Based on the current theory and method of related work, we define the main concepts related to URDCM as follows:

Definition1: Behavior refers to a certain operation of a user participating in knowledge organization.

According to the impact of user behavior on the knowledge organization structure, user behavior includes direct and indirect behavior.

Direct behavior: refers to the behavior except consumption behavior that has direct impact on knowledge organization structure, including new-built, modification, deletion and evaluation of knowledge.

Indirect behavior: refers to the consumption behavior, which has no impact on the knowledge organization structure, such as the user recommendation, extraction, sharing, discussion, communication and answering behavior of knowledge.

Definition 2: Score behavior refers to an operation when a user scores a direct behavior except his own score behavior according to the rules, score value is eand represents the reliability (the right degree) of the direct behavior, eis expressed with fuzzy number because of the fuzziness and randomness of user behavior, e ϵ.

Definition 3: Reliability evaluation level refers to evaluation reliability level of a user or a behavior in the open knowledge organization, they could be different.

Definition 4: Let a set of behavior A={a₁, a₂,..a_i,...a_l}, the corresponding weight set w={w₁,w₂,..w_i,...w_l}. Behavior constraint is the value range of i^th type a_i of k level user, such as behavior scale range and evaluation value range. constraint range is , , z is determined by experts, the same behavior type with different user level may have different constraint.

Definition 5: Group is made of the users participating in knowledge organization in same domain. In this paper, when group and user are presented at the same scene, they belong to the same domain except when a special explanation is provided.

Definition 6: Decision attribute is the index of quantifying user behavior and computing reliability.

According to the characteristic of open knowledge organization, the key point of URDCM is to choose some reasonable decision attributes. Three factors have main influence on the user reliability as follows:

1. User direct behavior: User direct behavior is the basis of open knowledge organization, and reflects the knowledge abilities and the main perspective of the user about domain knowledge. The direct behavior quality is one of the important factors influencing user behavior reliability.
2. Consumption behavior: Consumption behavior is the supplement of direct behavior, which implies the trust assumption among users. Consumption behavior includes group consumption behavior and user consumption behavior. User consumption behavior promotes the improvement of own knowledge ability, which has a positive effect on own behavior reliability at the same time. The higher the group consumption scale on a behavior, the greater the group trust on the behavior.
3. Time factor: A user could have different positivity at different time interval, that is, the behavior scale of user participation in knowledge organization may be different. When a user does not participate or seldom participates in knowledge organization at a time interval, the user positivity is very low, the group will reduce its trust on the user. Therefore, the time factor is an important factor to affect user reliability.

According to the above analysis, this paper will discuss how to qualify user reliability from the decision attributes of user direct reliability, bonus-penalty factor, group consumption factor and user consumption factor combined with time factor.

Definition 7: User direct reliability (UDR) in a domain is integrated by the reliability of user direct behavior a_i,... a_m in the domain.

Definition 8: User reliability in a domain will be gained by integrating user direct reliability, bonus-penalty factor, group consumption factor and user consumption factor in the domain.

It aims at verifying the sensibility of a_i group score reliability on low-quality score behavior during continuous time intervals when group useful score scale is introduced into computing UDR, comparing with group comprehensive score.

In the experiment, group participating in scores are ordinary users and have same reliability, m is group scale, a_i group scores are randomly generated in five continuous time intervals, and the detail is following:

t ₀~t₁:{1.0000,0.1233,0.0134,0.3697,0.6986,0.8893,0.5938,0.1567,0.8214,0.9501}

t₁~t₂:{0.0084,0.3969,0.6499, 0.8913,0.7688}

t₂~t₃:{ 0.9355,0.7138,0.9776,0.6371, 0.9169,0.2376}

t₃~ t₄:{0.9697,0.7148,0.782, 0.9000}

t₄~ t₅:{0.8481,0.8021,0.6683,0.671,0.8206, 0.7621, 0.5000 }

Table 1

Reliability level of behavior.

Table 2

User level, reliability range and behavior scale constrains.

