Knowledge-Based Systems 24 (2011) 1372–1379

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Knowledge-Based Systems

journal homepage: www.elsevier .com/locate /knosys

A knowledge-based framework for emergency DSS

C. De Maio b, G. Fenza b,⇑, M. Gaeta a, V. Loia b, F. Orciuoli a

a Department of Information Engineering and Applied Mathematics & CRMPA (Centro di Ricerca in Matematica Pura ed Applicata), University of Salerno, 84084 Fisciano (SA), Italyb Department of Computer Science & CORISA (Consorzio Ricerca Sistemi ad Agenti), University of Salerno, 84084 Fisciano (SA), Italy

a r t i c l e i n f o

Article history:Received 9 December 2010Received in revised form 4 April 2011Accepted 16 June 2011Available online 25 June 2011

Keywords:Emergency managementFuzzy Cognitive MapsSemantic WebSemantic Web servicesDecision Support System

0950-7051/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.knosys.2011.06.011

⇑ Corresponding author.E-mail addresses: cdemaio@unisa.it (C. De Maio)

gaeta@diima.unisa.it (M. Gaeta), loia@unisa.it (V. LoiOrciuoli).

1 http://www.w3.org/2005/Incubator/eiif/.2 http://www.tacticalsituationobject.org/.3 http://www.w3.org/2005/Incubator/eiif/XGR-EIIF-2

a b s t r a c t

Emergency management requires a shared vision on everything that happens nearby the emergency zoneand on the availability of resources enabling to face emergency situations. Specifically, emergency man-agers need to be concretely supported, by knowledge-based systems, to make critical decisions. Thiswork introduces a framework that exploits Semantic Web technologies to harmonize heterogeneous dataand soft computing methods in order to handle uncertainties and to model causal inference embeddedinto an emergency plan. In particular, the paper presents an approach based on Fuzzy Cognitive Maps(FCMs) to support knowledge processing and resources discovery according to the emergency features.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction emergency domain ontologies that rely on existing emergency stan-

In situations where emergencies have to be handled, it is vital toprovide a vision, shared by all involved actors, on everythinghappening near the geographic area interested by the emergencyand on the availability of resources like hospitals, ambulances,volunteers and so on. Moreover, operators need to be concretelysupported, by knowledge-based systems, to make critical decisionsand to coordinate the above said resources.

On the one hand, knowledge sharing is a mandatory step in emer-gency situation. In fact, W3C Emergency Information Interoperabil-ity Framework Incubator Group (EIIF XG)1 have convened to explorethe creation of interoperability standards in the emergency and disas-ter management domain. Specifically, EIIF XG has introduced someXML based technical protocols, such as TSO2 (Tactical SituationObject) and CAP3 (Common Alerting Protocol). Nevertheless, thesestandards lack of semantics. Furthermore, emergency managementrequires to be aware of the availability of people, services, organismsand their localization in order to support the resources managementand coordination. In order to fill the gap, Semantic Web puts the stresson the binomial ‘‘meaning and content’’ allowing large scale interop-erability across heterogeneous information sources. So, this workemphasizes the role of ontologies artifacts [1] by introducing

ll rights reserved.

, gfenza@unisa.it (G. Fenza),a), orciuoli@diima.unisa.it (F.

0090806/.

dards (like TSO and CAP) and by adding semantics to web resources(i.e., people, services, etc.).

On the other hand, the knowledge processing able to supportemergency decision making by means of semantic models is stillan open question. Usually, emergency management activities fore-see that resource plans are designed and applied in order to answerto a specific emergency situation. In particular, given the featuresof a specific emergency event there exists a set of actions that mustbe performed with a suitable assignment of resources (i.e. organ-isms, actors, skills, vehicle, etc.) according to emergency’s features,geographic information, current resources availability and so on.

This work proposes a framework (see Section 3) attaining a syn-ergy among semantic technologies and soft computing techniques.It is strongly based on Semantic Web-based modeling of geo-spa-tial data, geo-localized services, peoples skills and geo-positionsand on Fuzzy Cognitive Maps (FCMs) as suitable mathematicalmodel able to support emergency decision making.

