=Paper=
{{Paper
|id=Vol-1442/paper_19
|storemode=property
|title=EDXL-RESCUER Ontology: an Update Based on Faceted Taxonomy Approach
|pdfUrl=https://ceur-ws.org/Vol-1442/paper_19.pdf
|volume=Vol-1442
|dblpUrl=https://dblp.org/rec/conf/ontobras/BarrosKSABPV15
}}
==EDXL-RESCUER Ontology: an Update Based on Faceted Taxonomy Approach==
https://ceur-ws.org/Vol-1442/paper_19.pdf
EDXL-RESCUER ontology: an update based on Faceted
Taxonomy approach
Rebeca Barros12 , Pedro Kislansky1 , Laís Salvador12 ,
Reinaldo Almeida1 , Matthias Breyer3 , Laia Gasparin Pedraza3 and Vaninha Vieira12
1
Federal University of Bahia
2
Fraunhofer Project Center for Software and Systems Engineering at UFBA
3
VOMATEC International GmbH
{rebecasbarros,laisns,vaninha}@dcc.ufba.br,{pedro.kislansky,reifa28}@gmail.com
{matthias.breyer,laia.gasparin}@vomatec-innovations.de
Abstract. This paper describes an ontology created for the RESCUER1 (Re-
liable and Smart Crowdsourcing Solution for Emergency and Crisis Manage-
ment), a project funded by the European Union and the Brazilian Ministry of
Science, Technology and Innovation. RESCUER uses crowdsourcing informa-
tion for supporting Industrial Parks (InPa) and Security Forces during an emer-
gency situation. The proposal, EDXL-RESCUER ontology, is based on EDXL
(Emergency Data Exchange Language), and it aims to be the RESCUER con-
ceptual model related to the coordinating and exchanging of information with
legacy systems. The ontology was evaluated with end-users during a workshop
and the results show that EDXL-RESCUER is adequate for Emergency and Cri-
sis domain in InPa and Security forces contexts. Specifically, this paper presents
an update of EDXL-RESCUER ontology based on a faceted taxonomy approach.
Resumo. Este artigo descreve uma ontologia criada para o RESCUER (Re-
liable and Smart Crowdsourcing Solution for Emergency and Crisis Manage-
ment), um projeto patrocinado pela União Européia e pelo Ministério de Ciên-
cia, Tecnologia e Inovação do Brasil. O RESCUER usa informação do público
para apoiar Parques Industriais e Forças de Segurança durante uma emergên-
cia. A ontologia proposta, EDXL-RESCUER, é baseada no EDXL (Emergency
Data Exchange Language) e pretende ser o modelo conceitual do RESCUER
relacionado à coordenação e troca de informação com os sistemas legados. A
ontologia foi avaliada com usuários finais durante um workshop, e os resul-
tados mostram que EDXL-RESCUER é adequada para o domínio de Crises
e Emergências nos contextos de Parques Industrias e Forças de Segurança.
Especificamente, este artigo apresenta uma atualização da EDXL-RESCUER
baseada em uma abordagem de taxinomia facetada.
1. Introduction
Crowdsourcing information (information that comes from different sources: peo-
ple affected by the incident, eyewitnesses, security forces and others) is becom-
ing widely used as a source of knowledge and solutions for different problems
1
http://www.rescuer-project.org/
[Beriwal and Cochran 2013][Besaleva et al. 2013][Eccher et al. 2013]. This paper is part
of a research project for developing a crowdsourcing solution for emergency manage-
ment, the RESCUER project [Villela et al. 2013]. RESCUER intends to provide com-
mand centers with real-time contextual information related to the emergency through the
collection, combination and aggregation of crowdsourcing information, and to support
announcements about the emergencies tailored to different audiences (e.g. authorities,
affected community and public).
The RESCUER project encompasses four main components as shown in Figure 1:
Figure 1. Conceptual model of RESCUER [Villela et al. 2013]
• Mobile Crowdsourcing Solution: support eyewitnesses communication with offi-
cial first responders (police, firefighters, etc.) and command and control centers.
The crowd can send information in text, image and video formats. It comprises a
set of mobile applications tailored to different platforms and devices;
• Data Analysis Solutions: composed of the algorithms that will process and filter
the data in order to extract the required information;
• Communication Infrastructure: offers the needed equipment in order to allow the
information to flow between the stakeholders; and
• Emergency Response Toolkit: a set of solutions to manage the analyzed crowd-
sourcing information and to present them to the command and control center using
adequate visualization metaphors.
