1. Introduction. Department of Computer, Damghan Branch, Islamic Azad University, Damghan, Iran

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Journal of Advances in Computer Research Quarterly ISSN: 2008-6148

Sari Branch, Islamic Azad University, Sari, I.R.Iran (Vol. 11, No. 1, February 2020), Pages: 69-86 www.jacr.iausari.ac.ir

Nasim Mosahaneh ,

Mojtaba Shokohinia

Department of Computer, Damghan Branch, Islamic Azad University, Damghan, Iran

nasim.mosahaneh@gmail.com ;


Received: 2020/03/06; Accepted: 2020/09/14


Today, the need to deal with ambiguous information in semantic web languages is increasing. Ontology is an important part of the W3C standards for the semantic web, used to define a conceptual standard vocabulary for the exchange of data between systems, the provision of reusable databases, and the facilitation of collaboration across multiple systems. However, classical ontology is not enough to handle the vague information commonly found in many practical areas. An acceptable solution is to incorporate the ability of fuzzy logic to extend classical ontology. SWRL is used to build fuzzy ontology classes and property maps, as well as computations; it is added to ontology and creates new knowledge. In this paper, we introduce an approach based on the production of the fuzzy ontology using SWRL rules, which are the rule-writing language on the Semantic Web. Accordingly, a method for combining SWRL and OWL to produce a fuzzy ontology is proposed. Fuzzy logic is a way to model complex systems that are impossible or very difficult to model using classical modeling methods, much more easily and flexibly.

Keywords: Semantic Web, Owl, Fuzzy Ontology, SWRL

1. Introduction

Today, there is a great deal of interest in developing knowledge representation formalisms that are able to deal with uncertainty. This is a very common need in real-world applications. Despite the undeniable success of ontology, classical ontology languages are not suitable for dealing with the vague or inaccurate knowledge that exists in many areas of real-world applications. Not surprisingly, fuzzy ontologies are useful in several applications, including information retrieval[1, 2, 3], image interpretation[4, 5], software development [10], semantic web, and the Internet [6, 7, 8, 9]. Therefore, there is a need for a standard method for displaying such information. With the growth of the web world and various technologies, the dissemination of information continued unabated, and today there is a huge collection of information that is not easily manageable. Also, most of this information can only be understood and processed by humans and not by machines. Semantic web technology has greatly reduced the problems associated with data management by expressing new capabilities and making content understandable to the machine. The use of semantic web technology, especially ontologies, makes it possible to provide a common structure for information management in an organization by explicitly defining domain concepts


[11].In this paper, we introduce a fuzzy ontology-based approach using SWRL rules, which is the language of semantic web rule writing, for forecasting in the automotive industry. The proposed method is a combination of fuzzy and semantic technologies. Therefore, to maintain a high-quality ontology, the designed ontology must be meaningful, accurate, minimally redundant, and highly enriched. The main purpose of this paper is to establish a logical connection between domain-specific ontology and objects using fuzzy law. To achieve this goal, this paper presents a method for combining SWRL and OWL to produce a fuzzy ontology. Section 2 provides an example of research on semantic web applications in information and knowledge management. We can create ontologies using an ontology editor that supports OWL. In Section 4, a prototype of the ontology, including the hierarchy of classes with the characteristics of each automobile, such as the name of the automobile manufacturer, the type of automobile, the year of production, price, fuel consumption, etc. is developed using web ontology languages (OWL) of Protégé and Java. Section 5.1 shows the main contribution of our work and describes a Protégé plugin for generating fuzzy ontologies to support its method. Thus, the functions of fuzzy membership, price, and fuel consumption are defined and evaluated as features in the ontology, then each feature is defined as a rule in the form of SWRL statements. Also, based on the fuzzy results obtained from these features, new features related to the level of recommendations (low, medium, and high) for buying a car have been produced, for which, rules in SWRL format have been defined. After enforcing the rules, new fuzzy features for each instance appear in the ontology, and finally, by placing the purchase recommendation values (low, medium, and high) in the total recommendation value (TRV) formula, the ontology acts as a system and obtains the total recommendation value (TRV) to buy the car based on the calculation of the values of fuzzy features related to each sample and the resulting final percentage indicates the level of satisfaction with the purchase of the car. Section 6 assesses its practical behavior with some results and motivation to work. Section 7 provides conclusions and ideas for future research. In this research, dealing with ambiguous information on the values of ontology features is improved by using the semantic Web rule-writing language capability. By entering fuzzy logic into OWL, we see features with fuzzy values in the ontology. Fuzzy logic is a way to model complex systems that are impossible or very difficult to model using classical modeling methods, much more easily and flexibly.

