A Fuzzy Logic Model of Document Usage in Decision ...

British Journal of Mathematics & Computer Science 15(5): 1-13, 2016, Article no.BJMCS.24576 ISSN: 2231-0851

SCIENCEDOMAIN international www.sciencedomain.org

A Fuzzy Logic Model of Document Usage in Decision Problem Resolution Lukman Adewale Akanbi1* 1

Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria. Author’s contribution The sole author designed, analyzed and interpreted and prepared the manuscript. Article Information

DOI: 10.9734/BJMCS/2016/24576 Editor(s): (1) Andrej V. Plotnikov, Department of Applied and Calculus Mathematics and CAD, Odessa State Academy of Civil Engineering and Architecture, Ukraine. Reviewers: (1) Anonymous, Hacettepe University, Turkey. (2) Anonymous, Czech Republic. (3) Mehmet Şengonul, Nevsehir HBV University, Turkey. (4) Hasni Neji, University of Carthage, Tunisia. Complete Peer review History: http://sciencedomain.org/review-history/13926

Original Research Article

Received: 26th January 2016 Accepted: 15th March 2016 Published: 30th March 2016

_______________________________________________________________________________

Abstract The degree of usefulness of a document to the resolution of a decision problem is not only a function of terms in the document index matching terms in the user query. It is also a function of the user’s ability to express the problem in query terms that will facilitate document retrieval. This user’s ability is dependent on the experience as well as cognitive capacity of the user. A fuzzy logic model for capturing document degree of usefulness in the resolution of decision problem is presented in this study. The model is based on three parameters namely: (i) number of similar problems handled by the user, (ii) number of years the user had spent in the establishment and (iii) user’s direct rating of the document. The output of the fuzzy inference process is a document degree of usefulness to the resolution of decision a problem. The model has been integrated into a decision support system developed in another study which produced a platform for searching for relevant documents based on their previous usage.

Keywords: Document usage; fuzzy logic; document index; usage modelling; decision problem.

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*Corresponding author: E-mail: [email protected];

Akanbi; BJMCS, 15(5): 1-13, 2016; Article no.BJMCS.24576

1 Introduction In a more general term, a decision problem (DP) is a form of challenge that faces an individual or an organisation at a particular time. This challenge, if not addressed in a timely manner, is capable of degenerating into more complex challenges that may require more cognitive and material resources to resolve. The process of resolving any problem usually involves decision making. Wang and Soergel in [1] described decision making as a problem solving process that involves information acquisition and processing. It was further stated in the work that human rationality in decision making is bounded by the situation and by human mental computational powers. According to Zaidi-Chtourou and Bouzidi in [2], the relationship between information and decision making is a complex domain which has been at the centre of research for several years, and more recently researchers have shown a relationship between information quality and quality in decision making which has consequences on the organisation strategy. In information retrieval for the purpose of resolving a decision problem, the amount of information in terms of documents as well as detailed information about their previous usage that are available to users is a major factor that influences result of the problem resolution [3]. David in [4] pointed out that the degree of relevance of information (as contained in documents) can only be measured based on its contribution to the resolution of the decision problem of the decision maker (user). The process of determining the level of contribution of a document to the resolution of a decision problem cannot be automatically inferred, rather it is a function of the users’ capability in terms of cognitive ability and experience over time. These users’ parameters are continuous in nature and are usually describe with vague terms. Therefore, a fuzzy logic model for capturing degree of document usefulness in the resolution of a decision problem is presented in this work. The rest of the paper is organized as follows: Related works is presented in section 2. Detail description of the fuzzy logic model is contained in section 3. The result of the model based on data acquired are discussed in section 4. Section 5 shows the conclusion and some application areas of the model.