The InPa (Industrial Park) Brazilian partner is the COFIC [COFIC 2009] (Indus-
trial Development Committee of Camaçari), which manages security simulations and
deals with legal procedures and media. The Security Forces are represented by the CICC
Figure 2. Ontology’s role in RESCUER system [Villela et al. 2013]
(Integrated Command and Control Centre) in Brazil and by the FIRESERV2 in Europe.
These partners have contributed to the project with expertise and knowledge on how com-
mand and control centers operate in large-scale events, as well as in industrial areas. In
this context, interoperability between the RESCUER project and legacy systems’ part-
ners is critical for the success of the solution. For the purpose of semantic and seamless
integration of legacy systems with RESCUER, the use of ontologies seems to be most
suitable, since they offer a basis for a shared and well-formed specification of a particular
domain. Thefore, in this proposal, an ontology is presented that will comprise the RES-
CUER conceptual model related to the coordinating and exchanging of information with
legacy systems.
From this perspective, the use of a well-referenced standard by the scientific com-
munity, the EDXL [OASIS 2014] – Emergency Data eXchange Language-, as a ba-
sis for the new ontology was chosen. EDXL is a common standard that is accepted
and used in several applications dealing with disaster management [Genc et al. 2013]
[Kilgore et al. 2013]. It is composed of several packages – the current standard version
has seven packages, each of which is related to a particular aspect of the emergency do-
main. A subset of EDXL has been chosen in order to specify the EDXL-RESCUER
ontology (details of this process are discussed in section 3). The formalization of the first
version of the ontology can be found in our previous paper [Barros et al. 2015]. As an in-
cremental approach is being used, in this paper an update of EDXL-RESCUER ontology
based on faceted taxonomy formalization is presented
The Figure 2 shows the ontology-based integration module (green box) in RES-
CUER architecture, specifically in its interaction with ERTK. This module will have three
main parts: 1) the EDXL-RESCUER that works like a global ontology; 2) the database
schemas from ERTK and legacy systems involved; 3) mappings between the ontology and
the local schemas.
The evaluation of EDXL-RESCUER ontology was performed in two steps:
1. Validation through competency questions - questions that an ontology should
be able to answer. This validation is based on a well know method
in Ontology Engineering (for further information see the TOronto Vir-
tual Enterprise (TOVE) [Grüninger and Fox 1995] and the METHONTOLOGY
2
http://www.fireserv.at/
[Fernández-López et al. 1997]).
2. Brainstorming with potential end users for validating the ontology terms. The
results show that the EDXL-RESCUER ontology is suitable for specific goals
proposed in RESCUER project.
This paper is structured as follows: in the Related Works section, research projects
related to emergency, ontology and interoperability are presented. Next, an EDXL-
RESCUER ontology and its update based on faceted taxonomy approach is presented.
In the Evaluation section the workshop with end-users is described and the results de-
rived are presented; finally, a conclusion of the work done and future developments are
presented.
2. Related Works
Ontology has been used on several domains in order to solve interoperability prob-
lems, including emergency and crisis domain [Eccher et al. 2013] [Mescherin et al. 2013]
[Shah et al. 2013] [Shan et al. 2012] [Xiao et al. 2013].
Based on review of related literature, one project stood out: the DISASTER (Data
Interoperability Solution at Stakeholders Emergency Reaction) project [Azcona 2013]
[Schutte et al. 2013]. It mainly focuses on Data-Interchange (or more specifically, Data-
Artefact-Mapping) on a semantic level. In this project an ontology has been created
(EMERGEL) whose main objective was the mapping of different predefined informa-
tion artifacts, information representations and languages between countries in Europe. In
a RESCUER context, the EMERGEL ontology seems to be quite useful as an up-to-date
database, if the task of semantically mapping incident information was the objective. For
all other aspects needing to be addressed, the interoperability with legacy systems, for
instance, EDXL seemed to be more suitable. However, the use of EMERGEL may be
investigated in the future for enabling cross-border incidents in Europe.
In addition to the DISASTER project, several works that use ontologies and EDXL
in the context of Emergency and Crisis Management were found. Some of these are
presented in this section.
The IC.NET (Incident Command NET) is a system that can be used for Emergency
Services such as incident representation, triage, and more. It is based on EDXL-DE as
a top level loose coupler used for delivery and exposure of operational level Emergency
Services / First Responder data [McGarry and Chen 2010].
