Abstract To provide context-based personalized servicesutilizing smart appliances in a smart home environment, wepropose a framework for PersonAlized Service disCoveryUsing FuZZY-based CBR and Context Ontology (PAS-CUZZY). Basically, the PASCUZZY framework is imple-mented on case-based context ontology. To generate andmanage the case instances on the case-based context ontol-ogy, we adopt the fuzzy set theory to transpose numerical-type context data sensed from the surrounding environment.The context is transposed to linguistic-type context instanceson the context ontology. In addition, to formalize and managethe context and services as multi-attributed data, the contextontology was developed reflecting the structure of cases bor-rowed from case-based reasoning. Furthermore, we proposeadaptation methods to adjust the generic fuzzy membershipfunctions depending on the inhabitants’ context. It is per-formed by modifying the values of the membership numberand/or modifying the numbers of the linguistic terms thatare based on the inhabitants’ context to affect the member-ship numbers. The adapted membership functions return thepersonalized degree of memberships depending on the spe-
Communicated by A. Castiglione.
M. Sohn · S. JeongDepartment of Industrial Engineering, Sungkyunkwan University,300, Chunchun-dong, Jangan-gu, Suwon,Gyeonggi-do 440-746, Koreae-mail: [email protected]
H. J. Lee (B)Yonsei Institute of Convergence Technology, School of IntegratedTechnology, Yonsei University, 162-1, Songdo-dong,Yeonsu-gu, Inchon, Koreae-mail: [email protected]
cialized context of a specific fuzzy variable. Inevitably, thenumber of cases on the case-based context ontology will beincreased from time to time. We apply Ward’s method notonly to reduce the search effort via a hierarchical clusteringon the case-based context ontology but also to find the mostsimilar service as a solution to the new context. To verify thesuperiority of the PASCUZZY framework, we perform twokinds of evaluations. First, we evaluate the effectiveness ofthe adaptation of the fuzzy membership functions. Second,we verify the effectiveness of the application of a cluster-ing method to the case instances of the case-based contextontology to identify the most similar service. Results of theexperiment verified the effectiveness and superiority of thePASCUZZY framework.
Keywords Case-based context ontology · Personalizedservice · Fuzzy membership function adaptation ·Fuzzy set theory
1 Introduction
It is beyond question that context-based personalized ser-vices are spreading in a variety of fields including tourism,mobile commerce, and smart living services in our dailylives. The reason why these kinds of services are interest-ing and popular in our lives is that ever-changing data havebeen easily detected and collected as one of the essential ele-ments of service personalization to provide context-basedpersonalized services. In this research, the ever-changingdata depending on the surrounding environment is defined ascontext. Especially, the context is classified into two types—environmental contexts and personal profiles. The former iscollected by various sensors or devices with different scalesand numerical formats, and the latter includes inhabitants’
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personalized information such as their profiles, preferences,and needs. It is collected with a flat-type format that is com-posed of a set of numerical values. In order to provide theservices using the context in a smart home environment,the context should be formalized by an appropriate method.Although a great deal of studies have been conducted andvarious representation models have been presented to modelthe context precisely (Strang and Linnhoff-Popien 2004), theontology-based context model has been popularized as oneof the most powerful methods to provide a clear definition ofthe context-related concepts and their semantic relationships(Strang and Linnhoff-Popien 2004; Reichle et al. 2008). Ingeneral, the ontology-based context model is implemented byhierarchical structures among the shared and consensus con-cepts (classes) in the discourse domain. The classes can havea number of instances with linguistic terms and/or numer-ical values. In this light, to develop the context ontology,the context data with flat-type numerical values should betransposed into linguistic-type instances of the ontology like‘high,’ ‘medium,’ or ‘low.’ Much research has proved thatthe fuzzy set theory is possibly the best method to transposenumerical terms into linguistic terms (García-Crespo et al.2012; Cao and Li 2007; Bashon et al. 2013).
To do so, we adopt the trapezoidal-type fuzzy membershipfunctions for fuzzy variables for the transformation of con-text data. Using the fuzzy membership functions, the fuzzyvariables as context of the numerical-type are mapped intolinguistic terms. In addition, the degree of membership isderived depending on the probability of the fuzzy variablesto be included in the linguistic terms. If it is not specific, thenit is general that the context datum of a specific numericalvalue has been usually mapped to the same linguistic termusing the fuzzy membership functions which also return thesame degree of membership. It is true that these kinds ofthe fuzzy membership functions have a limited ability toprovide differentiated services to inhabitants who want toreceive personalized services according to their preferencesor needs. As a solution to the problem, we propose two-type adaptation methods of the fuzzy membership functions.The adaptation is conducted by modifying the values of themembership number and/or modifying a number of linguisticterms to change the shape of the fuzzy membership functions.Through the adaptation process, it is possible to extract thepersonalized degree of memberships of a specific fuzzy vari-able, and it contributes to providing personalized services.
