Integration of a secure type-2 fuzzy ontology with a multi-agent platform: A proposal to automate...

Information Sciences 198 (2012) 24–47

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Information Sciences

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Integration of a secure type-2 fuzzy ontology with a multi-agent platform:A proposal to automate the personalized flight ticket booking domain

Ahmad C. Bukhari, Yong-Gi Kim ⇑Artificial Intelligence Lab, Department of Computer Science, Gyeongsang National University, Jinju, Kyungnam 660-701, Republic of Korea

a r t i c l e i n f o

Article history:Received 6 August 2011Received in revised form 1 February 2012Accepted 17 February 2012Available online 6 March 2012

Keywords:Type-2 fuzzy ontologyMulti-agent system with ontologyInformation security in fuzzy systemsAutomation of flight booking systemPersonalization with type-2 fuzzy ontology

0020-0255/$ - see front matter � 2012 Elsevier Inchttp://dx.doi.org/10.1016/j.ins.2012.02.036

⇑ Corresponding author.E-mail addresses: [email protected] (A.C. B

a b s t r a c t

Recently, several fuzzy ontology-based solutions have been proposed in the domain ofweb-based applications to reduce the manual work load of daily activities. However,because of the explosive heterogeneity in the current internet structure, the performanceof the available solutions has been decreasing drastically. We propose an integrated securetype-2 fuzzy ontology multi-agent platform (ST2FO-MAS) to completely automate thelaborious process of manual air ticket booking. The air ticket booking domain is rife withuncertainties, and most of the information is based on complex linguistic terminologies.The available fuzzy ontology schemes cannot extract intensively blurred information fromthe internet to provide personalized solutions. Meanwhile, information security attackshave been growing significantly, which is a discouraging indicator for e-commerce appli-cations. The proposed ST2FO-MAS can provide an end-to-end solution that can ideallyaddress the ongoing information security challenges. The accommodation of a multi-agentsystem in a secure type-2 fuzzy ontology boosts the performance of the system because ofits robust autonomous working scheme. To completely judge the performance of the pro-posed solution, we developed an intelligent prototype system that was based on JAVA,JADE and XML security recommendations. We conducted several experiments under thesupervision of domain experts, and we evaluated the performance of the system. Theexperimental results are quite satisfactory and support the efficacy of the proposed model.

� 2012 Elsevier Inc. All rights reserved.

1. Introduction

The process of manual ticket booking is time-consuming and laborious. During the past two decades, researchers havepresented different solutions to make the air ticket booking process trouble-free [26,42]. The advancement of internettechnologies provides an opportunity for the air travel industry to place their ticket reservation portals online. Presently,there are thousands of websites available on the internet that are related to the flight booking domain and are equippedwith itinerary search facilities, but most of them address deal with specific airlines or destinations. Usually, passengersmust visit these websites and sometimes must spend several hours to find an acceptable fare. The travelers are anxiouslywaiting for a feasible solution that can provide personalized outcomes without a user’s full involvement in the entire pro-cess, including bank transactions. At this time, there are several hurdles in the development of such types of solutions, andresearchers are making a maximum effort to resolve the issues. Searching optimal available ticket information from theinternet is considered to be one of the primary components of a passenger support system. However, the heterogeneity

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ukhari), [email protected] (Y.-G. Kim).

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A.C. Bukhari, Y.-G. Kim / Information Sciences 198 (2012) 24–47 25

of the current internet is increasing, with the continuous addition of imprecise data on the internet. Because of theaccumulation of billions of web pages, it is difficult to extract the best information from the internet with available solu-tions. Search engines are common tools for finding information from the internet, but the majority of search engines makeuse of conventional technologies, such as keyword matching mechanisms, to search the web [17]. At present, the ontologyhas proven itself to be an effective technology for relevant information and knowledge searching and sharing. An ontologyis basically a conceptualization of a domain in which the domain terms are arranged in hierarchical order, and appropriaterelationships are created among them [22]. This unique feature of an ontology can become helpful in the development ofan accurate information extraction system. During the past few years, researchers have applied ontologies widely in sev-eral different fields. Additionally, most of the information that resides on the internet is in an imprecise format. A classicalontology addresses only crisp data and cannot find desirable results from vague sources of data. To address this issue,researchers have incorporated fuzzy logic theory into classic ontologies. Fuzzy theory is widely known among researchersin the community because of its ability to address imprecise information [38]. The combination of both technologies hasgained popularity, and researchers have proposed several solutions that are based on fuzzy ontologies in recent years.With the invention of third generation internet technologies, repositories of raw facts on the internet are increasing rap-idly. Many heterogeneous systems are being connected with each other for information sharing, and as a consequence,there is an increase in the complexity of the information. Type-1 fuzzy logic and the solutions derived for this methodare becoming ineffective because a type-1 fuzzy ontology-based system can extract relevant information from the internetonly to a limited extent. Currently, type-2 fuzzy logic systems (T2FLS) are considered to be an effective technology toaddress the information vagueness issue. A type-2 fuzzy ontology can extract the relevant information from intensive hazyheterogeneous sources of data. The multi-agent system (MAS) is one of the most promising technological paradigms in thedevelopment of intelligent information systems. The MAS consists of several autonomous agents that handle their respec-tive tasks by making connections with each other. Typically, we applied MAS in such a situation when a single agent failedto handle the tasks. The mechanism of MAS has the ability to integrate distributed information sources and can make theprocesses automatic. On the other hand, information content security has become the strategic issue in industry, andindustry profitability can be directly linked with secure and reliable information availability. To fully consider the infor-mation security aspect, we started our work and proposed a solution to automate the airline ticket booking process with asystem that involves personal constraints, tour operators’ limitations, and secure bank transaction issues. To provide acomprehensive solution that can cover all of the aspects of a problem statement, we integrated the secure type-2 fuzzyontology with a multi-agent system to make the system secure and to exchange the transparent information amongdifferent modules of the distributed system. Additionally, we applied XML-based content security recommendations.The major contributions to perform this research can be summarized as follows:

� We introduced a novel and real-time scenario handler mechanism called ST2FO-MAS (Secured type-2 fuzzy ontology-Multi-agent system). This system (shown in Fig. 7) is distinctive in its novel architecture and can be applied to the devel-opment of a variety of industrial solutions that are needed.� We proposed an efficient way to develop a ST2FO, which is the backbone of the proposed system. The step-by-step tuto-

rial on the secure type-2 fuzzy ontology development process can be helpful for interested readers to conductexperiments.� We discussed a mechanism for designing a type-2 fuzzy ontology-based multi-agent system, and we integrated the

multi-agent system with a pool of ontologies and external database sources.� To perform the case study, we developed a JAVA-based prototype that takes natural language input as its query. The

internal intelligent processes of the prototype control the query and the optimization processes. The query processagent of the proposed system extracts information from the internet and populates the corpus. In addition, thedecision-making pool of the multi-agent system utilizes the corpus data and finds the optimal ticket information withthe help of a type-2 fuzzy ontology-supported crawler. The agent pool autonomously completes the bank paymentprocedure through the exchange of secure information. In the last step, the system informs the user through emailabout the booked ticket.

During the development stage of the prototype, we have faced several different technical issues. For example, no singletool is available that can automatically design a type-2 ontology and integrate the information security algorithms with theontology. We attempted to make this article self-explanatory so that other researchers can develop a deep understanding ofthe whole process and contribute further to the development of new systems. The experimental results that are generated byutilizing this prototype advocate the efficiency of the proposed architecture. The organization of the remainder of this paperis as follows. Section 3 covers related research studies, which were performed by different researchers at different times.Here, we discussed the theoretical portion, relating to secure type-2 fuzzy ontology. The subsections of Section 3 briefly ex-plain the type-2 ontology, multi-agent system and information on the security role in the proposed system. Section 4 isabout practical modeling of a secure type-2 fuzzy ontology and multi-agent system, while in the subsections of Section 4,we elaborate on the methodology that couples the information content’s security algorithms with the ontology. The pro-posed architecture, which automates the ticket booking system, is studied in Section 6. Finally, Section 7 demonstratesthe experiments and their results.