2. Examples of fuzzy ontological representations

In recent years, with the expansion of the Semantic Web, various ontologies have gradually emerged in various fields of knowledge. In addition, in a specific domain, different ontologies in terms of lexical, structural, semantic, and fuzzy were designed by different experts to model knowledge in different methodologies.

Much research has been done in this area. In [12], a high amount of information that users constantly provide to the Internet is unorganized and difficult to interpret. Fortunately, there is no point in having too much information that we cannot work with. Therefore, we need methods that organize this information and store it in a way that is easily accessible and processable. In this paper, a new method is proposed using emotion analysis methods to automatically create a fuzzy ontology of free texts provided by users on social networks. In addition, multi-grain fuzzy language modeling methods are used to select the best average for storing information in a fuzzy ontology


[13]. In both fuzzy and classic evaluations, Google outperformed Bing and Yahoo. In addition, it was found that the accuracy of the fuzzy evaluation of search engines is higher than the accuracy of the evaluation of their classic type. The findings in both assessments also showed that Google, Bing, and Yahoo had the lowest false-drop rates, respectively. In addition, the rate of false drop of search engines in the fuzzy evaluation was lower than that in classical evaluation. The study found that Google had the fewest and Yahoo the highest number of duplicate records. The results showed that fuzzy evaluation increases accuracy and decreases the false drop in search engines. Due to the fact that fuzzy evaluation provides a more accurate and realistic degree of obstruction and false drop by spectralizing the relevance of retrieved records, researchers are advised to use fuzzy evaluation to evaluate the performance of search engines. Overall, the research findings showed that the Google search engine performed better than Bing and Yahoo based on three metrics. Therefore, users are advised to use this search engine when searching for Persian information on the web to save their time and money [14]. Ontology is the effective conceptualism commonly used for the Semantic Web. Fuzzy logic can be inserted for ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined hierarchy. However, building a conceptual hierarchy for a particular area can be a daunting task. To address this issue, this paper presents the FOGA (Fuzzy Ontology Generation Framework) for the automatic production of fuzzy ontology in uncertainty information. The FOGA framework includes the following components: fuzzy formal concept analysis, hierarchical concept production, and fuzzy ontology production. Finally, a fuzzy-based technique for integrating other database properties into an ontology is proposed. In [15], traffic congestion in urban areas, especially in large cities, is increasing rapidly. To date, several sensor network-based systems have been proposed to address this issue. However, these techniques are not sufficiently appropriate in terms of monitoring the entire transportation system and providing emergency services if necessary. These techniques require real-time data and intelligent methods to quickly determine traffic activity from useful information. In addition, these systems and websites in the field of transportation and travel in cities rely on privileges for various factors (e.g. safety, crime rate, cleanliness, etc.). These rankings are not efficient enough to provide accurate information, while reviews or tweets are significant because they help passengers and transport supervisors to know about every aspect of the city. However, it is difficult to read passengers, process transportation systems, all reviews, and tweets to get expressive feelings about the city's needs. The optimal solution to this type of problem is to analyze the information contained in social networking platforms and perform emotion analysis. On the other hand, clear ontology-based frameworks cannot extract ambiguous information from tweets and reviews. Therefore, they produce inadequate results. In this regard, this paper suggests that emotion analysis based on fuzzy ontology and the Semantic Web Rule Language (SWRL) and decision-making based on monitoring transportation activities (accidents, automobiles, street conditions, traffic volume, etc.) to be presented by creating an urban feature. Polarity map retrieves these reviews and tweets systems related to city features and transportation activities for travelers. Feature comments are extracted from this retrieval data, and then a fuzzy ontology is used to determine the transport and polarity of urban features [16]. The need to deal with ambiguous information in semantic web languages is increasing and therefore, we need to find a standard way to present such information. We may address this issue by extending current Semantic Web languages to combat ambiguity, or by