To find the optimized personalized service, it is neces-sary to analyze the meaning of the collected context data.To do this, rule-based reasoning is customarily applied ina manner in which, for example, if the indoor temperaturegoes higher than 25 ◦C, then inhabitant ‘Sohn’ turns on theair-conditioner. However, we applied case-based reasoning(CBR) to effectively manage the multi-attributed contextbecause the surrounding context is comprised of a set of mul-
tiple data including the environmental context and personalprofile. To recommend context-based personalized servicesusing CBR, it is necessary to effectively control the pairedrelationships between the contexts and the provided serviceson the case-based context ontology. So, we added a caseconcept as a new class with two-type instances that index aninstance of the context and of the provided service to the case-based context ontology. The case instances of the case-basedcontext ontology not only include the transposed contextdata as the instances, but also indicate the provided serviceunder the specific context. In the case-based context ontol-ogy, the pairs of the contexts and the services are managed ascases to provide context-based personalized services. How-ever, unfortunately, it often generates a tremendous numberof cases to be managed on the case-based context ontology. Itcan cause the increasing burden of similarity calculations tofind the most similar case from the case-based context ontol-ogy. To reduce the burden, a clustering method is appliedto search solutions to be served to inhabitants using thecase-based context ontology. To perform the clustering, thenew context is compared with multi-attributed cases. In thisresearch, Ward’s method is adopted as one of the agglomera-tive hierarchical clustering methods for the case clustering onthe case-based context ontology because it is appropriate toclassify cases with multi-attributed data (Byrne et al. 2009).By applying the clustering, the target space for the similaritycalculation to find the most similar case is restricted to onecluster that contains cases related to the new context. Finally,the most similar case as the most preferred service among thecases is identified and served to the inhabitants.
Thus, this paper aims to propose a framework to pro-vide personalized services on case-based context ontologyusing fuzzy membership functions. It is called PersonAlizedService disCovery Using FuZZY-based CBR and ContextOntology (PASCUZZY) framework. This paper is differ-entiated from the preliminary version which has been pre-sented in the conference paper (Sohn et al. 2013) asfollows:
• While we previously recommended a personalized ser-vice using a heuristic method, in this study, we proposethe case-based context ontology using the fuzzy set the-ory to provide personalized services depending on theinhabitants’ surrounding contexts. Especially, the fuzzy-based personalized service is performed differently on aninhabitant-by-inhabitant basis because it is possible forthem to feel and react differently in the same context.
• The collected context from inhabitants’ surroundingenvironment, which includes the environmental contextand the personal profile, is transposed into linguistic-typecontext, because the collected context has a flat-type con-text, while the instances of the case-based context ontol-ogy usually occur in a linguistic-type context. Using a
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fuzzy membership function, the collected flat-type con-text is transposed into the context that is appropriated tothe construction of the case-based context ontology.
• The proposed adaptation method of the fuzzy member-ship function contributes to providing the personalizedservice depending on the generation of different degreeof membership. The method has two types to modify theshape of the fuzzy membership function: one is modify-ing the values of the membership number, and the otheris modifying the numbers of the linguistic terms.
• While, in the previous study, the constructed contextontology was applied to a washing machine, in thisstudy we propose the case-based context ontology to pro-vide preferred service to support all smart appliances ina smart home environment. Furthermore, the proposedontology is structured on the CBR.
• To prove the usefulness and superiority of the case-basedcontext ontology using the fuzzy set theory to providepersonalized services, we implement the PASCUZZYframework using Protégé for implementation of contextontology, R to apply a clustering method.
• To verify the superiority of the PASCUZZY framework,we perform two kinds of evaluations. First, we evaluatethe effectiveness of the adaptation for the fuzzy member-ship functions. Second, we verify the effectiveness of theapplication of a clustering method to the case instances ofthe case-based context ontology clustering in identifyingthe most similar service. Results of the experimentationverified the efficiency and superiority of the PASCUZZYframework.
This paper is organized as follows. In Sect. 2, we describerelated works on personalized services for smart appliances,fuzzy logics, their adaptations, and clustering algorithms.Section 3 proposes the case-based context ontology in thePASCUZZY framework. Section 4 describes the develop-ment of the adaptation methods of the fuzzy membershipfunctions depending on the inhabitants’ context and the appli-cation of clustering to effective control of the case-basedcontext ontology to provide personalized services. Section 5implements the PASCUZZY framework and conducts exper-iments to prove the efficiency and effectiveness. Finally,Sect. 6 puts forth the conclusions.