26 A.C. Bukhari, Y.-G. Kim / Information Sciences 198 (2012) 24–47

2. Related work

Personalized information extraction and intelligent decision making on the basis of extracted information are both con-sidered to be hot topics of current information engineering research. The increasing heterogeneity of the internet makesthese issues more challenging. Currently, people utilize the available search engines to obtain information about specific top-ics. The backend searching mechanism of most of the search engines is still based on keyword matching algorithms. Theavailable search engines are not capable of knowing the underlying meaning of the data that reside on their servers; as aconsequence, the users must perform manual work to obtain the desired information. Scientists have been working on thisissue for the past two decades and have proposed several solutions for exact information extraction. An ontology is one of thesolutions that can make current information extraction tools powerful. Ontologies are an emerging technology that fit underthe umbrella of artificial intelligence and that can be helpful and efficient in the development of intelligent informationextraction solutions. An ontology arranges the domain knowledge into a hierarchical structure (classes, properties format)and generates relationships among the data nodes. This hidden power of an ontology can be exploited with the developmentof intelligent information representations and extraction tools. A crisp ontology can be helpful only in the extraction of aplain set of data. However, most of the information on the internet is in a vague format. Fuzzy set theory is a very populartechnique from artificial intelligence that works excellently in a situation that has uncertain inputs. In the literature, wefound several articles in which scientists utilized classical fuzzy set theory with an ontology to extract exact informationfrom an imprecise set of data. In this section, we summarize work relating to intelligent information extraction and itsutilization in the development of intelligent decision support systems. In [56], Yi et al. proposed a fuzzy ontology-based solu-tion for representing Chinese medicine. This paper highlights the process of fuzzy ontology construction and its applicationto representing Chinese medicine that helps to cure liver diseases. Zhai et al. accommodated a fuzzy ontology-based frame-work in their research [27] to address vague linguistic values of fuzzy concepts in supply chain management. Informationsharing and retrieval are the core activities in supply chain management (SCM). The fuzzy ontology-based framework makesinformation acquisition and sharing convenient. To identify the existing boundaries of knowledge in a patent search deci-sion-support system, researchers suggested a solution in [1] that is based on a fuzzy ontology. Acceptance of any patentis based on its uniqueness; a fuzzy ontology-based solution helps the editors to find a similar domain and patterns againstany requested patent. In [23], Huiying et al. utilized an ontology to develop an enterprise information-retrieval model. Be-cause traditional information retrieval systems cannot understand the user’s potential query requirements due to their key-word-based structure, the usage of an ontology works effectively in such scenarios. Noy et al. progressed one step farther andinvestigated an automated method of fuzzy ontology-generation in a semantic web domain [42]. The FOGA (Fuzzy OntologyGeneration frAmework) brought ease and automation into the manual construction of a fuzzy domain ontology framework.In the literature, we found the multi-disciplinary work of Zhai and his team in which they use a fuzzy ontology framework.They applied a fuzzy ontology to extract the relevant information from an e-commerce domain [28]. They also conductedresearch on knowledge modeling of a fuzzy system and presented their work in [29]. It was noted that the type-1 fuzzyontology-based system can handle imprecise data to some extent; however, it cannot address this type of data perfectlywhen the information is intensively blurred. The solution of this problem appeared in the form of type-2 fuzzy logic andits incorporation with crisp ontology. The type-2 fuzzy ontology-assisted system can handle the fatally blurred data easily.Its three-dimensional structure can handle intensive fuzzy information, which could hold another imprecise value inside it.Chang Shing et al., in [5], introduced an architecture that is supported by a type-2 fuzzy ontology for an application on dietrecommendations for diabetic patients. This paper displayed the T2FO construction steps and suggested a daily dietary planaccording to personal demographic statistics. The architecture of the computer-assisted diet assessment system is proposedin [10]. It is very important to exactly analyze the nutritional facts for food to live a healthy life. The authors of this articleintroduced a fuzzy markup language (FML) to develop a type-2 fuzzy ontology and knowledge base for intelligent decisionmaking. This system can propose a healthy meal based on a geographic region and any religious-based restrictions and pro-vide the nutritional information for the meal.’’ To present the computer Go knowledge, another technique based on a type-2fuzzy ontology is discussed in [11]. In this research, Lee and his team developed a type-2 fuzzy ontology with the help of FML(fuzzy markup language). This architecture helps the players to infer the possible next steps during game playing and in-creases the probability of game winning. Another unique work found in the literature uses a type-2 fuzzy ontology to auto-mate the laborious task of scheduling meetings [6]. Business meetings are a common practice of any organization, and hostsmust spend a substantial amount of time to determine the meeting time that will be the most suitable for all of the attend-ees. Several personal-level constraints are found in meeting scheduling systems, such as attendee availability and convenienttime slots. Jaber et al., in [35], presented a novel approach to automate customized learning paths in an e-learning platform.In this approach, students enter the objectives that they want to achieve at the end of a specific training session. The systemautomatically searches the appropriate course path for the students. The authors in this research coupled multi-ontologieswith a multi-agent system to obtain a synergy of their strengths. The attribute of quality in an electronic health (e-health)system can be achieved by providing transparent access to patient information. Currently, most of the e-health systems con-sist of a number of subsystems that store patients’ data separately. The concept of an integrated electronic health manage-ment system will integrate all of the subsystems of e-health and will show patient information at any location. Research todevelop an integrated health information system is presented in [60]. These researchers utilized a multi-agent system withan ontology to achieve the desired goal. The MAS can search, filter and integrate the relevant patient data by establishing a


connection among heterogeneous health information systems. The MAS makes the patient information interoperable, whichhelps the physicians provide accurate diagnosing and better prescriptions. The tacit knowledge of any organization is con-sidered to be important. In [59], we found a technique that can manage corporate tacit knowledge with the help of an ontol-ogy and multi-agent system. Usually, the companies share their internal working practices in the form of patents. Thepatents are legal documents that elaborate on specific inventions or industrial practices of a specific company. Patentsare considered to be the intellectual property of one company, and no other company or person can adopt those practicesor can use the inventions without prior permission of the company that registers the patent. To expedite the innovation pro-cess inside a company, company knowledge managers require a tool that can automatically extract and analyze the infor-mation that resides on patent documents. At this time, most of the patent management systems are not automatic, andusers must manually sort out bundles of documents to analyze patent information. The system under discussion claimsto solve the laborious activity of manual patent information searching. The distributed systems consist of different modules,and each module works with others to achieve a common goal. Multi-agent systems can be a solution for successfully per-forming the processes of distributed systems. During a literature survey, we found several efforts in which researchers at-tempted to apply a multi-agent system to develop several intelligent solutions. In [30], Jung exploited the multi-agentplatform to achieve an indirect alignment between multiple language ontologies. An ontology alignment is considered tobe important to achieve the semantic interoperability between different ontologies. The available techniques work whenthere are identical ontologies but fail in case of multiple languages and structures. Jung used a case study of tourism to provethe validity of the system. Ryan et al., in [49], demonstrated a multi-agent based autonomic solution named AutoCore to re-solve the protection issues among computational resources. The AutoCore system helps to protect the information contentsand can make a decision to take an action in the case of illegal intrusion because of its intelligent mechanism. The resultsgenerated through this experiment proved the effectiveness of the research. A hybrid holistic approach supported that han-dles the relevant information extraction procedure using an ontology and agents is discussed in [40]. The researchers intro-duced IFAs (information fetching agents) and IMAs (information managing agents) to fetch and manage the relevantinformation, respectively. We studied the past research work in depth and concluded that all of the research that was con-ducted so far has a major flaw regarding information security, and most of the proposed information extraction systems usea classical ontology or a type-1 fuzzy logic system. The type-1 FLS cannot be effective in current scenarios when most of thedeveloping information systems are based on a heterogeneous complex structural design. The proposed secure type-2 fuzzyontology-based multi-agent architecture (ST2FO-MAS) is a novel effort to design an automatic personalized decision supportsystem. In this system, we developed the type-2 fuzzy ontology-based crawler to extract the intensive blur and the hiddeninformation from the World Wide Web, and we merged the type-2 fuzzy ontology with the autonomous agents’ pool to usethe extracted information for intelligent decision making.