1.1 Related works Researchers in the document and documentation research have over the years tried to define the term document or documentation. A common approach is to use document as a generic term to denote physical information resource, rather than to limit it to text-bearing objects in specific physical media such as paper, papyrus, vellum and microfilm [5]. Paul Otlet was reported in [6] to have extended the definition of document to include graphic and written records which are representations of ideas or objects, but the objects themselves can be regarded as "documents", if one is informed by observing them. As examples of such documents, Otlet cited natural objects, artefacts, objects bearing traces of human activity (such as archaeological finds), explanatory models, educational games and works of art. Briet in [7], defined a document as any concrete or symbolic indexical sign or symbol, preserved or recorded for the purpose of representing, reconstituting, or proving a physical or intellectual phenomenon. It was elaborated further in the work that while a star, a pebble rolled by a torrent and a living animal are not documents, the photographs and the catalogues of stars, the stones in a museum of mineralogy, and the animals that are catalogued and shown in a zoo are documents [7]. According to [6], the implication of the definition of document in [7], is that documentation should not be viewed as being concerned with texts but with access to evidence. From Paul Otlet’s object as document, and Suzanne Briet’s physical evidence as document as pointed out in [6], it is clear that documents are used to transmit and preserve knowledge in terms of object and evidence. Considering documents as a semiotic product that facilitates transactional communication among the

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community of professionals has been reported in the literature. For example, Zacklad in [8] reported that, a document can be taken to mean a semiotic product transcribed or recorded on a perennial substrate, which is endowed with specific attributes intended to facilitate the practices associated with its subsequent utilization in the framework of distributed communicational transactions. From the foregoing, it is evidenced that documents usually hold information that transcends the meaning of individual terms that make up the document. Fuzzy Logic is a multi-valued logic that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, or on/off. It is primarily concerned with quantifying and reasoning about vague or fuzzy terms that appear in human natural language [9]. The concept of fuzzy set was introduced by Zadeh in [10] as a means of representing and manipulating data that was not precise, but rather fuzzy. There is a strong relationship between Boolean logic and the concept of a subset, and there is a similar strong relationship between fuzzy logic and fuzzy subset theory [11]. For example in classical set theory, a subset A of a set X can be defined by its characteristic function µ A as a mapping from the elements of X to the elements of the set {0, 1}, that is, µ A : X → {0, 1}. The key concept of fuzzy sets is to give a membership degree to each set member. In classical set (usually called a crisp set), whether an object belongs to the set or not is binary [12]. In essence, the object either belongs to the set completely or does not belong to it at all, there is nothing in between. In the real word, however, a lot of things are ambiguous. For instance, an object may be beautiful in some sense but not completely. The degree that an object belongs to a set is called its membership degree. The function that generalises the membership degrees of all members in a set is called its membership function. The range of a membership function is the interval between 0 and 1. In other words, the maximum membership degree an object may have is 1 and the minimum is 0 [12]. Fuzzy logic has been found to be suitable for modelling decision systems with vague or imprecise input. For example authors in [13] presented a novel fuzzy linear assignment method for multi-attribute group decision making in spare part inventory classification problem. It was demonstrated in the study that the fuzzy linear assignment method is easy to apply and able to provide effective spare parts inventory classes under uncertain environments. The flexibility with which human thought could be modelled with fuzzy logic that provides for capturing situations between none inclusive and total inclusive in an event is one of the motivation for using the fuzzy logic in this work.

2 Model Development Determining the Document Degree of Relevance (DDR) in the resolution of decision problems is an important means of preserving the effort of user in recognising various documents that contribute to the resolution of decision problems. This could serve as a tool to train upcoming users the art of solving problem in an establishment. The DDR is a system derived measure of document’s value in the problem solving task of the users. It is proposed to be based on three parameters, namely (i) the users’ rating of the document’s relevance to the problem resolution, (ii) the users’ number of years in the establishment, and (iii) the number of similar problem handled by the user in the past. The user’s rating parameter is the most crucial and it is inherently a function of user’s experience. It is expressed in human terms which are usually vague. The DDR is therefore determined with the use of fuzzy inference engine. The degree of relevance of a document to a decision problem cannot be binary always. A document will be relevant to a decision problem at different levels based on the level of knowledge of the user in the business of the organisation. This level of knowledge of the user can also not be binary as it varies based on the number of years spent in the organisation and the number of similar cases the user participated in resolving. In order to measure DDR, a fuzzy logic model presented in Fig. 1 is formulated. The model has three inputs and one output. Generally, there are five parts of the fuzzy inference process, namely (i) fuzzification of the input variables, (ii) application of the fuzzy operator (AND or OR) in the antecedent, (iii) implication from