2 Related works
In this study, to provide a personalized recommendation ser-vice, we applied case-based reasoning to case-based contextontology to effectively manage instances of the ontology andfuzzy logic-based similarity calculation to support personal-ized services depending on the inhabitants’ related context.In addition, the clustering method is adopted to help reduce
the search effort for the similarity calculation. Then we pro-ceed to review some related subjects as follows.
2.1 Personalized services for smart home appliances
Smart home environments are gaining popularity as smarthome intelligence because their computer systems can mon-itor many aspects of our daily lives as contexts and control,recommend, and suggest the appropriated services to inhab-itants depending on their lifestyle patterns (Bregman 2010;Lee et al. 2013; Makonin et al. 2013; Martin et al. 2009). Per-sonalized service can be created, adjusted, or expanded usingpreviously provided services depending on the users’ require-ments, preferences, interests, or tasks in a given context (Ballet al. 2006; Dabrowski et al. 2013; Göker and Myrhaug 2002;Lee et al. 2013; Ogiela and Ogiela 2012; Seo et al. 2013). Ina variety of areas, the personalization services are applied torecommend Hard/Soft goods on on/off-line, health service,e-education, etc. (Das 2013). For instance, there are manystudies focusing on the personalization of services to satisfythe profiles and preferences of users with disabilities (Gaedkeet al. 2009). Tzouveli et al. (2008) focuses on ontology-basedpersonalization learning system for the disabled. Kadoucheet al. (2009) developed Semantic Matching Framework(SMF) to deliver assisted services according to disabled per-sons’ capabilities and preferences in a smart home. Further-more, context-awareness is applied to the users using resi-dents’ profiles, behaviors, environments, and so on, to meetthe goals of comfort and efficiency by providing personal-ized services (Bainbridge 2004; Marsá-Maestre et al. 2008).In this research, the proposed system PASCUZZY frame-work is focusing on personalization of services for inhabi-tants like the old, the disabled as well as general people insmart home environment using fuzzy-logic to support intelli-gence and case-based reasoning to effective manage contextson context ontology. Especially, we are focusing on the dif-ferent level of services depending on contextual factors likethe environment, the individual lives or personal factors likeage, gender and social background (Martin et al. 2009). Evenif there are many researches on issuing to focus the appro-priateness of smart intervention using intelligent techniques,it is still problems to serve personalization with a variabledegree of automation (Martin et al. 2009).
2.2 Fuzzy logic and its adaptation
To recommend the personalized services, ontology is appliedto the retrieval and adaptation of case instances with case-based context ontology (Park et al. 2007; Lee et al. 2010). Inthis research, fuzzy logic is applied to support the degreesof automated smart home services as one of intelligenttechniques because fuzzy logic is often used to help makehumanlike decisions. There are fuzzy membership func-
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tions like Gaussian, Triangular, Trapezoidal, S-function, andL-function (Vijaya et al. 2010), which are applied to recom-mendation systems to determine the recommendation typedepending on age and income (García-Crespo et al. 2012),and to improve the correctness of the recommendation usinga nested type of the triangular function (Cao and Li 2007),and so on. Triangular and Trapezoidal membership functionshave been used extensively due to their computational effi-ciency (Jang et al. 1997). In this study, the trapezoidal mem-bership function is applied to calculate the degree of mem-bership for the fuzzy variables. Furthermore, we performthe adaptation to the fuzzy membership function using theinhabitants’ preferences that are reflected in the membershipnumbers for the fuzzy membership functions. In real-worldapplications, because it may not be acceptable assumptionsof the given fuzzy membership functions or the fixed shapeof the fuzzy membership function (Hudson et al. 1995), theadaptation process on the generic fuzzy membership func-tions is reasonable. There are many attempts to adapt thefuzzy membership function according to some research. Forinstance, Awad and Fayek (2012) proposed the adaptationmethodology using input–output contractor default predic-tion cases to adjust the originally developed generic fuzzymembership function. The proposed adaptation mechanismby Navale and Nelson (2010) modifies scaling and mappingfactors to reshape the fuzzy membership function to improvecontroller performance. Pedrycz et al. (1997) tried to adaptfuzzy membership functions to modify the model in responseto any context changes. In this research, we also focus onthe adaptation of the fuzzy membership function as someresearches have done. The difference of such studies is underconsideration of individuals’ preferences or needs to providea personalized fuzzy membership function.
2.3 Clustering methods for CBR
In this research, we adopted context to provide personalizedservices to inhabitants in smart home environments. The con-text is organized as case instances on the case-based contextontology. However, it is not easy to find an appropriated solu-tion from the case-base because it can be constructed by hugeamounts of cases. Accordingly, in CBR-related works, clus-tering is usually adopted to reduce the search space with anumber of cases to be retrieved (Fornells et al. 2006; Yueet al. 2013). There are k-means (Choy et al. 2009), self-organizing map (Sahoo et al. 2012), fuzzy C-means (Sheu2007; Chang et al. 2008), density-based spatial (Yang andWu 2001), and hierarchical clustering method (Wang andHsu 2004).