3. Type-2 fuzzy set

The backbone of the proposed system is a type-2 fuzzy ontology. To make the article self-contained in this portion, wewill define some concepts, definitions and terminologies before formally describing type-2 fuzzy ontology. Fuzzy set theorywas introduced by Lotfi Zadeh in 1965 [38] to address vague and imprecise concepts. In classical set theory, elements eitherbelong or not to a specific set. The partial membership concept does not exist in classical set theory. However, in fuzzy settheory, the association of an element with a specific set lies between ‘0’ and ‘1’, which is called the degree of association orthe membership degree [38]. Fuzzy set theory added the generalization concept to classical set theory and expanded itscapabilities to represent imprecise boundaries such as hot, tall, and low speed. A fuzzy set can be defined as follows:

Definition 1. A fuzzy set ‘s’ over the universe of discourse ‘X’ can be defined by its membership function l_s, which mapselement ‘x’ to values between [0,1].

l sðxÞ : X ! ½0;1� ð1Þ

In the above equation, s is the fuzzy set and l is the degree of membership. Here, x 2 X and l_s(x) represents the degree ofmembership by which x belongs to X. Here, x is considered to be a full member of X if l_s(x) = 1 and is considered to be apartial member if l_s(x) is between 0 and 1, for example, 0.65. If X is continuous, then S can be written as follows:

�S ¼Z

xl sðxÞ=x ð2Þ

A fuzzy set �S over the universe of discourse X can be organized into an ordered set of pairs:

�S ¼ fðx;l sðxÞÞjx 2 Xg ð3Þ

Definition 2. Let X and Y be the two universe discourses. A fuzzy relation R(x,y) is a set consisting of the product space X � Yin a membership function.

Rðx; yÞ ¼ fðx; yÞ;l RðX;YÞjðX; YÞj 2 X � Yg ð4Þ


In compliance with fuzzy set theory, x and y are considered to be fuzzy sets in the product space X � Y. The variable l is thefuzzy membership degree. A fuzzy relation represents the degree of the presence or absence, interaction or interconnected-ness between the elements of two crisp sets.

Definition 3. Uncertainty and vagueness are the vital parts of any real-time system. Usually, fuzzy logic-based systemsemploy classical fuzzy logic theory, which can handle uncertainty and vagueness at a certain level. Because of the increasingdemand of automatic systems, we need more sophisticated solutions, and for the development of these systems, we requiresomething superior to classical fuzzy logic. Type-2 fuzzy logic [16,33] is the extended version of classical fuzzy set theory,which can address uncertainty and vagueness better than type-1 fuzzy set theory. In type-1 fuzzy set theory, the member-ship values are crisp, while type-2 fuzzy systems have fuzzy membership values. Because of the unique nested behavior of atype-2 fuzzy system, it is also called a fuzzy–fuzzy set. The explanation part of Definition 3 provides a more detailed view of atype-2 fuzzy set.

Explanation of definition 3: To understand the working of type-2 fuzzy set theory more deeply, we provide the followingexample. Suppose that ‘‘Price of airline ticket’’ is the fuzzy term that can be expressed in graphical format for elaboration, asfollows (see Fig. 1a):

The graphs illustrate the ticket price in dollars, which is $1000 in the above case. The red dotted line indicates the ticketprice in dollars, and its corresponding fuzzy membership value (primary membership degree) is 0.5. Note that in the case ofsimple fuzzy set theory, the fuzzy membership is a crisp value (0.5). The three-dimensional structure of T2FS yielded anotherdegree of membership that is called the secondary degree of membership. The third dimension of the T2FS helps researchersto calculate the fuzziness of the terms more deeply and can be helpful when it is difficult to recognize simple fuzzy mem-bership values. Because of uncertainty in the system, the center points of both of the values change continuously. This sit-uation generates difficulty for the type-1 fuzzy system (classical fuzzy systems) when attempting to exactly count themembership function and compels us to use type 2 fuzzy set theory to address the uncertainty [31]. Fig. 1b depicts the

Fig. 1a. Type-1 fuzzy system.

Fig. 1b. Type-2 fuzzy system.


type-2 fuzzy set theory concept graphically [51]. The graphs show the interval of fuzzy membership values to be from 0.25 to0.58. The upper side boundaries of Fig. 1b are called upper membership functions, and the lower side boundaries are calledlower membership functions. The region captured between the upper and the lower membership boundaries is called thefootprint of uncertainty FoU(A). Mathematically, we can define type-2 fuzzy set theory as follows:

A T2FS ‘A’ is characterized by a type-2 membership function l_A(X,U), where x 2 X, and l 2 Jx # [0, 1] is expressed as thefollowing:

Å ¼ fðx;lÞ;l Aðx;lÞj 8x 2 X 8l 2 Jx # ½0;1�g ð4Þ

where 0 6 l_A(x,l) 6 1. Another way to define A is the following:

Å ¼Xx2X

Xl2Jx

l Aðx;lÞðx;lÞ ; Jx # ½0;1� ð5Þ

Rule-based systems are used to represent and infer from uncertain and imprecise data. Logically, a rule-based system can bedivided into four parts: rule, fuzzification process, and inference engine and output process. These components are intercon-nected components that work mutually to perform a task. Rules are considered to be a core part of any fuzzy logical system[16]. The effectiveness of a fuzzy system directly depends on the strengths and weaknesses of the rules. The rules are usuallyexpressed in the form of IF and THEN statements. The IF and THEN portion of the rules are called the antecedent and conse-quent, respectively. Similarly, x = x0; then, for each value of x, we have the following:

l Aðx0Þ ¼X

l 2 Jxfx0ðlÞ=l; for l 2 Jx0 # ½0;1� and x0 2 x ð6Þ

Here, l_A (x0) is called the secondary membership function. The shaded region in Fig. 1b is called the footprint of uncertainty(FoU). FoU is the aggregation of all of the primary membership functions [14,21]. The FoU of A0 can be stated as in the fol-lowing equation format:

FoUðA0Þ ¼ lx2X JX ¼ DOUðA0Þ DoU ¼ Degree of Uncertainty ð7Þ

3.1. Secure type-2 fuzzy ontology

An ontology is a branch of metaphysics that focuses on the study of existence. An ontology is an explicit and formal spec-ification of a shared conceptualization of a specific domain, which is a machine-readable and human-understandable format[3,49]. In an ontology, we focus on concepts (classes) of the domain, their values, properties and the relationships amongthem. An ontology arranges classes in a hierarchical structure, in the form of subclasses and superclass hierarchies, and itdefines which property has constraints on its values. Basically, an ontology is developed to share common understandingabout domain knowledge among people and software, for the purpose of reusing the classes of a domain instead of remod-eling them. An ontology is written in a specific language called OWL (Web Ontology Language) [18,55], which is developedby W3C. OWL is a very powerful language and is specially designed for ontology modeling. OWL-2 is the extended version ofOWL that holds new features and rationales. To increase the expressivity level of OWL, several new constructs were intro-duced along with their unique modeling features, such as extended annotations and complex data representations. The con-struction of an ontology has become both an art and an understanding of engineering processes [12,13].

Mathematically, an ontology can be expressed as follows:

eO ¼ ðC; P;R;V ;VCÞ ð8Þ

In the above expression, the notations C, P, R, V, VC represent the concepts, the properties of the concepts, the relationshipsamong the concepts, the values of the concepts and the constraints on the property values, respectively. An ontology hasbecome a hotspot in the intelligent information engineering domain. It is very generalized in its scope. An ontology canbe classified as a meta ontology, a domain ontology or an application-level ontology. A domain ontology is commonly usedfor the development of a solution; we use a domain ontology to represent information in a specific domain. There is not aspecific way to develop an ontology; researchers utilize a different way to define an ontology according to their needs anddemands. The proposed secured type-2 fuzzy ontology is a six stack demonstration of a fuzzy domain. The type-2 fuzzyontology structure is encapsulated into an XML-based logical security container that can protect the ontology contents frombeing used illicitly [25,27,41]. In the proposed T2FO model, stacks are called the following: information security stack, do-main category stack, fuzzy classes stack, fuzzy class’s property stack, type-1 fuzzy sets stack and type-2 fuzzy sets stack.Fig. 2 illustrates the secured type-2 fuzzy ontology model graphically. Let TB0 be the secured fuzzy ontology of the ticketbooking domain; mathematically, this secured type-2 fuzzy ontology can be described as follows:

T B0¼fS Glo;ELoc;Mc;FC;FCP;FOU;F I;Rg ð9Þ

where S Glo is the XML digital content security at the global level, ELoc is the XML hashing security at the local level (to sharethe secured information with other components of the system),Mc is the main category of the fuzzy domain, FC is the fuzzyconcept and FCP is the fuzzy concept’s property, which can be further subdivided into steps.