The TRIDEC3 project is based on the GITEWS (German Indonesian Tsunami
Early Warning System) and the DEWS (Distant Early Warning System). It provides a ser-
vice platform for both sensor integration and warning dissemination. Warning messages
are compiled and transmitted in the OASIS Common Alerting Protocol (EDXL-CAP)
together with addressing information defined via the OASIS Emergency Data Exchange
Language - Distribution Element (EDXL-DE) [Hammitzsch et al. 2012].
WebPuff is a system sponsored by the U.S. Army CMA(Chemical Material Activ-
ity)and developed by IEM, a security consulting firm based in North Carolina’s Research
Triangle Park. WebPuff provides users at CSEPP (Chemical Stockpile Emergency Pre-
paredness Program) sites with a suite of planning and response tools that are integrated
3
Project Collaborative, Complex and Critical Decision-Support in Evolving Crises
with a unique chemical dispersion model that provides an advanced level of science on
which decisions about public protection can be based.
In order to ensure interoperability with civilian jurisdictions, the system uses the
Emergency Data eXchange Language (EDXL) Common Alerting Protocol (CAP) de-
veloped by the Organization for the Advancement of Structured Information Standards
(OASIS) [Beriwal and Cochran 2013].
The German Research Centre for Geosciences developed a model for integrating
the national tsunami warning system on a large scale. They proposed a system based on
existing protocols such as EDXL Common Alert Protocol (EDXL-CAP) and the Distri-
bution Element (EDXL-DE) [Lendholt et al. 2012].
3. EDXL-RESCUER Ontology
EDXL is a set of packages of XML-based messaging standards that favor emergency
information sharing between organizations and systems. EDXL standardizes messaging
formats for communications between these parties. It was developed by OASIS (Organi-
zation for the Advancement of Structured Information Standards) [OASIS 2014]
EDXL is a broad enterprise to generate an integrated framework for a wide
range of emergency data exchange standards. The EDXL has several packages: EDXL-
DE (Distribution Element); EDXL-RM (Resource Messaging); EDXL-SitRep (Situation
Reporting); EDXL-HAVE (Hospital Availability Exchange); EDXL-TEP (Tracking of
Emergency Patients); EDXL-CAP (Common Alerting Protocol) and EDXL-RIM (Refer-
ence Information Model) [OASIS 2014].
An ontology for the semantic integration of data exchange between the RESCUER
platform and legacy systems has been defined based on EDXL standards. The current
version of EDXL has seven (7) packages and covers a full range of message contexts
in an emergency. The extended scope of EDXL has raised several questions, including:
(i) Should an ontology be constructed for all packages? (ii) What message contexts are
important for RESCUER? (iii) What kind of information will be exchanged with legacy
systems?
In order to clear up these doubts, other RESCUER documents related to Requisites
and Architecture tasks were analyzed. They were chosen because they provide useful
information that can be used in semantic integration of RESCUER with legacy systems.
Based upon this study, a list of competency questions can be designed, which serve as a
basis for the selection of EDXL packages for RESCUER domain.
Therefore, in order to address these questions, four packages were chosen: EDXL-
DE, EDXL-RM, EDXL-SitRep and EDXL-CAP. Four new ontologies were created, one
for each chosen package. These were based on ERM and Data Dictionary of their
associated standard. These four ontologies comprise the EDXL-RESCUER ontology
and the formalization of the first version of them can be found in our previous paper
[Barros et al. 2015].
With this first version of the ontology, a validation through competency questions,
where each competency question is related with the correspondent ontology elements can
be performed, as seen in (Table 1). In this way, the selection of EDXL packages can be
validated. This validation also contributes to a first step of evaluation of the ontology.
Table 1. Competency questions X EDXL-RESCUER ontology
Competency Ontology element
Questions correspondent
EDXL-RM owl:Class Location
Where was the incident?
EDXL-CAP owl:Class Area
EDXL-CAP owl:Class Category
What kind of incident was it?
EDXL-SITREP owl:Class IncidentCause
Which resource (human or material) EDXL-RM owl:Class RequestResource
will be necessary? or another ResourceMessage subclass
When (date and time) did EDXL-SITREP owl:DataProperty
the incident happen? incidentstartdatetime
EDXL-SITREP owl:DataProperty
What is the weather forecast?
weatherEffects
EDXL-SITREP owl:Class
How many people have been affected?
CasualtyandIllnessSummaryReport
(deaths, injuries, evacuations)
and related properties
Who reported the incident? EDXL-DE owl:Class Sender
What kind of message content
EDXL-DE owl:Class ContentDescription
was sent by the workforces?
As an incremental approach is being used, in this paper an update of EDXL-
RESCUER ontology based on faceted taxonomy formalization as well as its implementa-
tion is presented.