Case-based context ontology has the case instances of themulti-attributed data type. The case instances in the PAS-CUZZY framework have multi-dimensional data that arecomposed of multiple features and their values. It has been
proven that the hierarchical clustering is one of the best meth-ods of clustering multi-dimensional data by Olson (1995).Furthermore, it does not have to be prespecified for the num-ber of clusters and some parameters that affect the shapeor size of a cluster. In particular, we adopt Ward’s hierar-chical clustering method in searching for a solution on caseinstances because the case instances are less likely to includesome outliers and we want equally sized clusters (Mooi andSarstedt 2011).
In Sect. 3, we introduce case-based context ontology witha case concept on case-based reasoning using contextualfactors like profiles, behaviors, and environments.
3 Case-based context ontology in PASCUZZYframework
3.1 Description of case-based context ontology
In PASCUZZY framework, the case-based context ontol-ogy is organized with a multiple-inheritance hierarchy of theclasses that include context and services to provide context-based personalized services. The context is comprised ofenvironmental context and inhabitants’ profile to support per-sonalized automated service in a smart home environment.The required services depending on the context should behighly automated such as controlling of the thermostat ofan air conditioner, monitoring of health conditions like dis-eases, injuries, etc. In the proposed case-based context ontol-ogy, there is additionally a class type with a case structureof CBR to link instances to specify a specific context andservice. Thereby, we describe definitions of the componentsthat are used for the case-based context ontology as follows.
Definition 1 (Context Ontology Ontcxt ) Context ontologyOntcxt is simply represented as
Ontcxt = 〈C Lcxt , P Rcxt , I nscxt 〉, (1)
where C Lcxt , P Rcxt , and I nscxt are sets of classes, proper-ties, and instances, respectively.
Definition 2 (Class C Lcxt ) As a set of class of Ontcxt ,CL cxt is classified with sets of a case class CS, context classCXT, and service classSV . It is represented as
C Lcxt = 〈C S, C XT, SV 〉, (2)
C XT and SV are constructed by hyper–hypo relation-ships using commonly shared vocabularies among them. Inaddition, C S is applied to effectively maintain the contextinstances and the service instances as pairs. The details ofC S are described in Definition 3.
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Table 1 Examples of classesand subclasses of context CXTincluding EV, and PF andservice SV
Class Description of classes Illustration of subclasses
P F Inhabitants’ profiles, preferences,characteristics, and so on
p ffamitalRole, p fname, pfsex, pfage, pfoccupation, pfdisabled,pfdisablilityType, etc
EV Sensed environmental context in asmart home which needs servingpersonalized services
Inhabitants’ locations, temperatures, humidity,illumination, etc. which are differently generatedaccording to the inhabitant state and applied to make adecision to determine to be served a type of thepersonalized service
SV Provided personalized smart homeservices with processes that areinstalled by smart homeappliances
Turned on/off, timer, thermostat as services
Air conditioner, washing machine, TV, lighting, oven, etc.as smart home appliances
Definition 3 (Case C S) Class case CS as a subclass of C Lcxt
has two types of subclasses CSP as a problem and C SS as asolution like a case of CBR to maintain the set of instances.It is represented as
C S � 〈C S P , C SS〉, (3)
C S P and C SS do not have any subclasses. The C S hasrelationships with instances of context csp
i and instances ofservices css
i (1 ≤ i ≤ n number of case instances). csi isrepresented as a union of cs p
i and cs si .
Definition 4 (Context C XT ) Context CXT has two types ofsubclasses, i.e., environmental (EV) and inhabitants’ profiles(P F). In general, EV is monitored and collected by sensorsor mobile devices in a smart living environment, and PF isstored on the home server computer. It is represented as
C XT � 〈EV, P F〉, (4)
EV and PF are hierarchically represented by hyper–hyporelationships between concepts (classes) related to EV andPF on the case-based context ontology. CXT, EV, and PFhave sets of instances cxti j,evik and p fil(i, j, k, and l ≥ 1)
as an element of each set. The i is index of a case instance.
Definition 5 (Service SV ) SV is the service class of thecase-based context ontology. Just like EV and PF, it is hier-archically represented by hyper–hypo relationships betweenshared concepts (classes) that are related to the SV on thecase-based context ontology. SV has a set of instancessvo(o ≥ 1) as an element of the set.
Table 1 illustrates examples of classes and subclasses ofEV and PF as context and SV as services of the case-basedcontext ontology.
Definition 6 (Property P Rcxt ) Property P Rcxt representsa set of the binary-directed relationships that are comprised
of relations between two classes or two properties on thecase-based context ontology. It is represented as
P Rcxt = 〈P RcxtC , P Rcxt
p 〉, (5)
where P RcxtC and P Rcxt
p are sets of class and property rela-tions, respectively.