Fig. 2. The anatomy of a type-2 secure fuzzy ontology.


FCP ¼ fFCPmax;FCPmin;FCPmax�min;FPsc;l;J g ð10Þ

where FCPmax is the maximum threshold value of the fuzzy concept’s property, FCmin is the minimum threshold value of thefuzzy concept’s property, FPsc is the strength controller of the fuzzy concept property (which defines the intensity level ofthe property) and the notations l and J are the primary and secondary discourse universes, respectively. F_I is the fuzzyinstances, and R is the relation among type-1 fuzzy sets and type-2 fuzzy sets. We attempted to explain the step-by-stepprocedure for the modeling of secured type-2 fuzzy ontologies in Section 4. For simplicity, we divided the processes intosteps. At first, we developed simple flight reservation, personal and booking domain ontologies using OWL-2 and the Protégétool. On completion of the simple domain ontology process, we exported our designed ontology into a fuzzy OWL plug-in[20] of Protégé. The fuzzy OWL plug-in is a semiautomatic tool that is specially designed to build a fuzzy ontology that copeswith vague information in a real-time system. Information content security is the essential part of a secure fuzzy ontology;we applied XML-based security to make our ontology secure.

3.2. Information security role in an ontology

Information is considered to be the most valuable asset of any organization. An organization’s growth is directly propor-tional to the availability of information for decision making. An ontology is an emerging medium for sharing knowledge andinformation among various stakeholders in a heterogeneous environment. Currently, secure information has become a stra-tegic issue for online businesses. In an ontology, all types of information are shared in plain text format. This scenario raisesthe issues of information leakage and the altering and deletion of information content [39]. Information security can beachieved to increase the satisfaction level of authentication, authorization, integrity and confidentiality, and we appliedXML-based security recommendations to achieve these standards. To meet the security requirements, XML security providesXML vocabularies and processing rules. The most significant aspect to XML security is that it uses legacy cryptographic stan-dards such as DES and AES with the robust features of XML. The blending of these two approaches provides flexibility anddynamicity in the developed solution [57]. To understand the importance of information security and the possible securitythreats to information, we first briefly discuss the information security and the ontology threats; then, we will share thetechnique that we adopted to implement XML security in our proposed ontological structure.

3.2.1. Possible information security threats on ontologyAn ontology can be considered to be one of the trusted mediums for sharing domain knowledge among stakeholders. A

classical ontology is based on the OWL format, which is an extended and recommended type of XML. All types of information


in an ontology and in a semantic web are in the form of plain XML content. Thus, security can easily be broken, and an at-tacker can use the obtained information for a destructive purpose [43,58]. Here, we highlight some security loopholes in thecurrent ontology structure, and we suggest a way to make an ontology secure. XML (eXtentisible Markup Language) is awell-known markup language on the Internet and is most widely used to store and share data. In a multi-agent system,we used an ontology to share data among different agents. XML has various types of data holders or datatypes, such as PCDA-TA, CDATA, and NUMBER. The CDATA [2,52] can store any type of data, whether it belongs to XML syntax or not. The attack-ers use malicious code to break the security of the ontology because CDATA can take any data as input. Inside the ontology,all of the contents are in the form of plain coding, which can be used for any illegal purpose. Developers usually store XPATHinformation in the CDATA datatype. One possible way to hack an ontology is to change the XPATH information with a mali-ciously designed CDATA field. DDoS is one of the common attacks in the hacker community. It is an attack against the avail-ability of services. Attackers need little effort to generate the DoS attack [2,19]. Usually, a piece of code is embedded tocontinuously generate a request command, which leads to halting the routine services. Flooded and DDoS attacks are en-hanced forms of DoS attacks. An XML bomb attack is considered to be a derivation of a DoS attack. To perform this typeof attack, hackers usually design malicious code in XML that follows XML linguistic standards, and they embed the code intoan XML file. The XML is very diverse in its usage; during the process of compilation, the compiler becomes stuck at the mali-cious code point and can become a reason for system failure [2]. This type of attack is generated to exploit security weaknessin the OWL header. OWL syntax is written in a hierarchical format; a piece of code that can replace the signature of a file canhelp the hackers to use the system information illegally [2,39]. Security weaknesses in XML and OWL can become the reasonfor new attacks on the application’s policies, trust and addressing scheme.

4. Development of a secure type-2 fuzzy domain ontology (ST2FO) and a multi-agent system (T2FO-MAS)

As described in the previous section, a domain ontology is used for knowledge representation and information sharing fora specific domain from the real world [3,4]. This fact mostly explains the development process of the air ticket booking do-main. We found that in the literature, some researchers performed work on the travel ontology domain, and their work waslimited in scope and was designed for academic purposes only. Our ontology modeling approach is based on the type-2 fuzzyconcept [12] and its incorporation with a secure ontology that is novel in its usage; this approach can handle any type of real-world scenario that is related to the air ticket booking domain. The most prominent aspect to our approach is the applicationof the ontology contents security feature, which prevents attempts of unauthorized contents from altering or removinginformation. Domain experts and the collection of data from the internet are the two primary actors that can help to expeditethe modeling process of a domain ontology. We gathered travel support system information from the internet and classifiedit manually. Subsequently, we followed the ontology development steps that were defined in [22] to model our domainontology. The seven ontology development steps that we followed to model our domain ontology are the following [4,42]:

� Determine the domain and scope of the ontology.� Consider reusing existing ontologies.� Enumerate important terms in the ontology.� Define the classes and the class hierarchy.� Define the properties of the classes.� Define the facets of the slots.� Create instances.

We used Protégé 4.1 with OWL-2 to develop our ontology [37] [53]. Protégé is a very trusted tool in the information engi-neering circle for the construction of a semi-automatic ontology. During the phase of ontology development, we consulteddomain experts at each critical step to achieve accuracy in experiments. The graph in Fig. 3 depicts the relationships amongontology terms. We designed this graph using the OntoGraf tab of protégé. To achieve a high accuracy in the results, we di-vided the secure type-2 fuzzy ontology into steps. The equation below illustrates the parts clearly.

ST2FO = Domain ontology Development + Incorporation of Type 1 Fuzzy Set Theory in ontology + Enrichment of Type-2 Fuzzylogic in ontology + Information Security implementation (Application of XML security Recommendations)

First, we developed the simple domain ontology after deeply analyzing the design structure of a classical ontology. Weexported our ontology to a Protégé fuzzy-owl plug-in, to define the fuzzy concepts.

4.1. Incorporation of classical fuzzy set theory into an ontology

To represent vague information in an ontology, we utilized fuzzy set theory techniques in our pre-modeled domain ontol-ogy. We used the Fuzzy OWL plug-in of Protégé, which is a semiautomatic tool for the creation of a fuzzy ontology [20].

This plug-in can help to create a fuzzy data type, fuzzy concepts, fuzzy modifiers, and fuzzy axioms. A fuzzy ontology con-sists of classes, properties, instances and axioms that are the same as in a crisp ontology, but the basic difference between acrisp and fuzzy ontology is that all of the concepts and most of their values are blurry terms in the case of a fuzzy ontology[21,54]. A fuzzy ontology can be defined in the form of fuzzy sets. Let FC be a fuzzy class in a universe of discourse l; then,

Fig. 3. Relationships among ontology terms: the graph generated by the OntoGraf Tab.

Fig. 4. Screenshot of the fuzzy OWL plug-in of Protégé.


