required to deliver exercises. Moreover, the Pandora framework
supports the measurement and performance assessment of Crisis Managers, the
key decision makers participating in a training exercise event as trainees.
As such, Pandora is developing an enabling technology to simulate believable
dynamic elements of an entire disaster environment by emulating a crisis room
(the Simulation Learning Environment). In this context, we are developing a
Knowledge Base that makes use of Linked Data and Semantic Web technologies
to model and interlink the pieces of data needed in the training simulation
sessions. In the rest of the paper, we will use the Crisis Management scenario to
exemplify our approach.
1
Cf. http://www.pandoraproject.eu/.
A Linked Knowledge Base for Simulation Learning 5
3 Our Simulation Learning Linked Knowledge Base
Within a Simulation Learning system, knowledge exchange plays a central role.
In this section we give some details about the Simulation Planning module, focus-
ing on the requirements, design and implementation principles of its Knowledge
Base. All the technical details are related to the choices made in the Pandora
framework.
3.1 Knowledge required to Plan a Simulation
To formalize the lesson plan, it is natural to choose a basic representation from
timeline-based planning [10]. A plan is represented as a set of events having a
temporal duration, distributed over a time horizon and indexed according to
distinct features which should be planned for. This set of events is organized
inside a data structure called Event Network, very common in current state of
the art planning technology. The Event Network is a temporal plan of multi-
media communicative acts toward trainees (e.g., e-mail messages, video news
from an emergency location, etc.).
The Event Network can generated by a Simulation Planner. This planner
compiles static information into the Event Network, and then adapts the events
configuration according to the actions of the trainees, thus simulating different
courses of action of the world. The planner can be adapted from a generic AI
Timeline-based Planning and Scheduling module [10].
The core information item elaborated by a Simulation Planner is the so-
called synchronization. Synchronizations are the causal rules that regulate the
transitions between values on the same planning feature and the synchronization
of values among different planning features. In the Crisis Management scenario,
synchronizations are used to influence the Crisis Managers’ decisions, e.g. to
generate changes in the emergency conditions.
When adopting Planning and Scheduling technologies to simulate a scenario,
it is worth highlighting how a great effort and amount of time is necessary
to understand the problem, capturing all its specificity, and to create a model
of the relevant aspects of the domains and the problem [11]. This consideration
suggests, on the one hand, the need for identifying commonalities and similarities
among the different domains and problems to operate in a more systematic way
and, on the other hand, the opportunity to exploit Semantic Web technologies
to ease and support the knowledge modelling task.
For those reasons, we have built a Knowledge Base with Linked Data and
Semantic Web technologies. This KB is a central component in the Simulation
Learning system, responsible for collecting and maintaining the “knowledge”
about scenarios and training sessions. As such, the KB is the core information
source for the simulation: it contains all the knowledge required by the Sim-
ulation Planner to “orchestrate” the events during the training sessions. All
the causality in a simulation domain is modelled and stored in the KB; this
knowledge is then converted by the Simulation Planner into the suitable data
6 Irene Celino and Daniele Dell’Aglio
structures to synthesize the Event Network configurations for the lesson plan
goals.
3.2 Requirements for the Knowledge Base
The Knowledge Base [12] was carefully designed to fulfil a pressing requirement:
containing and managing all the knowledge needed to model and run the simu-
lation scenarios, the training events, the trainees’ behaviour, the time sequence,
and so on.
To fulfil such a requirement, the KB must reuse pre-existing information (e.g.,
in the Crisis Management scenario, training procedures, emergency management
guidelines) and, in the meantime, it must allow for customization and diversifica-
tion of training knowledge (e.g., emergency policies and legislation change from
country to country). Furthermore, since most of the related information can be
pre-existing in a variety of formats, the KB must able to gather information from
heterogeneous sources (e.g., location data from geographic datasets, audio and
video inputs from multimedia archives, participants profiles) and to synthetize
and interlink such knowledge into a coherent base.