Definition 7 (Instance Inscxt ) Instance I nscxt representssets of the actual values as instances of CS, CXT, andSV. The instances of the classes are denoted by csi ={cs p
i ∪ cssi } ∈ cs, cxti j = {evik ∪ p fil} ∈ cxt, and
svio ∈ sv(i, j, k, l and o ≥ 1), where cs, cxt, and sv aresets of instances of C S, C XT, and SV . In Instance layer,csp
i and cxti j have a binary-directed relationship pri j and cssi
has a binary-directed relationship between prio with svio. Itis represented as follows:
cspi = {
(pri j , cxti j )|pri j ∈ P Rcxt , cxti j ∈ cxt}
cssi = {
(prio, svio)|prio ∈ P Rcxt , svio ∈ sv}. (6)
Using the Eq. (6), it is possible to maintain the cases on thecase-based context ontology.
In Sect. 3.2, according to the definitions, we illustrate acase instance of case-based context ontology.
3.2 An illustraion of a case instance of case-based contextontology
A case instance csi is comprised of a set of a problem cspi and
a service cs si . For instance, if the new context is monitored
as ‘September-03-2013, AM 10:00, 37.588193, 126.993606,25 ◦C, and Sohn’ and the provided service is ‘air-conditioner,37.59800370, 126.9652346, and turn-off service,’ then thecase instance csi is depicted in Fig. 1.
The case instance csi of Fig. 1 is fuzzified in Fig. 5 to pro-vide the personalized services. The applied fuzzy set theoryis described in Sect. 4.
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ith case Indexes (hierarchical classes) Values (instances)
Fig. 1 An illustrative case instance of the case-based context ontology
Fig. 2 A partial example of the case-based context ontology
4 Fuzzy-applied case-based context ontology
4.1 Fuzzified case instances using a fuzzy set theory
It is necessary for some of numerical context data to fuzzifyinto the linguistic values with semantics as the instancesof case-based context ontology because it is possible forsome context data to be exactly mapped into the values ofthe instances. For instance, the ‘indoor Temperature’ (e.g.,25 ◦C) can be fuzzified into an instance ‘Warm’ depend-ing on the inhabitant’s feeling with any semantics, butthe data ‘Sohn’ does not have to be fuzzified because ofthe unambiguity of the concept. Furthermore, the fuzzi-fied linguistic values from context data can be varied frominhabitant to inhabitant because each of them can have adifferent feeling or reactions that can be transposed to dis-criminant values with semantics. For instance, an inhabi-tant feels ‘Warm’ with the degree of membership ‘1.0,’ butanother inhabitant feels ‘Hot’ with the degree of member-ship ‘1.0’ even though they have the same temperature. Toreflect the individual difference, the adaptation to the fuzzymembership functions is required. Figure 2 shows a partialexample of the fuzzified linguistic values from some con-text as instances of the proposed case-based context ontol-ogy.
For the fuzzification, we adopt the trapezoidal member-ship as a fuzzy membership function because it has a highcomputational performance (Xie and Church 1998). Prob-lem context cxti j can be used for a fuzzy variable. To expressthe level of the membership of cxti j ,we define μA(cxti j )
as a fuzzy membership function ((a, b, c, . . .) ∈ A). Thecxti j belongs to any personalized fuzzy set. As an instance,a membership function for cxti j is represented as in Eq. (7).
μa(cxti j ) =
⎧⎪⎪⎪⎪⎪⎨
⎪⎪⎪⎪⎪⎩
0, cxti j ≤ αa1
γa−αa(cxti j − αa), αa < cxti j ≤ γa
1, γa < cxti j ≤ δa
− 1βa−δa
(cxti j − βa), δa < cxti j ≤ βa
0, βa < cxti j
(7)
For instance, cxti j is ‘indoorTemp.’ a is an element of a setAwhich has linguistic values like A = {a|a = cold, cool,warm, hot, . . .} and αa, βa, γa, and δa represent the mem-bership numbers for cxti j . The fuzzy membership functionof a linguistic value a with max(μa(cxti j )) is adopted as thefuzzification result for the fuzzy variable cxti j .
4.2 Adaptation of the fuzzy membership function
Even though we adopt the generic fuzzy membership func-tions for the fuzzification of the flat-type context, it only
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Fig. 3 Illustrative examples with two-type modifying adaptation methods for indoor temperature as a fuzzy variable
has a limited ability to provide personalized services toeach inhabitant because some degree of fuzzy membershipscan be varied depending on inhabitants’ personal prefer-ences. Thereby, we propose two-type adaptation methodsof the fuzzy membership function, which are varied by themodifying values of the membership number and modi-fying the numbers of the linguistic terms that are basedon the inhabitants’ context like EV and PF and affectedby the membership numbers. In Fig. 3 are illustrationsof two-type adaptation methods of the fuzzy membershipfunctions.