FC : l! ½0;1� and the relationship between two ontology classes are the fuzzy relation A � B: l ? [0,1]. Fig. 4 is a screenshotof the Fuzzy OWL plug-in, which shows a feature menu, an annotation pellet and a reasoner. The annotation feature of proté-gé is used to define a fuzzy concept in a fuzzy ontology. The manual process of annotation adding is complex and error-prun-ing, but the fuzzy OWL tab helps us to make this process easy to use by applying its automation power. We exported ourdomain ontology into the fuzzy OWL tab by using the export feature of the plug-in, and we added fuzzy logic into the alreadymarked fuzzy terms. A classification of a cheap ticket can be described in a fuzzy form as Ticket u $ hasPrice.CheapTicket. Sim-ilarly, a very cheap ticket can be expressed as Very(Ticket u $ hasPrice.CheapTicket). To check the efficiency of the ontology,several reasoners are available, for example, DL reasoner, Pellet, and DeLorean are the names of popular reasoners. We applied


the DeLorean reasoner to obtain inference results from our ontology. The internal working of DeLorean [15,16] first converts afuzzy ontology into a crisp ontology and then generates the inference results.

4.2. Fuzzy type-2 ontology in OWL-2

The Fuzzy OWL plug-in of Protégé has been designed to incorporate the type-1 fuzzy logic system (T1FLS) into a crispontology that efficiently handles the type-1 level fuzzy terms in a domain ontology. However, in this experiment, ourrequirements are the development of type-2 fuzzy ontologies, which are the advanced form of type-1 fuzzy ontologies

Fig. 5. Graphical architecture of the proposed ST2FO.


[20,45,47]. We have already explained the working mechanisms of a type-2 fuzzy ontology. A powerful type-2 fuzzy logicsystem can be helpful in the development of a highly intelligent ontology, which further can extract and present hiddenassociated knowledge within domain terms. In Fig. 5, we presented the explanatory architecture of a secure type-2 fuzzyontology. In the first row of the architecture, readers can see technical terms such as XML encryption, XACML, SAML, XKMSand XML digital signatures. These terms are the XML security recommendations for secure communication. We integratedthese technologies into our proposed system to make our system secure. The implementation of the XML security sectionbetter explains this unique technical merger. In the second row of the secure type-2 fuzzy ontology structure, the terms tra-vel agencies, destinations, and travel packages are high-level domain categories that can be further subdivided into classesand properties. In the third row, we highlighted some intensive fuzzy terminologies with their properties. The fuzzy OWLplug-in generates the type-1 fuzzy sets on the basis of these fuzzy classes and properties. During the practical modelingof a T2FO, we faced various technical challenges because of a lack of established technologies. The Fuzzy OWL tab of Protégécannot handle intensively blurred data. Therefore, for the development of the T2FO, we imported the type-1 fuzzy ontologyinto a simple text editor, and we defined the intensive fuzzy terminologies and their relationships manually. To perform thistask, we first segregated all of the associated concepts, and we categorized those concepts into crisp, partial fuzzy, full fuzzyand intensively fuzzy. As shown in Fig. 5, terms that include air fare, travel time, ticket class and delay represent intensivefuzzy terms. We developed a module in our prototype to produce a fuzzy-based annotation among the terms. The domainexperts at this stage shared some data of already developed case studies, which helped us in the creation of extended levelannotations among the ontology terms. During the development of the T2FO, we found the OWL-2 to be an effective rule-defining language. A modified version of the DeLorean reasoner was used to generate the type-2 fuzzy inference. The exper-imental section explains the working of the DeLorean on T2FO.

4.3. Implementation of XML security recommendations in the T2FO

XML security provides five security standards: XML digital signature, XML encryption, XML key management specifica-tion (XKMS), security assertion markup language and XML access control markup language (XACML), for information contentsecurity [40]. As explained in the introduction section, the contents of the ontology are in plain format, and anyone can ex-ploit this weakness. We applied XML encryption to encrypt the document contents. It is interesting to note that XML securitystandards are designed not only to provide content security for XML documents but also for use in applying informationsecurity to many types of non-XML documents [34]. The XML encryption uses both symmetric and asymmetric key encryp-tions at the same time. In symmetric key security, the sender and receiver use the same key for encryption and decryption ofa specific document. In asymmetric or private key encryption, the sender encrypts the document with the receiver’s publickey, and the receiver uses its private key to decrypt the document. In past experiments, it was widely accepted that publickey encryption provides better security than asymmetric encryption [46]. The XML encryption uses the symmetric key toencrypt the documents or the contents inside the documents; after that, it encrypts the key with the receiver’s publickey. At the receiver end, the receiver first decrypts the key with his/her private key and afterwards uses the decryptedkey (which is actually a public key) to decrypt the document. We developed the passenger ontology, which holds all ofthe information that is related to a passenger. In a customer passenger, we store some confidential information, such asthe passenger’s credit card number and passwords of banks. The personal preference agent, which is designed on the basisof a personal fuzzy ontology, directly interacts with a third party for ticket selection and to make bank transactions [40,44].The XML encryption standards ensure the integrity of the ontology contents during content transmission. The code belowexplains the incorporation of XML security recommendations into an ontology. In this example, we use public key encryptionto transparently share the information from a sender agent to a receiver agent. Our developed tool first automatically con-verts the OWL ontology to XML nodes and then adds the security features into the ontology without requiring any userinteraction.

<? XML version=‘‘1.0’’?><! DOCTYPE Ontology [<! ENTITY xsd ‘‘http://www.w3.org/2001/XMLSchema#’’ >]><owlx:Ontology owlx:name = ’’http://www.ailab.gnu.ac.kr/t2fo’’xmlns:owlx = ‘‘http://www.w3.org/2003/05/owl-xml’’>< CustomerInfo xmlns = ‘http://www.ailab.gnu.ac.kr/st2fo-mas/person_ontology’>

<Name>ahmad chan</Name><EncryptedData Type = ‘http://www.w3.org/2001/04/xmlenc#Element’

xmlns = ‘http://www.w3.org/2001/04/xmlenc#’><EncryptionMethod Algorithm = ‘http://www.w3.org/2001/04/xmlenc#tripledes-cbc’/><KeyInfo xmlns = ‘http://www.w3.org/2000/09/xmldsig#’>

<EncryptedKey xmlns = ‘http://www.w3.org/2001/04/xmlenc#’><EncryptionMethod Algorithm = ‘http://www.w3.org/2001/04/xmlenc#rsa-1_5’/>

<KeyInfo xmlns = ‘http://www.w3.org/2000/09/xmldsig#’>


<KeyName>white tiger</KeyName></KeyInfo><CipherData>

<CipherValue>vHE@#$&&JUIOFdefghj...</CipherValue></CipherData></EncryptedKey></KeyInfo><CipherData><CipherValue>yyFE%!JJNIcflijnvcthsdrtg...</CipherValue></CipherData></EncryptedData></CustomerInfo></owlx:Ontology>

This encryption prevents illegitimate intrusion into ontology’s contents during content transmissions. The XML accesscontrol markup language (XACML) defines the rules and access level for all of the stakeholders in the system [46]. DoS (denialof service) attacks are usually generated when an unauthorized user obtains access to the main server and runs a maliciousscript that halts ongoing processes. The XML security standard secures the system from internal or external security threats.

4.4. A type-2 fuzzy ontology-based multi-agent system (T2FO-MAS)

Heterogeneity and complexity factors increase every day in modern software applications. A multi-agent system is con-sidered to be an efficient technology in the development of distributed systems. A multi-agent system is, basically, a group ofinterconnected agents in which each agent works autonomously [9,7]. An agent is a collection of code that is designed toperform a specific task on behalf of its user. All of the agents have certain common characteristics; for example, an agentis designed to work independently while sharing information and collaborating with other components of the system[54]. Usually, an agent has a local scope, and it does not know about other concurrent processes. The decentralized structurefeature of an agent keeps the agent in a working state in the case of a system failure. The multi-agent system is used pref-erentially in environments in which a single agent cannot solve a problem because of diversity and complexity. In this re-search, we designed five different types of agents (Query Processing Agent (QPA), the Personal Preferences and ScheduleMaintaining Agent (PPSA), the Type-2 Inference Engine Agent (T2IA), the Secured Bank Transaction Agent (SBTA) and theTicket reservation Agent (TRA)) to automate the entire system. The knowledge base of these agents is based on a type-2 fuzzyontology. We used JADE (JAVA Agent Development Environment) to develop a general model of the agents. Subsequently, weadded a type-2 fuzzy ontology into the action functions to perform tasks intelligently [8,48].