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the antecedent to the consequent, (iv) aggregation of the consequents across the rules, and (v) defuzzification of the output values. The first stage in the development of the fuzzy model is the fuzzification of the input data. There are three inputs to the model (i.e. number of similar problem handled by the user denoted as NP, users’ number of years denoted as NY, and users’ rating of document relevance to a decision problem denoted as UR). The output of the model is the document degree of relevance to the resolution of a decision problem denoted as DDR. Membership function is defined for each of the input variables and the output variable. For the input variables, a Universe of Discourse (UoD) is defined over 0 and 5 for NP, 0 and 10 for NY and 0 and 5 for UR. The UoD of [0, 5] for NP input indicates that the range of the number of problems handled by the user in past is from 0 (i.e. no similar problem in the past) to 5 (i.e. at least five similar problems). For the NY with UoD of [0, 10], the UoD ranges from 0 (i.e. someone who had not spent up to a year in the establishment) to 10 (i.e. someone that had spent at least 10 years in the establishment). The UoD of [0, 5] was chosen for the UR input because it is sufficient to represent the user’s description of document relevance. For the output variable the UoD ranges from 0 to 10. These UoDs are divided into several regions which belong to different predicate such as Not Relevant (NR), Slightly Relevant (SR), Relevant (R) and Very Relevant (VR). This UoD of [0, 10] is chosen for output variable because it is sufficient to represent the output variable. It is the composition of the predicates that form the fuzzy sets. Different types of membership function exists for the implementation of fuzzy logic system, however, triangular and trapezoid membership functions have been used in this work because of their simplicity in terms of calculation.

User’s Number of Years in the Establishment (NY) Users’ Rating of Document Relevance (UR)

Fuzzy Inference Engine

Defuzzification Process

Number of Similar Problem Handled by the User (NP)

Fuzzification Process

Fig. 2 is the pictorial representation of the membership function for the NP. On the x-axis is the number of similar problems that the users have participated in resolving in the organisation which is subdivided into different predicates. The y-axis is the degree of membership of NP in the predicates. From the figure, there are five predicates i.e. VS for very small number of similar problem, S for small number, M for mid number, H for high number, and VH for very high number of similar problem. For example, if the number of similar problem handled by a user is one or less, the NP is absolutely very small, whereas if the is four and half, the NP has degree in both high and very high membership function. Note that user participation in the resolution of problem can be whole or partial. If it is whole then a value of one (1) is added to the user’s NP otherwise a fraction that corresponds to the extent of participation of the user is added to the user’s NP. Equations 1 through 5 describe the membership function mathematically. The membership function for NY is shown in Fig. 3.

Document Degree of Relevance (DDR)

Fig. 1. Fuzzy logic model of document degree of relevance to decision problem

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1

µ VS(NP)

µ S(NP)

NP ≤ 1

=

(1) -NP + 2

1 ≤ NP ≤ 2

NP - 1

1

≤NP ≤ 2 (2)

=

µ M(NP)

µ H(NP)

µ VH(NP)

- NP + 3

2 ≤ NP ≤ 3

NP - 2

2 ≤NP

≤3 (3)

= - NP + 4

3 ≤ NP ≤ 4

NP - 3

3 ≤NP ≤ 4

- NP + 5

4 ≤ NP ≤ 5

NP - 4

4 ≤ NP ≤ 5

= (4)

= 1

NP ≥ 5

(5)