Fig. 2. Role of the Knowledge Base in a Simulation Learning Environment
The role of the KB in the Simulation Learning Environment and its interac-
tions with other components is depicted in Figure 2:
– The KB is “initialized” by the trainer who models the simulation scenarios
and the training path alternative options;
– It is accessed by the Simulation Planner that needs to understand what
“events” should be triggered and presented to the trainees during the learning
sessions;
A Linked Knowledge Base for Simulation Learning 7
– It is also accessed by other system components that need to get/give infor-
mation about the training session and the knowledge exchanged during or
after its delivery (cf. Section 4);
– It is used to record the events and decisions taken during each training
session, in order to enable the semi-automatically creation of an individual
trainee debriefing report at the end of the training session.
To cope with such challenges, we adopted Linked Data and Semantic Web tech-
nologies for the design and development of our Knowledge Base.
3.3 Benefits from the adoption of Linked Data
The choice of Linked Data and Semantic Web technologies in our KB is mo-
tivated by the need for an easy access, (re)use and integration of data and
knowledge [13].
The ease of access to the KB is implicit in the use of Web technologies, which
represent a mature and established technology stack. Following the Linked Data
principles [14], we provide a standard access means to the data and knowledge
stored in the KB. Moreover, Linked Data and Semantic Web facilitate and enable
an entity-centric design of Web APIs: in our implementation, on top of the KB,
we have developed a RESTful service2 with specific methods to get details about
certain entities on the basis of the concepts (entity types) defined in the KB
ontologies and models (cf. Section 4). The RESTful service is also employed to
abstract from the physical location of data, as explained further on.
The reuse of pre-existing datasets is also enabled by our technological choice.
Several useful data sources are already present on the Web of Data and, thus,
immediately exploitable by the KB. For example, in the Crisis Management
scenario, environment characteristics of crisis settings are retrieved from GeoN-
ames3 , the geographical database containing over 10 million geographical names,
7.5 million unique features, 2.8 million populated places and 5.5 million alternate
names. For example, a scenario about a river flood or a earthquake benefits from
the retrieval of localized information from GeoNames. As a pragmatic solution,
we are “caching” the relevant features from GeoNames locally to the KB. How-
ever, the reuse of GeoNames URIs constitutes a link to the remote dataset and
allows for further knowledge retrieval. In the same way, we can connect the KB
to other knowledge bases like Freebase4 or DBpedia5 [15] to get information on a
number of general-purpose topics and entities. The linkage to the latter sources
is still in progress.
But this re-usability benefit applies also to the knowledge explicitly mod-
elled for domain-specific learning scenarios: the choice of RDF to encode the
data and of RDFS/OWL to model their structure pays, since those data are
partially published on the open Web, thus enriching the Web of Linked Data
2
Cf. http://pandoratest01.xlab.si:8080/pandora-ckb/.
3
Cf. http://www.geonames.org/.
4
Cf. http://freebase.com/.
5
Cf. http://dbpedia.org/.
8 Irene Celino and Daniele Dell’Aglio
and becoming available for other Simulation Learning systems or for different
tools. To this end, in our Crisis Management scenario, we decided to store the
schemata and data generated by Pandora components natively as RDF triples
in the KB; the knowledge coming from pre-existing sources in different formats
(e.g., taxonomies, spreadsheets, guidelines) have been converted – manually or,
whenever possible, semi-automatically – to a structured RDF format. The ben-
efits of this approach are: the general Crisis Management knowledge is available
to the whole community; the simulation scenarios can be reused by any installa-
tion of the training system; the further enhancements and extensions of the core
knowledge are immediately “reflected” in all systems that make use of our KB.
The ease of integration comes from the native interlinking capability of
Linked Data technologies. RDF provides the basic mechanism to specify the
existence and meaning of connections between items through RDF links [16]. In
other words, through the adoption of RDF, we not only give a structure to the
data stored in the KB, but we also interlink the entities described by such data.
Moreover, the links drawn between knowledge items are typed, thus conveying
the “semantics” of such relationships and enabling the inference of additional
knowledge. The information sources of the KB can be maintained and evolve
over time in an independent way, but, in the meantime, can be connected via
the Linked Data lightweight integration means.