providing a way to present information in current standard languages and tools. In this work, we follow the second approach by identifying the syntactic differences that a fuzzy ontology language has to deal with, and by providing a method for representing fuzzy ontologies using OWL annotation properties. We also report on some prototype implementations: Plugin for editing fuzzy ontologies using OWL 2 annotations, some parsers that represent fuzzy ontologies translated into languages supported by some of the arguments using our method.

3. Basic Concepts

3.1. Ontology

In today's world, data and important information on the studied concepts are very important and necessary. With modern developments in the context of information, if information can be stored semantically, better results can be obtained. Accordingly, more knowledge is obtained about the environment of study and its objects and classify the objects and specify their relationship, a complete cognition is obtained which is very valuable and this is ontology. Therefore, ontology is the accurate and formal description of the properties of the area of study. Besides, an ontology defines a common vocabulary for the researchers who want to share the information regarding a specific area[7].

3.2. Fuzzy theory architecture

A fuzzy system is a proper solution for uncertain environments in which ambiguity probability is high. Figure 1 shows the general architecture of the systems based on the fuzzy theory. Fuzzy logic and fuzzy system architecture are described in[17, 18, 19, 20].

Figure 1. The architecture of the fuzzy theory system and its main components

The web dreams of simple interaction between humans and machines where internal cooperation and integrated information exchange take place among web applications and web services are identified fast and accurately. To accomplish these dreams, semantics, and services have grown ambitiously. Thus, methods are required to represent data under uncertainty. The term "uncertainty" indicates various aspects of incomplete knowledge including:

incompleteness: information about the word is not complete, in other words, some information is lost. Ambiguity: The reference of vocabularies in a sentence about the


world is not clear, thus it cannot be detected under what condition, the sentence is satisfied. Empirical: a sentence about the world (event) might be either satisfied or not satisfied in any world, but the world in which it is satisfied is not determined and determining it required obtaining additional information (through experiment).

Randomness: Sentence is a sample of a class. A statistical rule exists for determining the conditions that satisfy samples of the class. Incompatibility: The world which satisfies phrases and sentences does not exist.

The fuzzy propositional logic and ontology languages have a trust level instead of a binary trust value. Fuzzy descriptive logic is a method to represent the structured knowledge that faces inaccuracy and ambiguity in its nature. It is proved theoretically and practically that restricting trust degrees in the fuzzy descriptive logic is useful. In[21], the limited fuzzy descriptive logic is represented as a generalization of the existing methods. In this paper, a restricted ordered set of terms or labels of linguistics is considered as a hypothesis that is useful. It is proved empirically that expert knowledge is offered as linguistics vocabularies. Then, fuzzy descriptive logic based on smooth t-norm is applied to this set. The authors of [22] have presented the first fuzzy ontology inference system called DELOREAN which supports OWL2.

Another application discusses a sustainable solution for storing and restoring RDF triples. In[23], a method has been studied for employing conventional RDF storage systems for sustainable storage and exploration over a large volume of fuzzy information. To this end, a method has been developed for serializing fuzzy information as the RDF triplet. Furthermore, existing languages of the RDF storage base have been manipulated to support fuzzy query services. All these developments have been conducted of FiRE which is a strong inference system to fuzzy descriptive logic and uses fuzzy-SHIN language. In[24], a conversion from ALCfl fuzzy descriptive logic to ALCH classic descriptive logic has been presented preserving the main features of data. In[25], a coding method has been presented to convert uncertain knowledge and fuzzy semantic to RIF-BLD. Then, a development called RIF-URD (Uncertainty Rule Dialect) has been generated to support the direct representation of uncertain knowledge. Moreover, a development based on a combination of DL and LP (Logic Programms) has been presented for representing a fuzzy development based on DLP (Description Logics Programms) called Fuzzy DLP.