The x-axis is the number of years that users have spent in the organisation which is subdivided into different predicates. The predicates are i) TR for trainee level, ii) L for low level, iii) M for Mid-level, iv) H for High level, and VH for Very High level. The y-axis is the level, ii) L for low level, iii) M for Mid-level, iv) H for High level, and VH for Very High level. The y-axis is the degree of membership of NY in the predicates. Equations 6 through 10 describe the membership function mathematically. It is clear from the figure that the predicates have their corresponding range of data (number of years) overlapping one another. This is so, because the transition of the users from one level of knowledge to another is a gradual and continuous process. Therefore the NY is a fuzzy set with {TR, L, M, H, VH} elements. Each of the elements has a crisp value from the UoD for which its degree of membership is absolute. Fig. 4 is the membership function diagram for the users’ rating of document relevance to a decision problem. The x-axis is the user’s rating of document in terms of its contribution to the resolution of the decision problem. This rating, no matter how bias it may be, provides essential information on the way the documents

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are being used. The assumption here, however, is that the more the experience of users, the better and fair their rating will be.

VS

H

M

S

1

VH Legend VS = Very Small S = Small M = Mid H = High VH = Very High

µ(NP)

0

1

2

3

4

5

Number of Similar Problem handled in the past Fig. 2. Membership function for NP handled by the user

L

TR

M

1

H

VH Legend TR = Trainee L = Low M = Mid H = High VH = Very High

µ(NY)

0

2

4

6 8 Number of years

10

Fig. 3. Membership function for number of years spent by the user

µ

1 (UR)

NR

SR

R

VR Legend NR = Not Relevant SR = Slightly Relevant R = Relevant VR = Very Relevant

0

1

2

3

4

5

User’s Rating of Document Fig. 4. Membership function for users’ rating of document relevance

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1

µ TR(NY)

µ L(NY)

NY ≤ 2 (6)

= -(NY/2) + 2

2 ≤ NY ≤ 4

(NY/2) - 1

2 ≤NY ≤ 4 (7)

=

µ M(NY)

µ H(NY)

µ VH(NY)

- (NY/2) + 3

4 ≤ NY ≤ 6

(NY/2) - 2

4 ≤NY ≤ 6 (8)

= - (NY/2) + 4

6 ≤ NY

≤8

(NY/2) - 3

6 ≤NY ≤ 8 (9)

= - (NY/2) + 5

8 ≤ NY ≤ 10

(NY/2) - 4

8 ≤NY

≤ 10 (10)

= 1

NY ≥10

Since this rating is obtained from the user as fuzzy term (not relevant, slightly relevant, relevant or very relevant), the predicates are described over a UoD of [0, 5]. UR is therefore a fuzzy set with the following elements {NR, SL, R, VR}. These predicates are distributed over the UoD with NR equivalent to 1, SL equivalent to 2, R equivalent to 3 and VR equivalent to 4. Equations 11 through 14 describe the membership function mathematically. The output of the fuzzy model is the system’s measure of the document’s degree of relevance to a decision problem denoted by DDR. It is the result of fuzzy inference engine processing of the three inputs. The DDR is defined in terms of the following predicates: not relevant (NR), slightly relevant (SR), relevant (R), and

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very relevant (VR). These predicates are the eventual rating of the documents by the system which are terms understandable by the users. The predicates are defined over a UoD of [1, 10] to be able to get crisp value equivalent of the fuzzy terms. The crisp value can be used to determine the relevance of any document in numeral term such as percentage. This crisp equivalent of the predicates is also required for proper mapping of input to output by the fuzzy inference engine. The crisp values for the predicates are obtained by dividing the UoD into various regions with values 2, 4 and 6, correspond to NR, SR and R, respectively while value 8 corresponds to VR. Figure 5 is the membership function diagram for the output, while equations 15 through 18 describe the output membership function mathematically. 1

µ NR(UR)

µ SR(UR)

µ R(UR)

R ≤ 1 (11)

= -R + 2

1≤ R ≤ 2

R-1

1 ≤R ≤ 2 (12)

= -R + 3

2≤ R ≤ 3

R-2

2 ≤R ≤ 3 (13)

=

µ VR(UR)

-R + 4

3≤ R ≤ 4

R-3

3 ≤R

≤4 (14)

= -R + 5

4≤ R ≤ 5

The fuzzy inference engine operation is used to convert the input fuzzy set into an output fuzzy set through an inference process which includes rule block formation, rule composition, rule firing, implication and aggregation. The rule block consists of a number of rules which are interrelated and normally operated based on certain set criteria. The number of rules is determined in line with the complexity of the associated fuzzy system. A fuzzy rule is composed of two parts, namely the IF part and the THEN part. Unlike the conventional rule-based mechanism, fuzzy rules allow the use of imprecise, uncertain and ambiguous terms [14].