The KB contains different (although interlinked) datasets, which also require
diverse confidentiality/security levels for management and access. To this end,
the KB is designed as a set of federated RDF stores6 : the shared knowledge (e.g.
general Crisis Management information, basic scenarios) should be “centralised”,
to let all training system instances access and use it, while the installation-
specific knowledge (e.g., detailed or customized scenarios, trainees information,
personalizations) is managed in a local triple store, not accessible from outside
the system (see Figure 3). The RESTful service on top of the KB, as explained
earlier, provides a uniform access to the KB and hides to the other Pandora
components the existence of the various “realms” of distinct Linked Data sources.
Finally, the adoption of Semantic Web technologies in the form of ontologies
and rules provides a further gain, since we can exploit reasoning and inference
for knowledge creation and consistency checking, as explained in next section.
4 Modelling and Retrieval in our Knowledge Base
As previously mentioned, our Knowledge Base manages several different and
interlinked types of information. In this section, we introduce three “families”
of data included in the KB and explain their modelling choices. We also illus-
trate their use in the Crisis Management training scenario within the Pandora
Integrated Environment.
6
In the Pandora project, since the work is still in progress and for now we have one
single system installation, the current initial release of the KB consists of a unique
triple store with all the integrated knowledge.
A Linked Knowledge Base for Simulation Learning 9
Fig. 3. The KB as federation of different triple stores to preserve security and confi-
dentiality while benefitting from interlinking.
4.1 User Modelling
As introduced in Section 2.2, a Behaviour Sensing module is devoted to the
“detection” of trainees’ performance in order to create individual models that
help in tailoring the learning strategy of each participant to the simulation.
Prior to the training session, dedicated psychological tests and physiological
assessment at rest (e.g., through a Holter that measures the heart rate activity
at rest) are used to measure some relevant variables (like personality traits,
leadership style, background experience, self-efficacy, stress and anxiety). Those
variables are then updated during the training session, through self-assessment
measurements (i.e., asking the trainee about his performance) or through the
elaboration of the row data recorded by the sensors.
Those data about trainees’ behaviour are stored and updated in our KB, as
instances of ontology concepts that represent the “affective factors” that influ-
ence the decision-making of the trainees. Due to the sensitivity of such infor-
mation, the individual performances of the trainees are modelled in RDF and
stored in the “local” triple store (cf. Figure 3) for apparent privacy reasons. We
are also investigating the possibility to exploit Named Graphs [17] for access
control: if the training session recordings are “stored” in the KB as separated
named graphs, a named graph-aware access control component could grant ad-
mission to the allowed users (e.g., the trainer) and could deny the access of the
malicious or occasional users (e.g., the other trainees).
In the specific scenario of the Pandora Integrated Environment, the learning
sessions are targeted to the training of Crisis Managers. Therefore, the KB stores
and manages also a set of specific information about them.
The Crisis Managers are the so-called Gold Commanders, who are responsible
for the strategic development of responses to crisis situations. The trainee group
10 Irene Celino and Daniele Dell’Aglio
is usually composed of the representatives of the “command team”, i.e. the core
agencies involved in the strategic Crisis Management (e.g., police, local authority,
fire brigade, ambulance); sometimes, other trainees can come from other utility
companies (e.g. electricity, road transportation, environmental agency).
In our KB, therefore, we modelled the basic knowledge about those Gold
Commanders by creating classes to represent the different trainees typologies.
Those classes are “instantiated” per each training session, by adding the in-
dividual trainees to the KB. This lets the system record the training of each
participant in relation to his/her role in the simulation; this knowledge is very
precious for both the debriefing phase – when the trainer summarizes the per-
formance results of each trainee (see also below) – and for a general analysis and
mining of the achieved objectives and learning needs of the different agencies.
The initial version of the user modelling is part of the Pandora Ontology7 .
4.2 Training Simulation Modelling
The core module of the simulation learning system is the Simulation Planning (cf.
Section 2.2). Our KB therefore must be able to manage the knowledge required
for the planning, in terms of the basic entities used by AI Planning Applications
based on Timeline Representations.
In literature, several attempts tried to formalize the semantics of planners
[18, 19]. However, those approaches, on the one hand, tried to specify a generic
planning ontology and, on the other hand, were specifically tailored to some
application domains.