2. Related works

This work focuses on two aspects of the emergency decisionsupport system: knowledge sharing, by introducing the semanticmodeling of both domain knowledge and geo-located resources;knowledge processing, by exploiting soft computing methods in or-der to support the decision making.

Nowadays, the introduction of semantics in the description ofWeb resources reflects new achievements in Web Services andSocial Networks technologies. Semantic Web services allow to auto-mate services selection, composition, monitoring, etc. [2]. Semanticsocial networks often integrate geo-located information in order to

C. De Maio et al. / Knowledge-Based Systems 24 (2011) 1372–1379 1373

localize people [3]. In the light of the described scenario, this workexploits a semantic modeling of resources such as, people, services,vehicles, and so on, according to the emergency skills and localiza-tion. The geographical aspect plays a critical role to support emer-gency management [4]. Nevertheless, the availability ofemergency ontologies is not enough to support emergency decision.In literature there are many approaches that define emergency Deci-sion Support System driven by ontologies [5–7]. Nevertheless, ma-ture emergency domain ontologies with the exception of theCrime Emergency Ontology in [8] still do not exist.

Moreover, causation and classification tasks play a central roleespecially in order to handle emergency decisions. In the past years,both Bayesian network and FCMs were mainly exploited in litera-ture to model causal inference. Since BN is based on the traditionalprobability theory, it is difficult to handle uncertainties such asvagueness, granulation and natural language descriptions. FCMs of-fer an alternative solution to this problem. FCMs have been success-fully exploited in geographical information systems [9], web datamining [10,11], information retrieval [12]. More recently, FCMs havegained considerable attention and offer an alternative framework tomodel human decision making process and causal inference [13],also in medical diagnosis [14,15] and distributed decision processmodeling [16]. Specifically, in this work FCMs are exploited in orderto support the decision maker during an emergency. In particular,this work defines a hybrid approach able to evaluate emergency cau-sal inference and to find eligible available resources (i.e. people andservices) which appropriately meet the emergency requirementsand parameters that are partially known [17].

3. Framework overview

In this section an overview of the proposed framework for emer-gency management is illustrated. The overall architecture is re-ported in Fig. 1 and is intended as a basis for developingapplications to support emergency managers when handling emer-gency situations through the utilization of semantic, social, softcomputing and web technologies. Fig. 1 depicts the interactionsamong the following elements.

Emergency Manager: a user who has to coordinate resourceslocalized within the emergency zone. The user receives informa-tion to face with the emergency situation, selects the mostadequate resources on the basis of the specific situation, coordi-nates the selected resources in order to resolve the emergencysituation.Input represents data (e.g. latitude, longitude and radius of theemergency zone, emergency type, constraints, etc.) that emer-gency manager provides (to the Interaction Manager) in order

Fig. 1. Emergency Mana

to: (i) select the right resources to address a specific emergencysituation and (ii) design a suitable emergency plan.Output: the result obtained by emergency managers when theyask (by means of the Interaction Manager) for the selection ofmost suitable resources or ask for some kind of suggestionsfrom the Decision Support System.Stimuli: signals or messages received by emergency managers.Stimuli could be phone calls, geo-tagged images embedded inMMS (this kind of data can be automatically processed in orderto capture the geo-localization of the emergency situation),SMS, Instant Messaging posts, Twitter posts, service messages,etc. transmitted through the Communication Interfaces (fromhuman resources mobile devices) and Services Interfaces (fromservices). The stimuli imply the start of an emergency manage-ment session or the adaptation of an existing emergency plan.Directives: messages sent by emergency managers to coordi-nate volunteers with respect to the emergency plan and trans-mitted through Communication Interfaces (to human resources)and Services Interfaces (to services).Interaction Manager: the module providing suitable userinterfaces (e.g. mash-up using google maps, taxonomies andother search facilities) to allow emergency managers to selectresources to engage in emergency situations and to receivethe selection results. The Interaction Manager also providesmechanisms to handle Stimuli toward emergency managers.On the other hand, this module accesses the Decision SupportSystem to obtain suggestions or to make decisions and to build(or adapt) plans.Decision Support System: the module exploiting soft comput-ing methods and algorithms in order to provide decision mak-ing support on resource planning and coordination inemergency situations.Semantic Middleware: the module providing mechanisms toquery, store and populate information about people and ser-vices that can be activated during emergency situations. TheSemantic Middleware also acts as an integration layer usingontologies and W3C Semantic Web languages.Interfaces: classified into four types:

� Geo-Social Networks Interfaces. These interfaces are usedto connect existing (e.g. Google Latitude, Foursquare, VINE, etc.)or new geo-social networks by means of specific APIs in orderto manage a community of volunteers and capture the geo-posi-tion of community members by means of their GPS-enabledsmartphones constantly communicating their positions. Thisinformation is semantically organized by the Semantic Middle-ware. At the moment, the available APIs do not offer all the neededfunctionalities, so a from scratch implementation is required.

gement Framework.

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� Services Interfaces. These interfaces are used to connectexisting services (e.g. SOAP Web Services, REST, etc.) to be usedduring emergency situations. As well as the geo-social networksdata, the information provided by these interfaces is semanti-cally organized by the Semantic Middleware.� People’s Information Interfaces. In order to gather peo-

ple within the volunteers’ community, additional (to thoseoffered by the geo-social networks) data about members hasto be provided. It is plausible to connect specific applicationsproviding questionnaires to people when joining the volunteer’scommunity.� Communication Interfaces. These interfaces enable the

communication from emergency managers to volunteers, sup-porting coordination, and from volunteers to emergency man-agers, supporting feedbacks and allowing emergency plansadaptation. The messages go across the Semantic Middlewarewhen date storing and harmonization is needed.

In the next sections the most critical modules, i.e. Decision Sup-port System and Semantic Middleware, will be explained and dis-cussed in depth.

4. Semantic Middleware

The Semantic Middleware exploits W3C Semantic Webschemes, vocabularies and tools based on the stack composed byRDF, RDFS and OWL. The aforementioned languages are used tomodel and represent information in machine-understandable,interoperable and standards-accessible ways. There are severaladvantages due to the adoption of Semantic Web technologies. Inparticular, the Semantic Web technologies enable the effectiveand efficient cooperation among different applications. This coop-eration is sustained by the adoption of the same representationand data modeling language (e.g. XML and RDF) and by the adop-tion of the same languages and schemes (e.g. RDFS, OWL, etc.).Linking and integrating data, integrating different schemes andsharing the semantics of data across different applications are sim-plified by means of the Semantic Web language stack. Further-more, Semantic Web enables the use of standard reasoningtechniques, query languages and inference methods in order toprovide a great framework for applicative scenarios building. Forinstance, the application of the mash-up pattern, a useful tech-nique able to move the scenarios building at an end-user level, isfostered by the use of SPARQL4 query language. The aforementionedadvantages are especially valid for the proposed emergency manage-ment framework, given that it is required to link, aggregate and har-monize data, information and contents that are provided by differentapplications and communication channels. Specifically, two key ele-ments exist: domain ontologies (i.e. geographic information, humanskills, emergency environment, etc.), upper ontologies (OWL-S,5

Friend of a Friend – FOAF6 and ResumeRDF7) enabling resource mod-eling. Furtermore, the OWL-based modeling allows to use OWLrestrictions (and other contructs) and available tools (also opensource software) in order to provide an API layer for inserting, infer-ring and querying data organized with upper and domain ontologies.The APIs are mostly used by the Decision Support System. TheSemantic Middleware also provides a set of components able togather data coming from several Interfaces (see Section 3), wrapthem with schemes used for upper and domain ontologies, and storethem on the RDF Storage making them available for the API layeraccess.

4 http://www.w3.org/TR/rdf-sparql-query/.5 http://www.daml.org/services/owl-s/.6 http://www.foaf-project.org/.7 http://lsdis.cs.uga.edu/aleman/efw2007/bojars__efw2007.html.

4.1. Domain ontologies and taxonomies

Generally, a domain ontology models a specific domain, or partof the world. It represents the particular meaning of terms as theyapply to that domain. Specifically, this work deals with domainontologies (based on OWL) and taxonomies (based on SKOS8) use-ful to semantically model concepts and related relationships appliedon the emergency domain.