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NR

SR

2

4

1

R

VR

µ(DDR)

0

6 8 10 Crisp value for DDR

Fig. 5. Membership function for DDR

DDR/2

µ NR(DDR)

=

(15) - DDR/2 + 2

DDR/2 - 1

µ SR(DDR)

2 ≤ DDR ≤ 4 (16)

DDR/2 - 2

µ VR(DDR)

2 ≤ DR ≤ 4

= - DDR/2 + 3

µ R(DDR)

0 ≤ DDR ≤ 2

4 ≤ DDR ≤ 6 4 ≤ DDR ≤ 6 (17)

= -DDR/2 + 4

6 ≤DDR ≤ 8

DDR/2 - 3

6 ≤ DDR

≤8 (18)

= - DDR/2 + 5

8 ≤ DDR ≤ 10

Seventy-five (75) rules were established for deriving the degree of relevance of a document to the resolution of a decision problem. It is easy to produce the rule that lead to the determination of the degree of relevance of a particular document to the resolution of a particular decision problem. For example, within the rule IF

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UR (user’s rating of document relevance) IS VR (very relevance) AND NY (number of years spent participating in the problem resolution of the organisation) IS L (low) AND NP (number of similar problem handled in the past) IS H (high) THEN document degree of relevance IS relevant (R). Fig. 6 shows 25 out the 75 rules that make up the rule base of the fuzzy inference engine. Out of the 75 possible outputs from the inference engine, only 38 outputs are considered necessary for generating document degree of relevance to the resolution of decision problems. These 38 outputs are obtained at the point where the output of the inference engine is either R or VR. For the purpose of illustration, consider the following set of inputs: Number of similar problem handled in the past (NP) = 3, Number of Year (NY) = 10, and user’s rating of the document (UR) is specified as SR, these inputs correspond to µ M(NP) = 1.0, µ VH(NY) = 1.0 and µ SR(UR) = 1.0, respectively. This is an example of situation where the inputs belong absolutely to a particular predicate of the input fuzzy set. Consider another example where NP = 4.75, NY=8.5years and UR = 3.7. During the resolution of crisp values to the fuzzy surface, NP value of 4.75 meets the condition of equations 4 and 5. For equation 4 the µ H(NP) = 0.25 and for equation 5, µ VH(NP) = 0.75. Fuzzy maximum operator is applied to these two results to produce the membership function with higher degree of µ(NP). This implies that the users’ number of similar problem handled NP is VH (very high). Also, NY = 8.5 meets the condition of equations 9 and 10. For equation 9, the µ H(NY) = 0.75 and for equation 10, µ VH(NY) = 0.25. Fuzzy maximum is operator is also applied to these two results to produce the membership function with higher degree of µ(NY). This implies that, the user’s knowledge in terms of years spent in the establishment is H. Similarly, UR value of 3.7 meets the condition of equations 13 and 14. For equation 13 the µ R(UR) = 0.30 and for equation 14, µ VR(UR) = 0.70. Fuzzy maximum operator is applied to these two results to produce the membership function with higher degree of µ(UR). This implies that the users’ rating of document relevance UR is VR (very relevance). Looking through the 75 rules, the only rule that satisfies the condition “NP is VH and NY is H and UR is VR” is rule number 69 which produce VR (very relevant) as the output. In fuzzy logic system, composition is the process of calculating the membership values into the finalised rule input. Different basic logic operators involve different calculation methods. For example, using the rule above with the crisp input values NP = 4.75, NY = 8.5, and UR = 3.7 three values (0.75, 0.75, 0.70) are calculated from the input fuzzy sets. According to the rule, the operator AND is used to connect the three inputs, therefore the smallest value, 0.7 is the result of the composition. Rule firing is the process of determining which fuzzy rule is to be activated. The activated rules are fired and selected out for further analysis. There are many rules in the fuzzy system but only some rules are fired subject to the activating conditions. The term implication in the context of fuzzy logic refers to the THEN part calculation of the rules using the results of rule composition with different implication operators such as the Mamdani operator, the Larsen operator and the Lukasiewicz operator [14]. These three operators are the most commonly used. Implication operators can be classified into two groups: one group is for generating the directly proportional result that the higher composition result can have a higher value of implication result, e.g. the Mamdani operator and Larsen operator, whereas the other group can generate an inversely proportional result, e.g. the Lukasiewicz operator. They are used in different cases with different characteristics. Aggregation is the method for fusing all implication results into the final result. It is the last procedure in the fuzzy inference engine. All the implication results are processed to form the output fuzzy set by an aggregation operator. Different implication operators match with different aggregation operators such as union and intersection. The union operator is used by Mamdani and Larsen operators while intersection is used by the Lukasiewicz operator [14]. The principle of Mamdani operator is used in the system that implemented the fuzzy logic model presented in this work. Defuzzification is the final stage in the fuzzy system. Crisp values or linguistic values can be obtained through the process of defuzzification. The expected output from the DDR model is linguistic values with their corresponding crisp values. These linguistic values correspond to the degree of usefulness of a document to the resolution of a decision problem determined by the system based on the level of the experience of the users and users’ specified degree of relevance of document to a problem. Each crisp value output of the system corresponds to a particular linguistic value, for example a value of 2 corresponds to NR (not relevant) and a value of 8 corresponds to VR (very relevant). It is possible for the output crisp values to