JADE [7] is a well-known agent modeling integrated development environment that facilitates developers by providing agraphical interface for the creation, modification and configuration of agents based on a type-2 fuzzy ontology. Before thedevelopment of such tools, researchers had to develop their agents manually; the manual creation procedure was verytime-consuming, and the probability of errors was very high. We used an XML-based secure communication system forthe exchange of information, both inside and outside of the multi-agent system. XML is widely used as a data-holding lan-guage and is commonly used in distributed systems for communication purposes. JADE follows FIPA (Foundation for Intel-ligent Physical Agents) rules and regulations. The FIPA is a nonprofit Swiss-based organization that aims to promote theusage of multi-agent systems in modern software applications. Since its creation, FIPA has made significant achievementstoward developing standards for MAS. The fundamental language for JADE is JAVA, and its code is available under a generalpublic license (GPL) for research and personal use [48]. There are two components of JADE that are considered to be essentialparts of a FIPA agent platform. AMS is the agent management system; it controls all of the access rights across the platformby maintaining a directory of agent identifiers (AFD). Table 1 shows the schema of the agents that are used in our experi-ment. In Table 1, the Jade.Core.Agent class is used to create a simple agent. To automate the ticket-booking domain, we devel-oped five intelligent agents, specifically the query processing gent (QPA), the Personal preferences and schedule maintainingagent (PPSA), the type-2 fuzzy inference agent (T2FIA), the secured bank transaction agent (SBTA) and the ticket reservationagent (TRA). JADE API consists of several useful libraries and classes that are in compliance with FIPA standards. JADE acts asa mediator in the development process of the multi-agent system. According to the algorithm, the QPA takes the input fromthe user at an initialization stage of QPA. It performs natural language processing, query optimization and web crawling [52].The agent stores all of the fetched results in corpus 1. During the result saving process, the system starts a new thread andinitializes the PPRA. PPRA holds all types of user travel information, including passenger meeting schedules, passenger bud-get allocations and preferences. The PPRA requests to the TRA to find the optimal ticket according to the passenger’s require-ments. The TRA, with the help of the type-2 ticket booking ontology, finds the tickets and sends the results to T2FIA. TheT2FIA processes the input by using its internal intelligent algorithm and extracts the most appropriate fare rate from thedata. After ticket selection, the PPRA and the TRA both collectively forward the request to the SBTA for a bank transactionbecause all of the bank-related information is stored in the PPRA. Thus, the PPRA creates a secure channel and makes a trans-

Table 1(a) Algorithm for internal working of multi-agent systems, (b) a generic flow chart of internal working.

Import Jade.core.agent;//Package ImportingPublic class TicketBookMAS{StartupQPA (){getInput () {// taking input from user}//Intialization stageStore ();g.Action (){NLP Processing ();QueryOptimization ();Webcrawling ();}R = Get.Result ();mkConnect (IA) // Make connection with inference agentStart thread 1Delay 1000 ms;Startup IA (){Mkconnect (PPRA & & QPA){//sharing XML based authentication kerRight_cheching (); //application of XAML security standardsAuthetication ();}//After Connection with PPRA AND QPAInferencing (); //optimal ticket selection based on information provided

by QPAStoreResults ();Start thread 2Thread1 stop//store information and break the processStartup (SBTA & & TRA){//sharing XML based authentication kerRight_cheching (); //application of XAML security standardsAuthetication ();}{//Initialization of SSL + XML ENCRYPTION(SECURE SOCKET LAYER)Autoauthenticate ();Q = resultant input = Optimal ticket;RequestForReservation (Iternary Number like 152895623462);//for bank payment, taking user concern first and then sharing bank

credential through secure XKMS channelReservation Completed; //information displayed and stored and closing

all connectionTerminate thread 1//Close all process}}}


action on behalf of the passenger. The SBTA and the TRA send the optimal ticket information to the PPRA, and the PPRA in-forms the passenger through email or an SMS [7].

5. The proposed STFO-MAS and its application in the flight booking domain

This section briefly explains the working of our proposed secure type-2 fuzzy ontology-based multi-agent system. Thisarchitecture is a novel attempt to fully automate the manual process of ticket selection, reservation and booking. The ticketbooking system can be subdivided into two activities: an appropriate ticket selection and ticket purchasing using online pay-ment gateways. The personalized ticket selection process is complex and has several constraints; for example, the price of aticket should be within the range of a specific buyer (user preference information is managed by a personal ontology and issupervised by PPSA), flight timing, passenger meal selection and passenger route selection. To perform these activities, weneed a comprehensive technology that can store and handle all of these constraints automatically. We proposed an ontologywith the incorporation of type-2 fuzzy logic for this purpose. The second activity is the intelligent automation of the pro-cesses. Multi-agent systems have become popular in the development of complex and heterogeneous systems. In our pro-posal, the TRA takes input from a corpus and communicates it with a PPSA to verify the requirements. The PPSA and the TRAcreate another communication channel with the FIA, which is the main brain of the MAS. When these three agents reach the


point of mutual interest, they involve the SBTA through a secure communications channel for a secure transaction and ticketconfirmation. In the proposed system, all of the communication is managed by XML security parameters. Fig. 6 defines thearchitecture of the proposed system. In the coming subsections, we explain the internal working of the proposed systembriefly.

5.1. The Query Handling Agent (QHA)

The query handling agent divides its functionality into four parts: tokenization, word-category disambiguation, shallowparsing and query building and optimization. These four autonomous activities are performed in series. The GATE (General-ized Architecture of Text Engineering) [24,50] API is used for the development of a natural language processing module. Theinformation engineering process takes the scattered data as input and converts it into meaningful information for furtherreuse. The activity of information processing is not as simple because most of the data are in the form of a human languageformat. The information processing tool provides a facility for tokenization, part of speech tagging and named-entity recog-nition. The GATE is an architecture, development environment and framework for building information engineering systemsthat can process human language. ANNIE (A Nearly New Information Engineering) is the information extraction module thatis a part of the GATE suite. ANNIE is highly recommended by researchers because of its effectiveness in the information engi-neering domain. For a better understanding of a working system, we take a case study in which a user enters a natural lan-guage query into the system to find the appropriate ticket. We assume that the user has already stored his/her personalpreference information in a personal ontology. The information includes a user budget detail, personal preference, a meetingtime schedule and other information related to meeting and ticket booking. The sample query is shown in Fig. 6:

5.1.1. The tokenization processTokenization is the process of splitting up streams of bytes into small chunks or tokens [49]. The sentence splitting pro-

cess usually uses delimiters to split a string into small chunks. The most commonly used delimiters are (;, .: _) and whitespaces. The tokenization function takes the client query as input and removes the delimiters from the query. In the aboveexample, we took a sample query ‘‘I want to go from Seoul to London to attend a meeting. The meeting will be held in the after-noon, so I want to have a vegetarian lunch. Please book a ticket in the economy class with an inexpensive rate and a minimumdelay.’’ After applying the tokenization process, we obtain results such as the following: ‘ I’, ‘want’ . . . ‘rate’, ‘and’ ‘mini-mum’, ‘delay’. The results generated by the tokenization process are stored in an array format, and the system sends the arrayto the next function for query generation.