3.3. Fuzzy Ontology

Ontologies are used as a proper formalism for representing integrated knowledge in various areas. However, classic ontology based on Boolean logic is not sufficient for representing ambiguous knowledge found in real-world areas. A solution to this problem is to introduce fuzzy logic capabilities to develop the classic ontology. in other words, fuzzy ontology helps to manage the uncertainty of finding information satisfying user's requirements[16].

3.4.The difference between Fuzzy Ontology and Classic Ontology

Classes, samples, concepts, and phrases of classic and fuzzy ontologies are the same. However, all conceptual values of the classic ontology are ambiguous. Classic ontology cannot manage uncertainty. Fuzzy ontology is defined generally that expresses ambiguous knowledge using fuzzy concepts[16].


4. Ontology Representation

As shown in Figure 2, the hierarchy of classes, individuals, object, and data properties are defined. The ontology features along with examples of the automobile and manufacturer and the properties of the object and data are shown in Figures 3 and 4, respectively.


Figure 3. Features of the Automobile Manufacturer


5. The proposed Method

In this section, the implementation and feasibility of our approach are discussed and examined. Also, the main contribution of our work is shown. To support our approach, a Protégé plugin is described for generating fuzzy ontologies that assist users in the process of developing a fuzzy ontology.

5.1. Ontology Fuzzification using SWRL Rules

The proposed method is a combination of fuzzy and semantic technologies. Fuzzy SWRL rules are used to identify the price of a car and the amount of fuel it consumes. Models derived from ontologies are models for providing knowledge about domain entities.Classes and properties represent the data that must be recorded and organized for each of them. To store information about a particular entity, a member (individual) of a class must be created. An individual is an instance of a class whose properties are filled with data values.Our ontology is filled with several individuals to illustrate specific examples of domain domains.This language is a rule built on OWL, added to ontology, and creates new knowledge.An SWRL rule consists of a previous component and a result component separated by an operator ->[26].First, we put 4 members named cheap, Expensive, low fuel consumption, and high fuel consumption, for each of these members, a value is set as a threshold of 250, 400, 7, and 9 liters, respectively, as shown in Figure 5. The membership function of the fuzzy set for cost and fuel consumption is also shown on the membership function in Figure 6.


Figure 6. (a) performance of the fuzzy membership for the price (b) fuzzy set Fuel consumption membership function [27]

To fuzzify ontology, swrl redundancy is used as in Figure 7.

Figure 7. SWRL tab

Here, 12 rules are defined. Table 1 includes the subset of rules used in this architecture for generating fuzzy values of price and fuel consumption. All rules result in new features indicating the constructed properties and variables represent the price of the automobile based on how cheap or expensive the automobile is or how low or high its fuel consumption is. All of the defined swrl rules are created based on formulas and R and L membership functions as shown in Figure 8.


Table 1. SWRL rule for price and fuel consumption

SWRL rule No.

has_limit_value(cheap, ?c) ^ has_price(?m, ?p) ^ swrlb:lessThan(?p, ?c) -> has_cheap_price(?m, "1.0")


has_limit_value(cheap, ?c) ^ has_limit_value(expensive, ?e) ^ has_price(?m, ?p) ^ swrlb:greaterThanOrEqual(?p, ?c) ^ swrlb:lessThan(?p, ?e) ^ swrlb:subtract(?r, ?e, ?p) ^ swrlb:subtract(?sub, ?e, ?c) ^ swrlb:divide(?div, ?r, ?sub) -> has_cheap_price(?m, ?div) 2