• Modifying the values of the membership number (Type1 Adaptation)
A fuzzy membership function of trapezoidal can be describedby three parameters, i.e., support, boundary, and core thatdetermine the shape of the fuzzy membership functions(Makrehchi et al. 2003). In addition, these parameters arededuced by the values of the fuzzy membership numbers.The results of the fuzzification depend on the shape of thefuzzy membership function. In the Fig. 3, the pane of thelower left shows the effect of modifying the values of themembership numbers. For instance, when the indoor tem-
perature is 17 ◦C, the adapted fuzzy membership functionreturns ‘warm’ as a fuzzified result, while non-adapted fuzzymembership function returns ‘cold.’
• Modifying the number of the linguistic terms (Type 2Adaptation)
Another adaptive mechanism to adjust the sensitivity of theinhabitants to the context data is to modify the number ofthe elements of the fuzzy set. The number of the elementsis related to the number of the fuzzy membership func-tions of the fuzzy variable. In Fig. 3, the pane on the lowerright shows the effect of modifying the numbers of the lin-guistic terms as the elements. For instance, if the indoortemperature is 20 ◦C, then the fuzzification result is ‘Cool’using the adapted function, while it is previously fuzzifiedto ‘Warm.’ Modifying the number of the linguistic termsof the function also affects the output of the fuzzy vari-ables.
The set of the fuzzy membership numbers for the fuzzyvariables can be derived based on the context and thehistory of the provided service if it is for the inhabi-tants. Otherwise, it is collected through direct interactionslike questionnaires. Through the adaptation processes, var-
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{ {
Fig. 4 Adaptation algorithm of the membership function
ith case Indexes (hierarchical classes) Values (instances)
Fig. 5 An illustration of a fuzzified case instance I ns f csi
ious shapes of the fuzzy membership functions can bederived to support the personalized services. Figure 4 sum-marizes the adaptation algorithm of the membership vari-ables.
Using the generated personalized fuzzy membership func-tion through adaptation process, the personalized degree ofmembership of the linguistic terms has been augmented asan additive element of the case instance csi . So, the csi isfuzzified as a type of fuzzified case instance that is describedin Definition 8.
Definition 8 (Fuzzified Case Instance Ins f csi ) I ns f csi is afuzzified type of the case instance csi that is augmented bythe domp
i j to each cxti j . The dom pi j indicates the degree of
membership for cxt pi j . I ns f csi is represented as f cs p
i , cssi ,
where fcspi = { (pri j , (cxti j , dom p
i j )|pri j ∈ P Rcxt , cxti j ∈cxt, 0.0 ≤ dom p
i j ≤ 1.0}.
In this research, different degrees of a membership forthe same fuzzy variable are calculated by the inhabitants’context. In the PASCUZZY framework, domp
i j for each cxti j
is used to specify the likelihood of occurrence of cxti j . So, wecan modify the definition of a case instance csi (Definition 7)into the fuzzified case instance I ns f csi (Definition 8). Thefuzzified case instance using an example of Fig. 1 is describedin Fig. 5.
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Whenever the context-based personalized services areoffered, if new case instances are generated and added tothe case-based context ontology, then the space of the caseinstances of the case-based context ontology will be expo-nentially expanded.
4.3 Clustering of case instances for similarity calculation
In the PASCUZZY framework, the case-based context ontol-ogy acts as a huge case base. Therefore, the burdens ofthe similarity calculation will be increased to identify themost similar case instance. As a result, even if the guar-antee of feasibility to find a solution is increased, expo-nentially expanded search space to be inferred makes ittoo difficult to find the best solutions. To make up forthe fault, we tried to filter the search space using clus-tering on the fuzzified case instances. It is helpful toreduce the search effort to serve the personalized context-based services. Furthermore, we can expect to promotethe accuracy of the clustering using the fuzzified caseinstances that are augmented by the degree of the member-ship.
To improve the efficiency of the retrieval of the most simi-lar case, hierarchical clustering is applied to case-based con-text ontology because of the multi-attributed data type of thecase. Furthermore, it is very difficult to prescribe the numberof the clusters and some parameters that affect the shape andsize of a cluster due to the perpetually varying nature of thenumber of the case instances. Fortunately, hierarchical clus-tering requires not only the number of the clusters but alsothe parameters. In this research, we adopt Ward’s method asa hierarchical clustering, which is applied to cluster the caseinstances. To perform Ward’s hierarchical clustering usingthe case instances csi (i ≥ 1) and a new problem (np) witha new context that needs a service, we construct the matrixof (n + 1) − by − m that has (n + 1) rows, one for csi
and np, and m columns, one for each context datum. The(i, j) element of the matrix represents the degree of mem-bership dom p
i j for cxti j . Through Ward’s hierarchical clus-tering, the identified cluster (C Lo) contains case instancescsi ′ (i
′ ≤ i) and np. To find the most similar case among thecase instances, we calculate the similarity between csi ′ andnp.