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belong to more than one linguistic variable. There are many defuzzification methods to resolve such to the appropriate linguistic variable. These defuzzification methods include: centre of area (centroid), maximum possibility, mean of maximum possibilities, and centre of mass of highest intersected region. Normally the most suitable method is used subject to the conditions of operation. Centre of area and mean of maximum possibilities are the most commonly used techniques due to their simplicity and ease of use. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25.

if if if if if if if if if if if if if if if if if if if if if if if if if

UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR UR

is is is is is is is is is is is is is is is is is is is is is is is is is

SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR SR

and and and and and and and and and and and and and and and and and and and and and and and and and

NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY NY

is is is is is is is is is is is is is is is is is is is is is is is is is

TR and NP is VS THEN DR is NR TR and NP is S THEN DR is NR TR and NP is M THEN DR is NR TR and NP is H THEN DR is SR TR and NP is VH THEN DR is SR L and NP is VS THEN DR is NR L and NP is S THEN DR is NR L and NP is M THEN DR is SR L and NP is H THEN DR is SR L and NP is VH THEN DR is SR M and NP is VS THEN DR is NR M and NP is S THEN DR is SR M and NP is M THEN DR is SR M and NP is H THEN DR is SR M and NP is VH THEN DR is SR H and NP is VS THEN DR is NR H and NP is S THEN DR is SR H and NP is M THEN DR is SR H and NP is H THEN DR is SR H and NP is VH THEN DR is SR VH and NP is VS THEN DR is NR VH and NP is S THEN DR is SR VH and NP is M THEN DR is SR VH and NP is H THEN DR is SR VH and NP is VH THEN DR is SR

Fig. 6. The rule base of the fuzzy inference engine

3 Results and Discussion Data for modelling the system was obtained through questionnaire and interview. The respondents were 20 postgraduate students in the faculty of Technology of the Obafemi Awolowo University, Ile-Ife, Nigeria. Information obtained include the title of their thesis, thesis stage (e.g. concept, qualifying or defence), number of year spent on the programme, their rating of usefulness of selected paper to the thesis work. Table 1 shows sample data extracted from the questionnaires. The date was used to build the fuzzy logic model. The first column of the table contains the identity assigned to the respondent, the second column shows the identity of each of the documents. The third, fourth and fifth columns show the NP, NY and UR respectively. The output of the fuzzy system determines whether a document would be considered for indexing into the document usage. The model has been implemented in a competitive intelligence based decision support system presented in [15]. The model formed the bases for determining which documents are used to crease the document usage index of the system. The document usage index created was not just based on the user’s rating of the document as being relevant or not relevant, rather it is based on both user’s rating as well as user’s experience. The experience is also based on the number of years someone has used in the establishment and the number of similar problems handled by the user in the past.