has_limit_value(expensive, ?e) ^ has_price(?m, ?p) ^ swrlb:greaterThanOrEqual(?p, ?e) -> has_cheap_price(?m, "0.0")


has_limit_value(expensive, ?c) ^ has_price(?m, ?p) ^ swrlb:greaterThan(?p, ?c) -> has_expensive_price(?m, "1.0")


has_limit_value(expensive, ?e) ^ has_limit_value(cheap, ?c) ^ has_price(?m, ?p) ^ swrlb:greaterThanOrEqual(?p, ?c) ^ swrlb:lessThan(?p, ?e) ^ swrlb:subtract(?r, ?p, ?c) ^ swrlb:subtract(?sub, ?e, ?c) ^ swrlb:divide(?div, ?r, ?sub) -> has_expensive_price(?m, ?div)


has_limit_value(cheap, ?c) ^ has_price(?m, ?p) ^ swrlb:lessThan(?p, ?c) -> has_expensive_price(?m, "0.0")


has_limit_value(low_fuel_consumption, ?c) ^ has_fuel(?m, ?p) ^ swrlb:lessThan(?p, ?c) -> has_low_fuel_consumption(?m, "1.0")


has_limit_value(low_fuel_consumption, ?c) ^ has_limit_value(high_fuel_consumption, ?e) ^ has_fuel(?m, ?p) ^ swrlb:greaterThanOrEqual(?p, ?c) ^ swrlb:lessThan(?p, ?e) ^ swrlb:subtract(?r, ?e, ?p) ^ swrlb:subtract(?sub, ?e, ?c) ^ swrlb:divide(?div, ?r, ?sub) -> has_low_fuel_consumption(?m, ?div)


has_limit_value(high_fuel_consumption, ?e) ^ has_fuel(?m, ?p) ^

swrlb:greaterThanOrEqual(?p, ?e) -> has_low_fuel_consumption(?m, "0.0") 9

has_limit_value(low_fuel_consumption, ?c) ^ has_fuel(?m, ?p) ^ swrlb:lessThan(?p, ?c) -> has_high_fuel_consumption(?m, "0.0")


has_limit_value(low_fuel_consumption, ?c) ^ has_limit_value(high_fuel_consumption, ?e) ^ has_fuel(?m, ?p) ^ swrlb:greaterThanOrEqual(?p, ?c) ^ swrlb:lessThan(?p, ?e) ^ swrlb:subtract(?r, ?p, ?c) ^ swrlb:subtract(?sub, ?e, ?c) ^ swrlb:divide(?div, ?r, ?sub) -> has_high_fuel_consumption(?m, ?div)


has_limit_value(high_fuel_consumption, ?e) ^ has_fuel(?m, ?p) ^

swrlb:greaterThanOrEqual(?p, ?e) -> has_high_fuel_consumption(?m, "1.0") 12


Figure 8. Two specific cases of trapezoid performance (R and L functions)

Thus, by creating 12 rules of the membership functions, swrl uses the inferences of the ontology to add 4 new features to the ontology for each automobile where each feature has a fuzzy value between [0-1]. An ontology with fuzzy values was represented. In the following, the ontology is made more applicable such that it operates as a system and gives satisfaction for buying the automobile considering its fuel consumption and price. Table 2 shows various prices and fuel consumptions making a total of 8 states. Two rules are devised for each case.

Table 2. Different states of price and fuel consumption for the recommendation

Fuzzy swrl rules of Table 3 , add 3 features with fuzzy value for recommendation of each automobile where the features describe how much (low, medium and high) is the automobile is proper for buying. These features are as follows:


Fuzzy rule no. 1 2 3 4

Price C C E E

fuel_consumption L H L H


C = Cheap price E = Expensive price L = low fuel consumption H = high fuel consumption


Has_medium_recommendation_value Has_high_recommendatoin_value

Table 3. Fuzzy swrl rules of recommendation

Also, the recommendation grade is divided into three grades: low (20 points), medium (50 points), and high (80 points), as shown in Figure 9 on the chart.Accordingly, Formula 1 requires the result of the low, medium, and high recommendations to calculate the total recommendation value (TRV) ,Defuzzification. This value is from the evaluation of fuzzy SWRL rules.