Even if there are many similarity calculation methods likeEuclidian distance, cosine similarity, and so on, the PAS-CUZZY framework adopts Euclidian distance because it isappropriate for high dimensional data, the most intuitiveform of human perception of proximity, and it is easy tocalculate and conceptually straightforward (Qian et al. 2004;Lin et al. 2006; Sander et al. 2010). Through all of the process,the selected case instances that have the lowest value for thesimilarity calculation have been revised to find the personal-ized service.
5 Implementation and performance evaluation
5.1 Implementation of the PSACUZZY framework usingcase-based context ontology
The PASCUZZY framework is comprised of Context Iden-tification Module, Fuzzy-based Case Instances GenerationModule, Case instance Clustering Module, and SimilarityCalculation Module as in Fig. 6.
The Context Identification Module collects environmen-tal context and personal profile using embedded sensors andmobile devices in a smart home environment. To providecontext-based personalized services to the inhabitants, weassumed that there are necessary sensors to collect the con-text. The Fuzzy-based Case Instances Generation Moduletransposes the context collected by the Context Identifica-tion Module into case instances using the fuzzy set theory.The Case Instance Clustering Module performs Ward clus-tering as a hierarchical clustering of the case instances on thecase-based context ontology. To represent the data matrix forclustering, the PASCUZZY framework uses the personalizeddegree of membership for the fuzzy variables that are the by-product of the fuzzy-based case instances generation processto represent the data matrix for clustering. It is possible to skipthe normalization process of clustered data because of the useof the matrix. The Similarity Calculation Module focuses onthe calculation of similarity between case instances and thenew problem using a new context. Similarity is calculatedusing an m by n matrix data matrix that has m one for eachinstance, and n columns, one for each context datum. In thispaper, we adopt the Euclidian Distance measure to calcu-late the similarity. Through the modules, the PASCUZZYframework generates context-based personalized service thatis added to the case-based context ontology as case instances.
Fig. 6 Overall architecture of PASCUZZY framework
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Fig. 7 Case structure on case-based context ontology
The case-based context ontology is implemented by theontology language OWL using Protégé 4.1 as the widelyused OWL editor (Darmoul et al. 2011). As shown inFig. 7, C S, P F, EV and SV are implemented as classes ofcase-based context ontology. The classes are specialized bydepicted subclasses in Table 1 and constructed by the set ofcontext and the provided service that are mapped into a prob-lem and a solution of a case respectively. The case instanceof case-based context ontology is illustrated in Fig. 7.
5.2 Experimental design and discussion
The experiments are conducted in the environment of a smartliving room equipped with thermostats and automatic humid-ity controllers. The inhabitants have handheld devices likesmart phones with GPS installed to calculate inhabitants’location. To perform the experiment, we generated 5,000 caseinstances that are stored in the case-based context ontology asinstances. The context ontology is constructed by the ontol-ogy editor Protégé 4.1. The experimental subjects are ran-dom groups of 10 women and men in their 20s. The appliedinference engine is HermiT 1.3.8. For the clustering of thecase instances, R as the data mining tool is adopted. In thisresearch, we focus on the verification of effectiveness of theadaptation of the membership function for the personalizedservice and clustering of case instances on case-based contextontology to effectively find the most similar case as a solu-tion for the context. For the experiment, as was determinedin Sect. 4.2, there are two-type adaptations as follows: Type1 modifies the values of the membership values, and Type 2modifies the numbers of linguistic terms.
5.2.1 Comparing effectiveness of Type 1 and Type 2 of theadaptation with the generic fuzzy membershipfunction without adaptation
In this paper, two-type adaptations have been conductedusing the generic fuzzy membership function. To verify theeffectiveness of the adaptation methods, the numerical-typecontext data are mapped into the linguistic terms on caseinstances using the generic fuzzy membership function byType 1 and Type 2 adaptation mechanisms. After that, two-type adaptations are conducted for the two-subject groups togenerate a personalized fuzzy membership function depend-ing on each subject group’s context as a new problem. It isnecessary to calculate the similarity to find the most similarcase from the case instances on case-based context ontology.The case instances are addressed in a cluster that includesthe context as a new problem (np). If the selected service asan instance of the most similar case is satisfied with the sub-ject, then the subject returns value ‘1,’ otherwise ‘0.’ For theexperiment, 10 sets of the case instances from 100 to 1,000case instances are generated. For each case set, experimentsare conducted 30 times, and the average satisfaction value ofeach group is used as the satisfaction ratio for the providedsolution. The adaptations of the two-subject groups are com-pared for their effectiveness in Figs. 8 and 9. The experimentwith the two-subject groups shows that the degree of satisfac-tion using personalized fuzzy membership function throughadaptation methods for the provided service is higher thanthat of a non-adapted fuzzy membership function.