3.1. Update of EDXL-RESCUER Ontology
In order to update the EDXL-RESCUER [Barros et al. 2015], we made an in-depth anal-
ysis of the data model for the EDXL scheme. During this process, a natural way was
to choose Prieto-Diaz proposal [Prieto-Diaz 1987], a technique used for classifying con-
cepts called Faceted Taxonomy. This approach uses a faceted taxonomy with the purpose
of improving and reviewing an existing domain ontology. The facets handle three or more
dimensions of classification and can be used when it is possible to organize the entities by
mutually exclusive and jointly exhaustive categories.
In line with this approach, in [Denton 2003] a method is presented for making
a faceted classification using seven steps. These steps adapted for EDXL-RESCUER
ontology update are shown below:
a) Domain collection: we used the EDXL Documentation;
b) Entity listing: we listed all entities found;
c) Facet creation: we arranged all entities that resembled under a main entity, the
facet (main entity was chosen to represent a domain segment EDXL);
d) Facet arrangement: we made sure that the entities resembled to the associated
facets, reorganizing them when appropriate, (the checks were made through the
EDXL documentation, which contains the description and data model for the en-
tities).
e) the citation order and f) classification – phases that refer to how the taxonomy
would be implemented. In our case, the goal was the creation of an ontology, then
we defined what every element under a facet and the facet itself would be in an
OWL ontology, i.e. what is a class, sub-class, object property, and data property.
g) The last phase included revision, testing, and maintenance: the result of this
phase is EDXL-Rescuer v2.
Figure 3. Review and building process of ontologies - Based on
[Prieto-Díaz 2003]
Figure 3 summarizes the entire process of the EDXL-RESCUER update. The first
version of the ontologies that composed the EDXL-RESCUER relied on EDXL docu-
mentation and the ERM models available there. Hence, a faceted taxonomy based on the
same documentation, which allowed one to better detail the domain of each chosen pat-
tern was created. Moreover, we were able: (i) to determine the main concepts with higher
precision; and (ii) to use the results for reviewing and revalidate the ontologies created at
the first iteration.
For instance, the concepts Severity, Urgency and Certainty found in EDXL-CAP.
After the procedure previously mentioned (the concepts reviewing), those concepts be-
came classes instead of DataProperties. Those classes received sub-classes with the ability
to have different values according to the EDXL documentation as can be seen in Figure 4.
Another improvement from the previous version is that we were able to reuse
common concepts among more than one type of EDXL pattern. Hence, Severity, Ur-
gency and Certainty, which EDXL-SitRep also employs, they are imported concepts from
EDXL-CAP; therefore the URI is the same as found on the original ontology.
The approach based on faceted taxonomy seemed to be adequate, considering that
this technique for classifying concepts is characterized by randomly choosing the terms
that represent concepts within a domain (facets). Furthermore, it chooses the relationship
between other domain terms and the terms previously chosen, creating categories (each of
which is related with a facet). Finally, the faceted approach selects the terms and the rela-
tionship between them within the same category or between categories [Dahlberg 1978]
Figure 4. Concepts Urgency, Severity and Certainty - Partial Taxonomy of EDXL-
CAP
[Prieto-Díaz 1990]. Additionally, a faceted approach relies not on the breakdown of a
universe of knowledge, but on building up or synthesizing from the subject statements
of particular documents and that facet can be constructed as perspectives, viewpoints, or
dimensions of a particular domain [Prieto-Díaz 2003].
Table 2. Relationship definitions (EDXL-CAP)
Concept1 Relationship Concept2 Restriction
AlertMessage hasIncidentRelated Incident some
AlertMessage hasInfo Info Min 0
AlertMessage hasMsgType MsgType Max 1
AlertMessage hasScope Scope Max 1
AlertMessage hasStatus Status Max 1
AlertMessage hasSender Sender Max 1
Info hasArea Area some
Info hasCategory Category Max 1
Info hasResource Resource some
Info hasResponseType ResponseType Max 1
Info hasCertainty Certainty Max 1
Info hasSeverity Severity Max 1
Info hasUrgency Urgency Max 1
3.2. Implementation
Due to space limitation, only part of the EDXL-RESCUER ontology is shown. The con-
cepts that make up the EDXL-CAP Ontology and their definitions are:
• AlertMessage: Refers to all component parts of the alert message.
• Info: Refers to all component parts of the info sub-element of the alert message.
• Resource: Necessary element to deal with an emergency. A Resource contains
information about its Identity, Description and Status.