In order to guarantee standards compatibility, the developmentof domain ontologies and taxonomies relies on the existing emer-gency standards. Specifically, according to TSO and CAP, we defineemergency environment taxonomies in terms of: occurring events,involved actors (e.g. animals, children, women, etc.), involvedstructures (e.g. hospital, dwelling places, etc.), emergency degreeand so on. Analogously, we define a taxonomy model representingpeoples skills coherently to the emergency scenario. The taxonomymodeling operations are performed using the SKOS schema.

4.2. Upper ontologies: semantic models of services and people

In this section we will provide upper semantic models enablingrepresentation of services and people by means of an instantiationof Upper Ontologies: FOAF, ResumeRDF to semantically representpeople profiles, OWL-S to semantically model available services.Details about these models are given below.

4.2.1. Modeling of people’s informationTo discover located people, in this work we present an Aug-

mented Geo-social Network Model where individuals (volunteers,professionals, etc.) provide their profile data to some authorizedorganizations that can exploit to face emergencies. Profile datamainly consist of competencies and geographic position. Fig. 2shows the conceptual model of Augmented Geo-social Networks.It represents the ontology based modeling of people constructedon top of the model of Geo-social Networks defined in [3], by con-sidering skills dimension and by exposing information usingSemantic Web schemes.

In order to satisfy interoperability issues and to exploit existingmodeling efforts and tools we introduce the harmonized use [18]of FOAF and ResumeRDF in order to model human resource inthe proposed architecture. The FOAF project has become a widelyaccepted standard vocabulary for representing social networks,and many large social networking websites are using it to produceSemantic Web profiles for their users. The way it is used satisfiesthe goal utilizing an ontology to represent considerable amountsof distributed data in a standard form. However, for FOAF to trulyserve as an example of the Semantic Webs full potential, reasoningover the data must lead to the discovery of connections betweenwhat is represented as distinct data sets. FOAF provides a way toorganize basic information about a person [19] (e.g. Person, first-Name, surname, nick, title, mbox, etc.), other personal data (e.g.interest, knows, etc.), online accounts (e.g. icqChatID, msnChatID,jabberID, etc.), groups information (e.g. Organization, Group, Pro-ject, etc.) and documents information (e.g. PersonalProfileDocu-ment, Image, etc.).

In order to describe the typical latitude and longitude of an indi-vidual of type foaf:Person it is possible to use the RDF version of theWorld Geodetic System from 1984 (WGS84).9 WGS84 is a standardway of representing points on the earth surface, and is widely usedbeing the encoding used by the Global Positioning System (GPS).The following code fragment shows how to geo-localize an individ-ual modelled with a FOAF profile.

8 Simple Knowledge Organization System - http://www.w3.org/TR/skos-reference/.9 http://www.w3.org/2003/01/geo/#vocabulary.

Fig. 2. Conceptual model of Augmented Geo-social Networks.

C. De Maio et al. / Knowledge-Based Systems 24 (2011) 1372–1379 1375

<foaf:Person rdf:ID=‘‘FrancescoOrciuoliFOAFPerson’’><geo:location>

<geo:Point rdf:ID=‘‘Position_01’’><geo:lat rdf:datatype=‘‘#string’’>-76.850706</geo:lat><geo:long rdf:datatype=‘‘#string’’>39.184543</

geo:long></geo:Point>

</geo:location>. . .

</foaf:Person>

Nevertheless, FOAF does not cover aspects like skills. So we

need to have recourse, in synergy with the FOAF schema, toResumeRDF [20]. ResumeRDF is an ontology developed in orderto express, on the Semantic Web, the information contained in aresume, such as business and academic experience, skills, publica-tions, certifications, etc. For instance, ResumeRDF provides cv:CV,cv:Person and cv:Skill that are classes we can use to model skillsand to link them to FOAF profiles. The link between a FOAF Profileand a ResumeRDF instance could be set by using the owl:sameAsproperties supported to perform inference using common OWLreasoners. So, if A owl:sameAs B then in any triple (RDF) wherewe see A, we can infer the same triple with A replaced by B.