To compare the effectiveness of Type 1 adaptation withthat of Type 2, the satisfaction ratios extracted from each
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Fig. 8 Comparatives ofadaptation applied Type 1, Type2, and non-applied adaptation(for subject group 1)
Fig. 9 Comparison ofadaptation applied Type 1, Type2, and non-applied adaptation(for Subject Group 2)
group are compared in Figs. 10 and 11. For Subject Group1, the effectiveness of Type 1 is higher than that of type2. On the other hand, for Subject Group 2, the effec-tiveness of Type 2 is higher than the other. The resultproves that the effectiveness of the adaptation dependingon the types can vary according to the inhabitants’ con-text.
5.2.2 Effectiveness of clustering of case instances
To show the effectiveness of the clustering of case instanceson case-based context ontology, the processing time usinga hierarchical clustering to find the most similar service iscompared with that of non-applied clustering. The first exper-iment is conducted with instance sets comprised of a mini-mum of 100 and a maximum of 5,000 case instances. Thenumber of the cluster is limited to 12. When the clusteringis applied to find the most similar case, the processing timeis measured with two cases as searching time including theclustering and matching time and only the matching time.The processing time to search for a solution of a group withclustered case instances is compared with that without clus-
tered case instances. The comparative results are illustratedin Fig. 12.
When we considered the processing time including theclustering and matching time, the effectiveness of clusteringwas verified by almost 3,300 case instances. As the numberof instances was increased, the clustering cost was decreasedand the effectiveness of clustering was also increased. Whenonly the matching time was considered, the number of thecase instances was found to be irrelevant to the effectivenessof clustering. Thereby, it is important to reduce the process-ing time by adopting clustering to increase the efficiency ofpersonalized service provided to inhabitants who want to getpersonalized service without any time lag in a smart homeenvironment.
6 Conclusion
In this paper, we propose the PersonAlizedService DisCoveryUsing FuZZY-based CBR and Context Ontology (PAS-CUZZY) framework to provide context-based personalizedservices by utilizing smart appliances in smart home envi-ronments. The collected flat-type context data are transposed
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Fig. 10 Comparison of Type1with Type 2 adaptation (forSubject Group 1)
Fig. 11 Comparison of Type 1with Type 2 adaptation (forSubject Group 2)
Fig. 12 Comparison ofprocessing time with clusteringtime and matching time
into linguistic-type context of the context ontology using theFuzzy set theory. In particular, the case-based context ontol-ogy is composed of context CXT, service SV, and case CSas classes as well as properties P R and instances I nscxt .
Notably, CS is added to the context ontology to effectivelymanage the classes of context and service. In this light, CShas case instances that are linked to context and servicesof the ontology on the case-based context ontology. Case
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instances Ins cxt have different degrees of membership func-tion using the adaptation of fuzzy membership function andmodifying the number of the linguistic terms and the valuesof the membership number. It is designed to provide context-based personalized services. Based on a personalized degreeof membership for fuzzy variables, Ward’s hierarchical clus-tering is performed on the case instances to reduce the burdenof the similarity calculation.
From the experiment, we focused on the verification ofthe effectiveness of the adaptation of the membership func-tion for the personalized service and the clustering of caseinstances on case-based context ontology to effectively findthe most similar case as a solution for the context.
Contribution of the PASCUZZY framework can be sum-marized as follows. First, we developed the case-basedcontext ontology through transposing numerical-type con-text data into linguistic-type context instances. Second, weadapted the fuzzy membership function to provide personal-ized service. Finally, we applied clustering for an effectivesearch for a solution. The proposed PASCUZZY frameworkis applied to smart home appliances in smart home environ-ments and showed the effectiveness and usefulness of the pro-posed concepts. In future studies, to show the effectiveness ofthe proposed concepts, case-based context ontology will beapplied to a variety of smart home appliances that need to pro-vide personalized intelligent smart home services. In addi-tion, if there is no solution for the selected cluster providedfor an efficient search, the cluster expansion mechanism willbe investigated in search of a solution to the problem.
Acknowledgments This research was partially supported by the ITR&D program of MKE/KEIT [No. 10041788, Development of SmartHome Service based on Advanced Context-Awareness] and partiallysupported by the MSIP (Ministry of Science, ICT and Future Plan-ning), Korea, under the “IT Consilience Creative Program” (NIPA-2014-H0201-14-1002) supervised by the NIPA (National IT IndustryPromotion Agency).
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