5.1.2. The word-category disambiguation and shallow parsing processThe word-category disambiguation is the process that syntactically categorizes each word in the string. Its internal

mechanism analyzes and tags each word of a string according to its grammatical order, such as the following: ‘‘I(noun) wantto(preposition) go(verb) from(preposition) Seoul(noun) to London(noun) to attend(preposition) a meeting(verb). The meeting will be held in theafternoon (noun,adjective), so I want to have(verb) a vegetarian(adjective) lunch(noun). Please book (verb) a ticket(noun) in the economyclass(noun+adjective) with an inexpensive rate(noun+adjective) and a minimum delay(noun+adjective).’’ The built-in functionality of GATEAPI is used to categorize the words into nouns, verbs, adjectives, prepositions, adverbs, conjunctions and interjections[39,49]. The tagging and categorization of a sentence is a complex task because sometimes words change their meaningand structure according to the sentence. Thus, to recognize the words as a noun, pronoun and verb has become very difficultin some situations, but the trained API of GATE can perform this task efficiently. The sentence catcher mechanism of a shal-low parsing state finds verbs and a noun from an array generated by the process of tokenization and word category disam-biguation and arranges them grammatically to form a query.

5.1.3. Query building and optimizationAt the query building and optimization stage, we receive a shallow parsed array [8,39]. The query building algorithm per-

forms optimizations at the beginning; it eliminates the preposition, pronouns and articles (a, an, the, of, as) to convert thenatural language text to a proper searching query. In the algorithm, the notations T[k], OQ1 and IFT are known as the textarray, the optimize query at stage 1 and the initial filtration and tagging, respectively. We used publicly available search en-gines such as Google, Yahoo, MSN, and NAVER to perform our experiments. The filtration process stores the results into aninitial corpus (CPS), as mentioned in algorithm 1. To obtain the best result from an experiment, we set the upper and lowerpopulation threshold values for the corpus. For example, we set a 30 record limit as the lower threshold MnTV and a 600record limit for the upper threshold MxTV. The count function in the algorithm calculates the records that are stored in aninitial corpus; if the numbers of stored records are more than the upper threshold boundary, then the algorithm will usethe search engine’s literals and make the query specialized. In case the number of records is found to be lower than the men-tioned limit, the algorithm consults the associated ontology to regenerate the alternate query and randomizes the words inthe query for every web search, until a result value is obtained within the defined limits. The algorithm in Table 2 defines theprocess mathematically.

Fig. 6. A sample of a passenger query in human language format.

Fig. 7. A graphical view of a secure type-2 fuzzy ontology-based multi-agent framework for the air ticketing domain.



5.2. The type-2 fuzzy ontology-based crawler

The fuzzy ontology crawler plays an important role, which is to search for relevant information from extraneous fuzzyresources. Most of the available web crawlers use a keyword-based search mechanism and cannot find exact information.We developed a type-2 fuzzy ontology-based crawler using JAVA, type-2 fuzzy ontology and ANNIE (Another Nearly NewInformation Extraction). Fig. 8 displays the screenshot of the query processing module of prototype. The QPA populatesthe corpus with related data using publicly available search engines, as explained in the previous step, and the unique struc-ture of the ontology helps the crawler to find the exact information. It forwards the crawling information to the agents’ poolfor further action.

5.3. Decision support multi-agent pool

Previously, we found relevant air tickets with travel agency information. The role of multi-agent systems is to automatethe remaining function of ticket booking, which includes negotiation with the travel agency for the ticket booking and thecreation of a secure communication channel with a passenger bank account for a secure bank transaction. For this purpose,we developed a multi-agent system and embedded that system into our prototype system. We developed five soft agents toperform segregated tasks. The agent’s name, with an acronym, and its functionality can be viewed in the agent schema,shown in Table 3.

The PPSA takes the input from a type-2 fuzzy ontology crawler and verifies the passenger’s personal preferences. We as-sumed that PPSA contains all of the passenger-level information, including the passenger’s budget, the passenger’s prefer-ences and the passenger’s time schedule. The fuzzy ontology crawler also uses personal preference ontology, which is thepart of the ontology repository that finds the information according to the passenger’s requirement. This process verifiesthe results once again; to be certain that the extracted information meets the passenger’s requirements. At the time of infor-mation evaluation, it creates an underlying link with T2FIA to make occasional decisions [32,36]. The communications

Table 2The QPA internal working algorithm.

Function QueryModelling (){Let T be the text array which is created by the part of speech and shallow parsing stage process.T[k] = {w1,w2,w3, . . . ,wk}//Remove the preposition p, pronoun PNand article ATfrom T [K] array////////Query Optimization Stage 1////////////OQ1 = T[K] � {(p + PN + AT)};Let P be the pool of publically available search engines like Google, Yahoo, Naver, MSNP = {Google g, Yahoo y, Naver n, MSN m}//Initial filtration and taggingIFT = filter P(OQ1);IFT = Extracted Links (EL�)CPS = EL�

Let MnTV be the minimum threshold value and MxTV be the maximum threshold valueMnTV = 30;MxTV = 600;Total result Extracted (TRE)=COUNT (CPS);If ((CPS)> MnTV & & (CPS)> MnTV < MxTV){// sent into main corpusMC = CPS;}Else if((CPS) <= MnTV)Then//Change the query String Word OrderT 0 ½k� ¼ RANDðT½K�Þ; which is ¼ Q 01And Q 01;Q

02;Q

03 ¼

P3i¼1Q 0

//Filter and populate the corpus again

CPS = ift = filter PP3

i¼1Q 0� �

;

//Use Ontology Lexicon to find the alternate word for query. Let OnQ be the ontology function, which can match and replace the word with ontology KM.

OnQ ¼ Replac OnP3

i¼1Q 0� ��

/////////Optimization stage 2////////////Search, Count and populate the corpusElse if(CPS) > = MxTV{//Make the query specialized add ‘+’, ‘‘’’ and other query concatenations.MC = CPS;//End of Query Modeling and optimization}}


among the agents are encrypted by XML encryption, which provides certainty that this information will not be accessible toany third parties. If the information is according to the requirements, then the PPSA generates an alert and applies the securesocket layer with the XML secure protocol to access the passenger’s bank details. The passenger bank account information issaved in a personal ontology, and PPSA is authorized to use that information without the passenger’s involvement. In the laststep, the PPSA informs the passenger through email or through SMS about the ticket booking status.

6. Experiments and results

To evaluate the performance of our proposed system, we implemented the system in JAVA. We selected JAVA because ofits ability to design real-time complex systems with security and reliability. To make a system powerful enough to addressheterogeneity, we accommodated the CORBA (Common Object Request Broker Architecture) with JAVA native API. In ourexperiment, we used two PCs with an Intel (R) Core (TMI) 2 Quad CPU; one was used as a multi-agent server and the otherwas used as a client terminal for data storage. JADE, XML securities, RMI, SWING package and OWL-2 based type-2 fuzzyontology are the underlying technologies that were used to build the prototype. To judge the performance of the whole sys-tem, we divided the evaluation process into four phases: the ontology analysis phase, the two system security testing phasesand the overall system efficiency assessment phase.

6.1. Ontology performance analyzing phase

We analyzed the performance of designed ontologies in each step of the development phase, and we compared the resultsat the completion of each round to measure the improvement level. To measure the efficiency of the crisp domain ontology,we designed several diverse queries with the domain expert’s consultancy. The Manchester OWL-2 syntax is used to build thequeries. The protégé tool provides the Pellet reasoner to draw the inference results from the ontology. Some queries withtheir results can be viewed in the following screenshots. The results generated by the queries were very close to ourexpectations.

Evaluation Query 1: Travel_AgencyandprovideTravelScheduling value RyanAirLineExplanation: In this query, the ontology analyst wants to know the name of the travel agencies that can help a passenger

to book a ticket according to the passenger’s schedule. The output of the query can be viewed in Fig. 9.

Fig. 8. A screenshot of a JAVA-based prototype for automating a flight booking case study.

Table 3Multi-agent system schema.

Agent name Agentacronym

Functionality

Query processing agent QPA � Natural language to query building� Query Processing� Query Optimization

Personal preferences and schedule maintainingagent

PPSA � Interaction with personal ontology� Communication with other agents to provide the personal preferences

information� Monitoring the information process and implementations of user

constraints.