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Table 1. Data for building the model Respondents number 1 1 2 2 4 4 5 5 6 6

Document number 1 5 1 5 1 5 1 5 1 5

Number of similar problem 4 4 4 4 5 5 3 3 3 3

Number of year

Users’ rating

5 6 5 6 6 6 4 4 2 2

VR R VR SR VR VR R R VR R

4 Conclusion The document degree of relevance in the resolution of decision problem has been modelled based on the fuzzy logic system. The model has been used in the creation of document usage index of a decision support system which assists the user in the creation and exploration of the document usage index. The result from the decision support system shows that the document usage index created based on the DDR model presented is useful in the preservation of the effort of the users in discovering relevant documents for the resolution of decision problems. The result obtained shows that representing documents in terms of their usage can enhance the quality of information search results. This is as a result of the fact that documents that would have been hitherto considered not relevant to user query were found to be ranked very relevant based on previous usages.

Competing Interests Author has declared that no competing interests exist.

References [1]

Wang P, Soergel D. A cognitive model of document use during a research project. Study I. Document Selection. Journal of the American Society for Information Science. 1998;49(2):115-133.

[2]

Zaidi-Chtourou S, Bouzidi L. Information Usefulness in an Information System: Performance at the Strategic Level of the Organisation. Communications of IBIMA; 2010. DOI: 10.5171/2010.613429 Available: http://www.ibimapublishing.com/journals/CIBIMA/2 010/613429/6 13429.pdf (Retrieved 10/08/2012)

[3]

Akanbi LA, Adagunodo ER, David A. Integrating document usage with document index in competitive intelligence process. International Journal of Computer Applications. 2015;132(13): 37-43.

[4]

David A. Relevance information in economic intelligence. In information science (Papy F, ed). ISTE Ltd, London and John Willey & Son New York; 2009.

[5]

Buckland MK. Information as thing. Journal of the American Society of Information Science. 1991;42(5):351-360.

12

Akanbi; BJMCS, 15(5): 1-13, 2016; Article no.BJMCS.24576

[6]

Buckland MK. What is a “document”? Journal of the American Society of Information Science. 1997;48(9):804-809.

[7]

Briet S. Qu’est-ce que la documentation. Edit, Paris. Éditions Documentaires. French; 1951.

[8]

Zacklad M. Documentarisation processes in documents for action (DofA): The status of annotations and associated cooperation technologies. Computer Supported Cooperative Work (CSCW). 2006;15(2-3):205-228.

[9]

Durkin J. Expert system design and development. Macmillan Publishing Company, New York; 1994.

[10]

Zadeh LA. Fuzzy sets. Information and Control. 1965;8:338-353.

[11]

Fullér R. Neural fuzzy systems. In advances in soft computing series. Springer-Verlag, Berlin/Heidelberg; 2000.

[12]

Jeng B, Jeng Y, Liang T. Film: A fuzzy inductive learning method for automated knowledge acquisition. Decision Support Systems. 1997;21:61-73.

[13]

Baykasoglu A, Subulan K, Karaslan FS. A new fuzzy linear assignment method for multi-attribute decision making with an application to spare parts inventory classification. Applied Soft Computing. 2016;42:1–17.

[14]

Leung RWK, Lau HCW, Kwong CK. Article on a responsive replenishment system: A fuzzy logic approach. Expert Systems. 2003;20(1):20-32.

[15]

Akanbi LA, Afolabi BS, David A, Adagunodo ER. CIDUCE: A competitive intelligence tool for the creation and exploration of document usage. African Journal of Computing & ICTs. 2015;8(4): 119-134. _______________________________________________________________________________________

© 2016 Akanbi; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Peer-review history: The peer review history for this paper can be accessed here (Please copy paste the total link in your browser address bar) http://sciencedomain.org/review-history/13926

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