Fig. 3. Linking FOAF and SKO

ResumeRDF does not provide the possibility to describe skilltaxonomies but it only offers a way to link skills to the curricu-

lum vitae of a specific person. SKOS can be a good answer to thequest for possible ways to describe sharable taxonomies. SKOS(Simple Knowledge Organization System) is a schema specificationto support the use of thesauri, classification schemes, subject head-ing systems and taxonomies within the framework of the SemanticWeb. SKOS is based on the stack RDF/OWL. SKOS can, in more ad-vanced applications, also be used side-by-side with OWL to ex-press and exchange the knowledge about a domain. However,SKOS is not a formal knowledge representation language. It canbe used to model structures for ‘‘ideas’’, ‘‘meanings’’ and ‘‘con-cepts’’ (without any formal semantics) that are often organizedas hierarchies or association networks. Taking this approach, the‘‘concepts’’ of a taxonomy are modeled as individuals in the SKOSdata model, and the informal descriptions about and links betweenthose ‘‘concepts’’, as given by the taxonomy, are always modeled asfacts about those individuals, so never as class or property axioms.The source code in Fig. 3(a) shows how to map emergency situa-tion skills using SKOS scheme.

Moreover, Fig. 3 also indicates how to link FOAF profiles withskills information (modelled in SKOS) through ResumeRDF (name-space cv_rdf).

S through ResumeRDF.

1376 C. De Maio et al. / Knowledge-Based Systems 24 (2011) 1372–1379

4.2.2. Modeling of servicesDeploying the semantics embedded in Web Services is a man-

datory step in the automation of discovery, invocation and compo-sition activities. The semantic annotation is the ‘‘add-on’’ to copewith the actual interoperability limitations and to assure a validsupport to the interpretation of services capabilities. There are sev-eral studies and projects aiming at adding semantics to Web Ser-vice infrastructures. OWL-S [21] provides a qualified OWL-basedsupport for semantic Web Services advertising and process capa-bilities; analogously Web Service Modeling Framework (WSMF)exploits ontologies (i.e., WSMO [22]) in order to solve the interop-erability problems among heterogeneous web services; METEOR-S[23] has moved towards the same direction, adding an interface toUDDI [24] for concept-based querying. This work exploits theOWL-S for describing the Web Services capabilities. OWL-S is anOWL-based upper ontology that allows the providers to deploy acomplete description of the web services capabilities, throughthe following three modules: Service Profile, Service Model andService Grounding. In particular, as highlighted in a previous work[25] we exploit Service Profile parameters (e.g., IOPR, ServiceCate-gory, Service Parameter, etc.) in order to characterize the ServiceProfile according to an emergency domain.

5. Decision Support System

Emergency Management Systems (EMS) have to consider a highamount of data and information from heterogenous sources and, inaddition, information may be vague, missing or not available. TheEmergency Management strategy is based on complex plans andtakes into consideration many factors that may be complementary,contradictory and competitive. It is obvious that EMS requires amodeling tool that can handle all these difficulties and, can atthe same time, can infer a decision. Fuzzy Cognitive Maps (FCMs)is a modeling and simulation methodology for complex systemsand processes. FCM can successfully represent knowledge andexperience through the use of cause effect relationships among dif-ferent factors. FCM is a computational modelling and inference toolsuitable for complex systems, which consist of a great number ofhighly interconnected elements. FCMs are used to develop modelsof aggregate behavior and inferring models that govern the compo-nents and interaction from large amounts, incomplete and uncer-tain data. This work introduces FCM as a suitable mathematicalmodel able to represent emergency plans and, on the other hand,able to support needful simulation and decision making taking intoaccount the evolving of emergency scenarios and the news comingfrom emergency sites.

The following sections present how FCMs support emergencydecision making in our approach. Specifically, we argue how tomodel and how to process the FCM according to our approach. Fur-thermore, taking into account the FCM processing, a resources dis-covery activity is described.

5.1. Fuzzy Cognitive Maps

From the theoretical point of view, Fuzzy Cognitive Maps(FCMs), introduced in [26], is a soft computing technique able tomodel systems’ behaviour by means of a collection of conceptsand causal relationships among them. The FCM developmentmethod is based on fuzzy rules that can be either proposed by hu-man experts or derived by knowledge extraction methods, in sucha way that the accumulated experience and knowledge are inte-grated in the causal relationships between factors/characteristics/components of the modeled process or system [27].