Building on their experience, we decided to make our own formalization to
encompass the family of techniques known under the name of Timeline-based
Planning and Scheduling. In fact, current AI planning literature shows that
timeline-based planning can be an effective alternative to classical planning for
complex domains which require the use of both temporal reasoning and schedul-
ing features [10]. Moreover, our modelling aims to become the foundation for the
investigation on the interplay between Semantic Web Technologies and Planning
and Scheduling research [12]; Semantic Web knowledge bases, in fact, can rep-
resent a good alternative to the current domain modelling in the planning area,
which encompasses a multitude of custom and not interoperable languages.
Our modelling is formalized in a Timeline-based Planning Ontology8 . As
in classical Control Theory, the planning problem is modelled by identifying
a set of relevant features (called components) which are the primitive entities
for knowledge modelling. Components represent logical or physical subsystems
whose properties may vary in time; in the simulation learning, components are
either trainees behavioural traits or learning scenario variables. Their temporal
evolutions is controlled by the planner to obtain a desired behaviour. Therefore,
our ontology includes a set of time functions that describe the evolution over
temporal intervals. The evolution is modelled by events happening on modelled
7
Cf. http://swa.cefriel.it/ontologies/pandora.
8
Cf. http://swa.cefriel.it/ontologies/tplanning.
A Linked Knowledge Base for Simulation Learning 11
components. To this end, a set of planning rules (or synchronizations) specifies
what events can be triggered to modify these evolutions. The task of the Simu-
lation Planner is to find a sequence of events that brings the system entities into
a desired final state.
The core concept of the Timeline-based Planning Ontology is therefore the
planning rule: each rule puts in relation a “reference” event – which is the poten-
tial cause of some phenomena in the simulation – with a “target” event – which
is the possible consequence –, under a set of conditions called rule relations.
We modelled such conditions as SPARQL FILTER or LET clauses9 ; therefore,
we reused the modelling of such clauses and functions included in the SPIN
Modeling Vocabulary [20] and extended it with regards to temporal conditions.
At learning design time – i.e. prior to the simulation sessions –, the trainer
has to model the possible training scenarios, by instantiating in the KB the
ontology concepts, in particular the planning rules and the related events. The
choice of Linked Data and Semantic Web technologies for our modelling is not
only useful for reusing and exploiting pre-existing knowledge. In this case, we
can also exploit the semantics of such ontology for the consistency checking of
the simulation scenarios: by automatic means, we can check if all the planning
rules are satisfiable, if they represent possible “states” of the world simulated
during the sessions, if all the events can happen under opportune conditions,
and so on.
At run-time – i.e. during the simulation learning sessions –, all the events
and decisions taken by the trainees during their learning are recorded in the
KB. The KB is therefore used by the Simulation Planner to create and update
the simulation plan. SPARQL-based querying is used to perform the knowledge
retrieval required in this step: based on the actual recorded events, only the
admissible planning rules are returned to let the planner decide what events to
trigger.
After the learning session, at debriefing time, the recording of trainees’ be-
haviour and decision-taking is exploited to summarize the session progress. Also
in this case, SPARQL-based querying on the KB is exploited to retrieve all the
events and situations that involved each trainee; this knowledge is immediately
at disposal of the trainer to produce a debriefing report for each participant
and can be used to highlight personal performance, achieved training goals and
attention points for improvement or further training.
4.3 Asset Modelling
The Learning Delivery module (cf. Figure 1) takes as input the simulation plan
and “execute” it by sending the opportune stimuli to the trainee. To do this,
it needs to recreate the actual simulation conditions, by pretending a near-real
situation. For example, in the Crisis Management training scenario, the partic-
ipants must be solicited by phone calls, mail, news, videos, etc. that give them
9
The SPARQL LET clause is defined in some implementations, like the Jena Semantic
Web Framework http://openjena.org/
12 Irene Celino and Daniele Dell’Aglio
updates on the evolution of the emergency. To this end, the Learning Delivery
module manages two types of “learning objects” that are described in the KB.
The first type of simulation objects consists in audio and video assets, which
give information to the trainees about what happens outside the simulation
room. In the Pandora scenario, those assets are pre-canned recording of simulated
video news or audio inputs – like phone calls from the crisis setting – which
are used to put pressure on the trainees and, in the meantime, to give them
further inputs on which they must base their decisions. To model such assets,
it is possible to re-use existing learning objects modelling, such as [4, 21]. In
the Pandora project, we are still in the process of selecting the most suitable
modelling for our purpose.