• Incident: Term referring to occurrences of any scale that may require some form
of Emergency Response and Management, and that requires tracking and infor-
mation exchange.
• ResponseType: Refers to the type of action recommended for the target audience.
• Area: Refers to all component parts of the area sub element of the info sub element
of the alert message.
• Category: Refers to the category of the subject event of the alert message
• MsgType: Refers to the nature of the alert message.
• Status: Refers to the appropriate handling of the alert message.
• Scope: Refers to the intended distribution of the alert message.
• Sender: The originator of an alert.
• Certainty: The certainty of the subject event of the alert message
• Severity: The severity of the subject incident or event.
• Urgency: The urgency of the subject event of the alert message
Table 2 presents the definition of their relationships. The following semantics are
used:
Zero or more objects of with ob-
jects of .
Where can be some, all, Max 1, Min 0, Exactly 1. Min 0 is
the default value.
Some axioms have also been defined, for instance: (i) Private, Public and Re-
stricted - subclasses of Scope – are disjoint concepts; (ii) Actual, Draft, Exercise, System
and Test – subclasses of Status - are disjoint concepts too.
4. Evaluation
The evaluation occurred during the RESCUER Brazilian Consortium Meeting on July
21-23, 2014 and had the goal of validating the terms with potential RESCUER users in
Brazilian side. Next, the Goals, Method and Results of this evaluation will be presented.
4.1. Goals
• To present some ontology terms to the stakeholders - terms which were chosen be-
cause they represent the main classes of the selected EDXL packages and were the
most controversial for both industrial parks (InPa) and large-scale events (LSE);
• To match those terms with the vocabulary the stakeholders use on a daily basis
in order to extract synonyms and verify differences, if differences exist, between
InPa and LSE.
4.2. Method
The “brainstorm technique” was used in order to capture stakeholder feedback concerning
the ontology terms.
The stakeholders were divided into two groups;
• Industrial parks (COFIC)
• Large-scale events (CICC)
During this session, the EDXL concepts were shown to the experts and they tried
to find synonyms or correlated terms used in their contexts. At the end of the session, there
was an open discussion about the findings related to main concepts of EDXL-RESCUER
ontology.
4.3. Results
Based on the activity conducted with the stakeholders, it can be deduced:
• The concepts related to EDXL-SitRep package, in the COFIC context, were suit-
able;
• Some concepts, for instance the term “incident”, had minor variations between the
two groups;
• Almost all EDXL terms had related instances or synonyms according to this ac-
tivity.
• The exception was the term “Jurisdiction”, which did not have an instance or a
synonym for COFIC. However, at CICC, was found a related instance.
• Some collected terms can be used as instances for populating the EDXL-
RESCUER ontology in the future.
This activity raised some important conclusions:
• The necessity of validating all concepts with Brazilian stakeholders;
• A deep investigation of the differences between industrial parks and large-scale
events in Brazil; and
• The need to replicate this activity in the European scenario
It is important to note that the differences between the scenarios (COFIC and
CICC) emphasize the need for an Interlingua and the relevance of this proposal - EDXL-
RESCUER as a common basis for communication.
5. Conclusion
This paper discuss the conceptual model for semantic integration – EDXL-RESCUER on-
tology. It aims to integrate, semantically, the RESCUER system with legacy systems. In
particular, this paper presents an updated version of the ontologies that composed EDXL-
RESCUER based on a faceted taxonomy approach. This approach relied on a bottom-up
analysis of the EDXL documentation in order to synthesizing the subject statements of
these documents. It is important to note that the construction of facets provides different
perspectives and views of the domain. In this way, we were able to review our first version
of EDXL-RESCUER ontology and adjust its concepts and relationships.
Moreover, in regards to the evaluation, the legacy systems information and data
are still missing, as well as the data from RESCUER base. After populating the EDXL-
RESCUER ontology, we are going to validate it using reasoning algorithms and queries.
Another step is to implement the ontology-based integration module between RESCUER
and legacy systems.
Some further investigations will be carried out as well: (i) the use of LOD (Linked
Open Data) in this context; (ii) the use of the EMERGEL-knowledge base as an additional
controlled vocabulary or just as a synonym-base.
6. Acknowledgements
This research is part of the RESCUER (Reliable and Smart Crowdsourcing Solution for
Emergency and Crisis Management) project funded by European Union under grant ref-
erence 614154 and by CNPq under grant reference 490084/2013-3. This work also was
partially supported by the National Institute of Science and Technology for Software En-
gineering (INES), funded by CNPq, grant 573964/2008-4.
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