Specifically, a FCM is a signed oriented graph with feedbackconsisting of nodes (i.e., concepts) that illustrate the systems’

behavior. The nodes may represent variables, states, events, trends,inputs and outputs, which are essential to model a system. Eachconcept has a number Ai in the interval [0,1] that represents a va-lue of the system’s variable, for which this concept stands. The con-nection edges between nodes are weighted and directed andindicate the direction and degree of causal relationships. Theweighted edge specifies information on the influence degree ofthe relationship between related concepts. The influence can bepositive (a promoting effect) or negative (an inhibitory effect). Inparticular, there are two possible types of causal relationships be-tween concepts that express the type of influence:

� Positive causality: the weights of the arcs between concept Ci

and concept Cj could be positive (Wij > 0) which means that anincrease in the value of concept Ci leads to the increase in thevalue Aj of concept Cj, and a decrease in the value Ai of conceptCi leads to the decrease in the value of concept Cj.� Negative causality: the weights of the arcs between concept Ci

and concept Cj could be negative (Wij < 0) which means thatan increase in the value Ai of concept Ci leads to the decreasein the value Aj of concept Cj and a decrease in the value Ai ofconcept Ci leads to the increase in the value Aj of concept Cj.

Specifically, the value Ai of concept Ci expresses the degreewhich corresponds to its physical value. At each simulation step,the value Ai of a concept Ci is calculated by computing the influenceof the interconnected concepts Cj’s on the specific concept Ci fol-lowing the calculation rule:

Aðkþ1Þi ¼ f AðkÞi þ

XN

j–i;j¼1

AðkÞj wji

!; ð1Þ

where Aðkþ1Þi is the value of concept Ci at simulation step k + 1. AðkÞi is

the value of concept Cj at simulation step k, wji is the weight of theinterconnection from concept Cj to concept Ci and f is the sigmoidthreshold function:

f ¼ 11þ e�kx

; ð2Þ

where k > 0 is a parameter determining its steepness. In this ap-proach, the value k = 1 has been used. This function is selected sincethe values Ai of the concepts, lie within [0,1].

5.2. FCM modeling

In order to model expert’s knowledge and emergency byexploiting FCM encoding a suitable definition is given. Specifically,the nodes of FCM are defined according to the entities of the emer-gency domain ontology available in the Semantic Middleware (seeSection 4). In fact, each node of the FCM is identified by means of aURI which represents a concept in the ontologies. Specifically, theFCM is structured as a graph which contains three kinds (levels) ofnodes (i.e., the concepts):

� First level: Emergency Features – In this layer there are inputconcepts that represent features of the emergency events (i.e.fire, earthquake, smash up, flood, etc.) in terms of ontologicalentities. These concepts are activated when a specific eventoccurs.� Second level: Emergency Actions – In this layer there are con-

cepts that represent emergency actions (i.e., fire extinction, eva-quation, road traffic accident cleaning, and so on) defined in theontologies. The activation of the emergency actions depends onspecific features of the occurring emergency event (i.e. fire,earthquake, smash up, inundation, etc.).

Fig. 4. An example of three layered Fuzzy Cognitive Map evaluation according to the emergency scenario.

C. De Maio et al. / Knowledge-Based Systems 24 (2011) 1372–1379 1377

� Third level: Types of Resource – This layer is represented by out-put concepts that characterize people’s skills (i.e. medical, fire-fighter, volunteer, policeman, etc.), services capabilities, vehicle(i.e. ambulance, fire engine, airplane etc.) and so on.