Figure 9. Deffuzification function of total menu recommendation points [27]

SWRL rule No.

has_cheap_price(?m, ?c) ^ has_low_fuel_consumption(?m, ?f) ^ swrlb:lessThanOrEqual(?c, ?f) ^ swrlb:greaterThan(?c, "0"^^xsd:integer) -> has_high_recommendation_value(?m, ?c)


has_cheap_price(?m, ?c) ^ has_low_fuel_consumption(?m, ?f) ^ swrlb:lessThanOrEqual(?f, ?c) ^ swrlb:greaterThan(?f, "0"^^xsd:integer) -> has_high_recommendation_value(?m, ?f)


has_cheap_price(?m, ?c) ^ has_high_fuel_consumption(?m, ?f) ^ swrlb:lessThanOrEqual(?f, ?c) ^ swrlb:greaterThan(?f, "0"^^xsd:integer) -> has_medium_recommendation_value(?m, ?f) 15

has_cheap_price(?m, ?c) ^ has_high_fuel_consumption(?m, ?f) ^ swrlb:lessThanOrEqual(?c, ?f) ^ swrlb:greaterThan(?c, "0"^^xsd:integer) -> has_medium_recommendation_value(?m, ?c) 16

has_expensive_price(?m, ?c) ^ has_low_fuel_consumption(?m, ?f) ^ swrlb:lessThanOrEqual(?f, ?c) ^ swrlb:greaterThan(?f, "0"^^xsd:integer) -> has_medium_recommendation_value(?m, ?f) 17

has_expensive_price(?m, ?c) ^ has_low_fuel_consumption(?m, ?f) ^ swrlb:lessThanOrEqual(?c, ?f) ^ swrlb:greaterThan(?c, "0"^^xsd:integer) -> has_medium_recommendation_value(?m, ?c)


has_expensive_price(?m, ?c) ^ has_high_fuel_consumption(?m, ?f) ^

swrlb:lessThanOrEqual(?f, ?c) ^ swrlb:greaterThan(?f, "0.0"^^xsd:decimal) -> has_low_recommendation_value(?m, ?f)


has_expensive_price(?m, ?c) ^ has_high_fuel_consumption(?m, ?f) ^ swrlb:lessThanOrEqual(?c, ?f) -> has_low_recommendation_value(?m, ?c) 20


Formula. TRV (centres’ mean) [27] (lrv 20) (mrv 50) (hrv 80) TRV lrv mrv hrv        

TRV: Total Recommendation Value lrv: low recommendation value mrv: medium recommendation value hrv: high recommendation value

Example 1. Price and fuel consumption of Camry are assigned as initial features as shown in Figure 10.

Figure 10. Features (Price and Fuel consumption) of Camry

By applying the fuzzy swrl rules (rules of Table 1) the following fuzzy features are obtained.

has_cheap_price has_expensive_price has_low_fuel_consumption has_high_fuel_consumption

In addition, rules of Table 3 create fuzzy features of recommendation where the output of integrating SWRL and OWL can be seen in Figure 11.


Figure 11. Features of Camry after integrating swrl and owl

Thus, by inserting the recommendation value (low, medium and high), TRV or defuzzification value is obtained which is shown in the following. The values (20, 50 and 80) are the empirical results and considered as coefficients.

(0.2 20) (0.2 50) (0.5 80) 60 0.2 0.2 0.5

TRV       

 

As can be seen in the above relationship, the obtained value shows that the automobile is 60% proper for buying.

Example 2. Price and fuel consumption for Accent are assigned as the initial features and the outputs of integrating SWRL and OWL are shown in Figures 12 and 13 and the corresponding TRV is also calculated.