Type-2 fuzzy inference engine agent T2FIA � Critical decision making based on information� Remain in touch all the time with PPSA and SBTA� Responsible for making underlying connection with fuzzy ontology

Secured bank transaction agent SBTA � Receiving requests for transaction.� Authentication� Resource allocation� Transaction processing� Log generation

Ticket reservation agent TRA � Making connection with travel agency databases� Finding and reserion of the optimal ticket� Keep in touch with T2FIA AND PPSA


Output of query: Swedish Travel agencyEvaluation Query 2: Travel_Agency and bookHotelReservation value HotelReservation1Explanation: This query shows the list of travel agencies that can book flight tickets along with hotels. Fig. 10 demon-

strates the output of the query.Output of query: XANADU and Swedish TRAVEL agencies

6.2. Fuzzy type-1 and type-2 ontologies performance evaluation

The Pellet and DL reasoner were designed to parse the crisp ontology and to draw the inference results from the ontology.These reasoners cannot work in case of an intensively blurred ontology. Nevertheless, the type-1 and the type-2 fuzzy ontol-ogies have many vague terms. We picked the DeLorean (DEscription LOgic REasoner with vAgueNess) [21] to generate theinference results from type-1 and type-2 fuzzy ontologies. The DeLorean is one of the strongest reasoners based on Jena APIthat can generate reasoning from a fuzzy-based ontology. It can provide support for the fuzzy DLs SROIQ (D) and SHOIN (D),which are alternatives to OWL and OWL-2. The DeLorean is, basically, a type of reducer that converts type-1 and type-2ontologies into crisp ontologies. We parsed our type-1 and type-2 fuzzy ontologies from the DeLorean reasoner and obtaineda crisp ontology. Furthermore, we used the Pellet reasoner on a processed crisp ontology to generate the reasoning. We eval-uated domain expert and ontology tester queries to evaluate the performance, and we compared the results. Because theexpressivity levels in type-1 and type-2 fuzzy ontologies are very high, the information was shown at deeper expressivitylevels.

6.3. System security evaluation

DoS (Denial of service) is considered to be one of the biggest security threats in any distributed system. It is a very com-mon type attack, which can be easily generated and diagnosed, but sometimes it becomes difficult to restore the system fromthe attack in a short time span. We implemented XML-based security on the contents and system level to prevent such typesof destructive activity. We designed a simple system monitoring utility as a module in our prototype; it can keep records ofthe system activities in the log files and can inform the user in case of unusual behavior inside the system. Fig. 11 shows thescreenshot of the security evaluator module. This utility can generate the ping requests any number of times against the sys-tem. In our case, we inserted the multi-agent server IP address and port number, and we generated the 50000 ping requests.At the same time, we calculated the outputs and result generation times. When we implemented the system level security,our system generated a beep after detecting the threat and paused all of the activities to balance the workload. These attackswere generated to create a load on the server, and we attempted to halt the system; however, our server resisted the attackand generated a beep to inform the system operator about unauthorized intrusion. We demonstrated the second test to eval-uate the ontology and information content. To check the security implemented at the authentication and authorization level,we anonymized an agent and attempted to send the malicious packets to other agents and to the pool of ontologies. TheXML-based security, which was implemented at the system and content level, prevented the intrusion and generated anindicator for the attack. The system automatically maintains the logs generated during this operation.

Fig. 9. The output of evaluation query 1.

Fig. 10. The output of evaluation query 2.

Fig. 11. The screenshot of the system security evaluator module.


Table 5Overall system performance results recoded in the case of the secured type-1 fuzzy ontology.

Total number of resourceextracted corpus 1 (ce)

Number of trueelements (te)

No of falseelements (fe)

Precisionpercentage (PP)(%)

Recallpercentage(RC) (%)

Job CompletionTime (JCT) (s)

Volunteer 1 569 311 258 68.8 71.2 228Volunteer 2 479 292 187 71.9 67.3 258Volunteer 3 587 496 91 86.5 54.2 286Volunteer 4 389 278 111 77.9 58.3 305Volunteer 5 495 267 228 68.5 64.9 315

Table 4Overall system performance results recoded in the case of the secured domain ontology.




Precisionpercentage (PP)(%)




Table 6Overall system performance results recoded in the case of the secured type-2 fuzzy ontology.




Precisionpercentage (PR)(%)





6.4. The overall system performance measurement

An information system can be categorized on the basis of its effectiveness. There are some known ways to define the effi-ciency of an information system, such as the precision, recall and time. In our experiment, we asked volunteers (we re-quested five volunteers (V1, V2, V3, V4, and V5) to join us in our experiments.) to make inquiries to the system. Werecorded the results at three levels. First, we used a crisp ontology and asked the volunteers to enter their queries, and

Fig. 12. A graphical view of the results using a crisp ontology.

Fig. 13. A graphical view of the results using a type-1 fuzzy ontology.


we noted the time, precision and recall. Subsequently, we configured the type-1 and type-2 fuzzy ontology and calculatedthe results. Mathematically, the precision and recall can be expressed as the following:

RC ¼ ce=ðceþ teÞ � 100%

PR ¼ ce=ðceþ feÞ � 100%

where ‘ce’ is the total number of records that are extracted from the internet, and ‘te’ and ‘fe’ represent the true and falseelements in the extracted records.

The results calculated from these scenarios are shown in the Tables 4–6 below.The bar charts (Figs. 12–14,) depict the experimental results in graphical format. In these graphs, the volunteers are on

the x-axis, and the number of resources is on the y-axis. The y0-axis of the graphs presents the time span of one completeprocess. It is clear from the experimental results that the precision rate increases with the application of a type-2 fuzzyontology. In Fig. 13, volunteer 1 has a precision rate of 61.1 % in the case of a crisp ontology. After the application of atype-2 fuzzy ontology, the precision rate increases to 78.2%. As additional computational power is required to execute thetype-2 fuzzy ontology, the type-2 fuzzy ontology-based system takes more time in its query execution than a crisp ontol-ogy-based system (see graphs). In Fig. 15, the graph depicts the performance analysis between the crisp, type-1 fuzzy andtype-2 fuzzy ontology. It is clear from this figure that the average precision rate increases significantly by incorporatingthe type-2 fuzzy ontology in the development of a precise information extraction system.

7. Conclusions and future work

In this paper, the proposal of a secure type-2 fuzzy ontology-based multi-agent framework is discussed to automate thetricky task of optimal ticket booking. The automation of personalized ticket booking includes the following: precise informa-tion extraction, optimal information selection, personal preferences, secure payment gateways, and secure information dis-semination. The proposed integrated solution promises to provide the best results in an uncertain and heterogeneousenvironment. To implement the proposal, we developed a prototype system that is based on a type-2 fuzzy ontology, JADE

Fig. 14. A graphical view of the results using a type-2 fuzzy ontology.

Fig. 15. The performance analysis of a crisp, type-1 and type-2 fuzzy ontology.


and JAVA technologies. This prototype takes input from a client in natural language format; it extracts the desired featuresfrom the input and, after applying the optimization process, translates the natural language input into a structured searchingstring. The sophisticated underlying technologies of the proposed system make the personalized ticket booking process auto-matic. Experimental results that are derived from the proposed prototype system show that our approach is capable ofachieving a high level of performance. From the experimental data, we can analyze that precise results can be achievedby applying type-2 fuzzy theory with a crisp ontology. The graphs show significant improvement in overall system efficiencywhen we use a secure type-2 ontology with multi-agent systems. The direct involvement of domain experts and real-timecase studies help us to produce an intelligent solution that can book an optimal flight ticket that is closer to a human flightticket reservation agent. As we involved a computational algorithm in our system to increase the precision rate, we notedthat the query execution takes more time in the case of the type-2 secured fuzzy ontology approach. To check the efficiencyand information content security at the system and module levels, we designed several experiments. For example, to checkthe security resistance level, we developed an evaluation module that generates DoS and ontology contents, altering attacksagainst the system. The results produced during these experiments are very satisfactory. Currently, we are analyzing oursolution with different case studies and attempting to fix the loopholes in order to make this proposed solution perfect inall respects. In the future, we will automate the manual process of type-2 fuzzy ontology generation and will develop a smartphone application to make the system remote and more user friendly.

Acknowledgements

This work was supported by the Korea National Research Foundation (NRF) Grant funded by the Korean Government (No.2010-0002346).

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