Furthermore, among the concepts in the three layered FCM,there are two kinds of relationships:

� Causal, is the relationship between first and second level (e.g.,the relationships in Fig. 4 between ‘‘Fire’’ and ‘‘Fire Estinguish-ing’’) or viceversa (e.g., the relationships in Fig. 4 between‘‘Evacuation’’ and ‘‘Traffic Jam’’). Relationships have to bedefined according to the effective emergency plans adopted inthe specific geographic area. Specifically, according to the emer-gency situation (i.e., fire, earthquake, etc.) specific actions haveto be performed, such as: road traffic accident cleaning, evaqua-tion, rescue, and so on. The relationships and their weights arebased on expertise and on well-defined emergency actions plan.� Requires, is the relationship between second and third level. The

weights of the edges represent involvement degree of specifictypes of resources according to the selected emergency action.The relationships are derived taking into account action model-ing in the ontologies (i.e., skills, capabilities, etc. required toaccomplish the action).

The weight of the edges is represented by values in the interval[0,1], but to improve readability we use linguistic labels, such as:low, middle, high, etc. In particular, according to the above descrip-tion the outputs of the FCM are Types of Resources needful to man-age emergency situation. The following section details the FCMprocessing.

10 For interpretation of colour in Fig. 4, the reader is referred to the web version othis article.

5.3. FCM processing

This section describes how the FCM supports emergency deci-sion making. In particular, from the mathematical point of view,each emergency event has an initial vector Vi (i.e., values of allFCM’s concepts), representing the event at a given time of the

process, and a final vector Vf, representing the last state producedin convergence region of FCM. Values of Vf are taken into account inorder to support resources discovery (i.e., filtering and ranking)useful to manage the emergency situation. So, at the beginningtime of the emergency management process, the Emergency Man-ager (as highlithed in Fig. 4) interacts with the system by specify-ing features of emergency and their activation level (i.e., Vi).Specifically, Emergency Manager selects the right set of emergencyfeatures by varying the slider interface component correspondingto the specific input node of the FCM (i.e., Emergency Features).Subsequently, FCM process starts. The algorithm used to obtainthe final vector Vf is the following:

1. Definition of the initial vector V according to the emergencyevent.

2. Updating values of vector by using Eqs. (1) and (2) (see Section5.1).

3. This new vector is considered as an initial vector in the nextiteration.

4. Steps 2–3 are repeated until jV(t) � V(t � 1)j 6 �. Where � > 0 ischosen small enough and tuned according to the implementa-tion requirements.

Usually more than one emergency event happens concurrently.Just to simplify the description, let us consider a test scenario,splitted according to the emergency notification time as follows:

� At time 0: ‘‘A fire flares up at Rome downtown and it is possiblethat there are explosive materials in the zone’’.� At time 1: ‘‘In the emergency zone there is no explosive materials’’.� At time 2: ‘‘There are injured people of different degrees’’.� At time 3: ‘‘The place of fire is near to the school’’.

Taking into account this scenario, Fig. 4 shows a final snapshotof the FCM processing. The figure emphasizes (in red10) concepts

f

Fig. 5. FCM simulation results according to the emergency scenario.

1378 C. De Maio et al. / Knowledge-Based Systems 24 (2011) 1372–1379

that are activated by the user or by the FCM processing. Conceptson the left (i.e., circles) of the figure represent ontological featuresof the emergency. Concepts in the middle (i.e., triangles) representactions useful to answer the emergency situation. Finally, on theright (i.e., squares) there are concepts that represent types of re-sources required to accomplish the actions.

At each notification time, the Emergency Manager interactswith the system in order to start the FCM process. On the one hand,the process shows the activation of FCM’s nodes. On the otherhand, as highlighted in Fig. 4, the FCM simulation enables to elicita set of resources that matches to the involved area (on the geo-graphical map) and to the required skills.

The Emergency Manager is able to activate/deactivate nodes inorder to tune the simulation according to the incoming notifica-tions. In particular, as shown in Fig. 5 at time 0, the EmergencyManager activates nodes representing Fire, Downtown and Explo-sive Material. Then, the first simulation starts. At the end of the pro-cess there are two effects: activation of Types of Resources useful tosupport the resources discovery; and activation of Emergency Fea-tures not specified in the emergency request, such as: ExplosionRisk, and so on.

The activation of further Emergency Features is a value added interms of prediction of consequences. Furthermore, this added re-sults are taken into account by the experts in order to tune the sim-ulation at the next step, if necessary. In fact, at time 1, taking intoaccount the new incoming notification, the Emergency Managerdeactivates the concept of Explosive Materials influencing the nextsimulation process. Again, simulation at time 2 shown in Fig. 5takes into account activation of Injured concept in order to startthe next evaluation. Further simulation may be performed by thesystem according to the incoming notifications.