There is a second type of stimuli for the simulation trainees. Since the sensing
system records the “performance” of each participant also in terms of stress and
anxiety, the simulation can be adapted to the specific conditions and deliver
tailored inputs for the individual trainees. For example, if the purpose is to
augment the pressure on a participant, the input could be made more dramatic.
To this end, the Learning Delivery module makes use of Non-Player Characters
(NPC): in games terminology, elements that act as a fictional agents and that are
animated and controlled by the system. Those NPCs simulate additional actors
from outside the learning environment and are used to deliver information to
the trainees.
Our KB, therefore, includes also the modelling of NPC descriptions, in terms
of their role in the simulation, their basic characteristics (e.g. gender, ethnicity,
disability), their profiles (expertise, experience, emotional type, communication
skills, etc.), their multimedia rendering mode (from the simplest text represen-
tation to fully rendered 3D avatar), etc. For this modelling, Linked Data are
exploited for the reuse of pre-existing descriptions and Semantic Web technolo-
gies are leveraged to retrieve and select the most suitable NPC to simulate a
desired stress or anxiety situation.
5 Towards Provenance Tracking
As detailed in the previous section, our Linked Knowledge Base is used to manage
the knowledge required to produce simulation-based learning sessions. We think
that Simulation Learning can be seen as a special case of the Open Provenance
Model (OPM) [22]. The sessions are our main process, the trainees, as well as the
simulated external characters, are our agents and the events and the decisions
taken by the trainees are the artifacts of the learning sessions.
Our future investigation will focus on the definition of the suitable OPM
Profiles for Simulation Learning systems; specifically, we aim at mapping our
Timeline-based Planning Ontology to the Open Provenance Model Vocabulary
Specification [23]. While this is still work in progress, hereafter we give some
hints on how we can build on the Open Provenance Model and why it is useful.
The provenance tracking in simulation learning can be done at two levels:
at design time – when the learning scenarios are modelled in the KB with their
A Linked Knowledge Base for Simulation Learning 13
possible planning rules –, and after the learning sessions – when the results of
the simulations are analysed.
At design time, provenance can be used to trace the cause-consequence chains
between the possible simulation events. As explained in Section 4.2, planning
rules are used to model the admissible transitions between events in the simula-
tion; the completion and inference rules defined in OPM [22] can be exploited for
the consistency checking of the simulation modelling. On the one hand, those
rules can help in refining the modelling, by eliminating useless entities, com-
bining eventual repetitions and introducing missing entities; on the other hand,
OPM rules can help in examining the possible decision-trees (i.e., the possible
alternative planning options) to identify unreachable states or decision bottle-
necks.
After the learning sessions, the simulation records can be analysed to under-
stand and synthetise the learning outcomes. Tracking the provenance of trainees’
decisions and mining the most popular causal chains across several sessions de-
livery can be of great help for identifying learning needs, common behaviours
(as well as common trainees’ mistakes), wide-spread procedures, etc. This infor-
mation can become of considerable importance: on the one hand, to improve the
learning simulations and better address learners requirements and, on the other
hand, to better study and interpret learning outcomes for individual participants
or for entire classes of trainees.
6 Conclusions
In this paper, we presented our approach and experience in building a Linked
Knowledge Base to support Simulation Learning systems. We introduced the
general architecture of such a system together with a concrete scenario in Crisis
Management training; we illustrated the benefits of the use of Linked Data and
Semantic Web technologies and we summarised our modelling choices. We also
suggested the introduction of provenance tracking, to further enrich and better
analyse the contents of a Knowledge Base for Simulation Learning.
Our approach is being integrated in the Pandora Environment, which, in the
second half of 2011, will be tested at the UK Emergency Planning College in
their “Emergency Response and Recovery” training courses.
Acknowledgments
This research is partially funded by the EU PANDORA project (FP7-ICT-2007-
1-225387). We would like to thank the project partner for their collaboration.