Figure 13. Fuzzy features of Accent after integrating swrl and owl (1 20) (0.9 50) (0 80) 34 1 0.9 0 TRV         

An example of the automobiles and their fuzzy features is given in Table 4. Fuzzy features can be obtained using price and fuel consumption of each automobile and finally, TRV is calculated using the obtained values as shown in Figure 14.

Table 4. An example of multiple automobiles and their initial features and the corresponding fuzzy features and TRV Variable Attribute Mazda3 Accent Azera Camry Maxima ioniq Prius 240.0 265.0 200 280 340 370 530 price 7 10 11 8 7 4 3 fuel 1 0 0 0.5 1 1 1 Low_fuel_consumption 0 1 1 0.5 0 0 0 High_fuel_consumption 1 0.9 1 0.8 0.4 0.2 0 Chep_price 0 1 0 0.2 0.6 0.8 1 Expensive_price 0 1 0 0.2 0 0 0 low_recommendation_value 0 0.9 1 0.2 0.6 0.8 1 medium_recommendation_value 1 0 0 0.5 0.4 0.2 0 High_recommendation_value 80 34 50 60 62 65 50 Total_recommendation_value(TRV)


Figure 14. TRV for an example of automobiles

6. Evaluating the Results

The need to deal with ambiguous information in semantic web languages is increasing and therefore, we need to find a standard way to present such information.We may address this issue by extending current Semantic Web languages to combat ambiguity, or by providing a way to present information in current languages and standards. In this method, we implemented fuzzy logic in the ontology using the SWRL plugin properties and extended the ontology.We also generated a number of new features from each of the two initial characteristics for each automobile model, including price and fuel at constant values, by SWRL definition rules, and finally, for each automobile sample, the final value (total recommendation value) or the same defuzzification was achieved.The main purpose of all research in the field of fuzzy ontology is to optimize, provide more accurate results, show uncertainty information, and deal with ambiguous information, improve performance, and data quality. The results obtained in Section 5.1 suggest a fuzzy ontology.Also compared to [2, 6, 7, 10, 12, 13, 14], our job is to be accurate, save time and money in producing fuzzy ontologies, and deal with ambiguous information on the values of ontology features. Also, because the original (classical) ontology does not have the ability to compute fuzzy values, we improved these by using the semantic web rule language feature.In addition, the accuracy of the fuzzy ontology evaluation is higher than that of classical evaluation. As mentioned in [10], in the future, fuzzy ontologies can improve and optimize search engines.Accordingly, our empirical evaluation shows that our approach is extensible and can easily be supported in alternative fuzzy logics, modifier functions, and fuzzy data .

0 20 40 60 80 100 TRV



7. Conclusion

In this paper, a method is presented that can be used to address the problem of fuzzy ontological knowledge. Instead of proposing an ontology language, we propose the fuzzy SWRL plugin as a candidate to become the standard for fuzzy ontologies. Our work considers a fuzzy format of the OWL 2 language. Then, we can make part of the ontology fuzzy by using the properties of the Semantic Web Rule-Writing Language. A summary of ontologies was introduced and a sample of research on semantic web applications in information and knowledge management was presented. Then, a framework for ontology fuzzification was presented, according to the definition of fuzzy membership functions, using SWRL. Ontologies are a key component of all semantic web-based information management systems, which express explicit concepts of domain and provide a common structure for storing information. In general, fuzzy ontology expresses vague knowledge using fuzzy concepts. Ontology fuzzification enabled us to extend the ontology and improve its accuracy and uncertainty by fuzzy logic.We saw its use in the automobile company. Experimental results show that the ontology acts as a system and shows the satisfaction of the car in terms of price and amount of fuel, as well as the level of recommendations (low, medium, high) to buy each car, in the form of a fuzzy number.Finally, by evaluating the level of the final recommendation to buy the desired car, which is obtained by placing the values (low, medium, high) in the formula, we reached the final result for buying a car.This function can be used for various purposes.In the next work, we want to improve and optimize the results of queries (SPARQL query) as well as Reasoner in the ontology by means of fuzzy ontologies and update our programs with new features.


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