As highlighted above, this work adopts the FCM as a suitablemathematical model enabling an expert to tune the simulation justin time, in order to make the better choice before the involved areasituation becomes very chaotic. The following section provides de-tails about resources discovery based on FCM results.

5.4. FCM & emergency resources discovery

At the end of each simulation, the FCM carries out Vf that containsthe final activation levels of Types of Resources, namely skills and

capabilities useful to manage the event occurred. So, in order to ful-fill the requirements, a discovery process must be performed in or-der to find a suitable set of resources among the available ones.

As highlighted in Section 4, the Semantic Middleware gathersontologies and individuals (i.e., people, services, etc.) definedaccording to our vocabularies. Since, FCM’s nodes are strictly re-lated to the ontology concepts, resources discovery activity (i.e., fil-tering and ranking) relies on SPARQL-based queries and ontologybased reasoning. So, DSS by taking into account FCM’s output valuesis able to ask the Semantic Middleware in order to retrieve re-sources according to the elicited emergency requirements (i.e., Vol-unteer, Physician, etc.) and emergency constraints (i.e., geographiclocation of emergency and resources, availability of resoruces, etc.).

Specifically, DSS defines OWL classes defined according to theelicited requirements, such as:

<owl:Class rdf: about=‘‘AlertingPeople’’><owl:equivalentClass>

<owl:Class ><owl:intersectionOf rdf:parseType=‘‘Collection’’>

<owl:Class rdf:about=’’cv_rdfs#CV’’/><owl:Restriction>

<owl:onProperty rdf:resource=‘‘cv_rdfs#hasSkill’’/><owl:someValuesFrom>

<owl:Class ><owl:unionOf rdf:parseType=‘‘Collection’’>

<skos:Concept rdf:abaut=‘‘Physician’’/><skos:Concept rdf:abaut=‘‘Volunteer’’/>

</owl:unionOf></owl:Class >

</owl:someValuesFrom></owl:Restriction>

<owl:intersectionOf></owl:Class >

</owl:equivalentClass></owl:Class>. . .

Finally, DSS asks the Semantic Middleware for retrieving indi-

viduals that match the constraints by means of SPARQL queries,such as:

C. De Maio et al. / Knowledge-Based Systems 24 (2011) 1372–1379 1379

PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>PREFIX cv_rdfs: <http://kaste.lv/�captsolo/semweb/resume/

cv.rdfs#>PREFIX skills: <http://www.ourSchema.com#>SELECT ?xWHERE {

?z rdf:type skills:AlertingPeople.?y cv_rdfs:aboutPerson ?x.

}

At the end, the retrieved resources are ranked and filteredaccording to their availability and their proximity to the emer-gency area. In particular, k-nearest-neighbor queries on the re-trieved resources are used to filter and to rank the results.

6. Conclusions and future works

The framework has been defined by using semantic technolo-gies and soft computing techniques. In particular, the frameworkdeals with knowledge representation, harmonization, sharing anddiscovery by means of strong usage of Semantic Web technologiesallowing large-scale interoperability across tools. Furthermore, thiswork introduces the FCM as a suitable mathematical model able tomodel causal inference embedded in emergency plans and to sup-port the resources discovery activity. Specifically, the work exploitsFCM in order to support just in time simulation and to provide con-crete assistance to Emergency Manager during a critical situation.The emergency resources discovery method has been defined tak-ing into account the results that come out from the FCM process-ing. In the future, the research will deal with: automaticacquisition of emergency notification from unstructured and mul-timedia contents (e.g. SMS, images, audio clips, etc.); and machinelearning algorithms in order to change the FCM, taking advantageof the previous system usage.

Acknowledgments

The results of the present work have been achieved thanks tothe know-how that has been acquired during the research activi-ties of two co-funded projects, namely SIEGE (national researchproject co-funded by Italian Government – MISE) and ABSC (na-tional research project co-funded by Italian Government – MIUR).

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