References
1. Stojanovic, L., Staab, S., Studer, R.: elearning based on the semantic web. In: In
WebNet2001 - World Conference on the WWW and Internet. (2001) 23–27
14 Irene Celino and Daniele Dell’Aglio
2. Tiropanis, T., Davis, H.C., Millard, D.E., Weal, M.J.: Semantic technologies for
learning and teaching in the web 2.0 era. IEEE Intelligent Systems 24(6) (2009)
49–53
3. Learning Technology Standards Committee of the IEEE: Draft standard for learn-
ing technology - learning object metadata. Technical report, IEEE Standards
Department, New York (2002)
4. Brase, J., Nejdl, W.: Ontologies and Metadata for eLearning. In Staab, S., Studer,
R., eds.: Handbook on Ontologies. International Handbooks on Information Sys-
tems. Springer (2004) 555–574
5. Aldrich, C.: Simulations and the Future of Learning: An Innovative (and Perhaps
Revolutionary) Approach to e-Learning. Pfeiffer (2003)
6. Caird-Daley, A., Harris, D., Bessell, K., Lowe, M.: Training Decision Making using
Serious Games. Technical report, Human Factors Integration Defence Technology
Centre (2007)
7. Sniezek, J., Wilkins, D., Wadlington, P., Baumann, M.: Training for Crisis
Decision-Making: Psychological Issues and Computer-Based Solutions. Journal
of Management Information Systems 18(4) (2002) 147–168
8. Stern, E., Sundelius, B.: Crisis Management Europe: An Integrated Regional Re-
search and Training Program. International Studies Perspective 3(1) (2002) 71–88
9. Lehto, M., Nah, F.: Decision-making Models and Decision Support. In: Handbook
of Human Factors and Ergonomics. John Wiley & Sons, Inc., NY (2006)
10. Cesta, A., Fratini, S.: The timeline representation framework as a planning and
scheduling software development environment. In: 27th Workshop of the UK Plan-
ning and Scheduling SIG. (2008)
11. Cesta, A., Cortellessa, G., Fratini, S., Oddi, A.: Developing an end-to-end planning
application from a timeline representation framework. In: 21st Applications of
Artificial Intelligence Conference. (2009)
12. Celino, I., Dell’Aglio, D., De Benedictis, R., Grilli, S., Cesta, A.: Ontologies, rules
and linked data to support crisis managers training. IEEE Learning Technology
Newsletter, Special Issue ”Semantic Web Technologies for Technology Enhanced
Learning” 13 (2011) Issue 1
13. Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space. 1st
edn. Volume 1 of Synthesis Lectures on the Semantic Web: Theory and Technology.
Morgan & Claypool (2011)
14. Berners-Lee, T.: Linked Data – W3C Design Issues, Architectural and philosophical
points, Online at http://www.w3.org/DesignIssues/LinkedData.html. (2006)
15. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hell-
mann, S.: Dbpedia – a crystallization point for the web of data. Journal of Web
Semantics: Science, Services and Agents on the World Wide Web 7 (2009) 154–165
16. Bizer, C., Heath, T., Berners-Lee, T.: Linked Data – The Story So Far. Interna-
tional Journal on Semantic Web and Information Systems 5 (2009) 1–22
17. Carroll, J.J., Bizer, C., Hayes, P., Stickler, P.: Named graphs, provenance and
trust. In: WWW ’05: Proceedings of the 14th international conference on World
Wide Web, ACM (2005) 613–622
18. Rajpathak, D., Motta, E.: An ontological formalization of the planning task. In:
International Conference on Formal Ontology in Information Systems (FOIS’04).
(2004) 305–316
19. Gil, Y., Blythe, J.: Planet: A sharable and reusable ontology for representing plans.
In: the AAAI - Workshop on Representational Issues for Real-World Planning
Systems. (2000) 28–33
A Linked Knowledge Base for Simulation Learning 15
20. Knublauch, H.: SPIN Modeling Vocabulary. http://spinrdf.org/spin.html (Oc-
tober 20, 2009)
21. Meta-information for Multimedia eLearning. In: Computer Science in Perspective.
(2003)
22. Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska,
N., Miles, S., Missier, P., Myers, J., Plale, B., Simmhan, Y., Stephan, E., den
Bussche, J.V.: The Open Provenance Model core specification (v1.1). Future
Generation Computer Systems (2010)
23. Zhao, J.: Open Provenance Model Vocabulary Specification. Online at http:
//purl.org/net/opmv/ns (2010)