According to the computational method of a_i group score reliability, at timet, a_i group score reliability Ge_i^' and group comprehensive score Ge_i^' in continuous interval times is shown in Table 3.

Table 3

a_i group score reliability and group comprehensive score at time t.

From Table 3, with group score scale increasing, group are coming to common opinion and a_i reliability shows the characteristic of growth trend, Ge_i^' ≥ Ge_i and Ge_i^', removes part of low quality score.

With score scale increasing, the values of parameter h and n ₀ are different, the value of n_' is different, so the number of Ge_i^' removing low quality score is different If, n^' / n < n₀ this indicates that group scores are disperse, and the majority of group scores are unusual and will be removed, if so, only represents the opinions of minority of group, Ge_i^' so is degraded to Ge_i. For example, Ge_i^'(h=0.35, n₀ =0.6 ) at time t₁, Ge_i^' (h=0.3, n₀ =0.5 ) at time t₁ and t₂, Ge_i^' (h=0.3, n₀ =0.6 ) at time t₁, t₂ and t₃, Ge_i^' is equal to Ge_i, If Ge_i^' represents the opinions of majority of group, and few scores of group are removed. Therefore, Ge_i^' can more efficiently represent the influence of group score behavior on a_i than that of Ge_i^'.

Generally, the larger the threshold h and the smaller the n₀, the better Ge_i^' can represent the opinions of the majority of group. However, if h is equal to 1 and ₀ is equal to 0, Ge_i^' will represent the opinions of all users of group and unusual  scores cannot be removed; if the smaller is threshold h and the larger is n₀, Ge_i^' may only represent the opinions of minority of group. In two cases, is degraded to, therefore, h and value need to be determined according to the actual demand.

It aims at verifying the influence of uncertain behavior on user SBR in continuous time intervals when uncertain behavior reliability is introduced to user reliability computing, compared with pass rate (PR) of user reliable behavior.

In the experiment, suppose the number of user certain behavior is unchangeable in continuous time intervals, and the number of user uncertain behavior is increasing. At time t, let certain direct behavior scale be n₁, and n₃ is certain direct behavior approved by the group. Uncertain behavior scale is n₂. Suppose during following five continuous time intervals, n₁and n₃stay unchangeable, that is, there is no behavior achieving common opinion by the group, based on which, the influence of uncertain behavior on user reliability is simulated. Let parameter n=30, n₃=20 and n₂ =6 , then s₁ =0.8333. The behavior set A= {a₁, a₂, …, a_n} is the common score object of user u and group, the score sets of user u and group are {e_{j 0}} and {Ga _j} respectively, and randomly generated. respectively, and randomly generated. The detail is shown in Table 4.

Table 4

Uncertain behavior score of user u and group.

Because user uncertain behavior reliability varies in differentvalue of parameter a, the experiment chooses five different values for parameter a, that is 0.1, 0.2, 0.3, 0.4 and 0.5. The reliabilities of uncertain behavior s₂ and UDR s are shown in Tables 5, 6 respectively.

Table 5

User u uncertain reliability at time t.

From Table 6, the scale of uncertain behavior is increasing, and PR and s value are  descending, but the s value is greater than the value R at time t₁~t₅. It is known that group has not come to an agreement on behavior from the Table 5, but the highly consistent uncertain behavior score between user u and group exists at five continuous time intervals. For example, at time intervals t~t₁, the uncertain behavior score is more consistent between user u and group, s₂ >0.5, and uncertain behavior influence on user u reliabilityis considered in computing s value and fails   to  take into account  the computing PR value. So if there exists highly consistent uncertain behavior between user u and group, then s> PR, otherwises=PR.

Table 6

User u direct reliability at time t.

In a word, uncertain behavior reliability introduced into user reliability computing will more fully represent user behavior reliability, especially if p=0, then s=s₂, PR=0.

According to Table 5, at time t, uncertain behavior reliability s₂ of user u is increasing with the increase in parameter a, because parameter a decides the maximum score distance d of between user u and group whether a behavior is reliable, the smaller parameter a valueis, the smaller d value is. So parameter a value is determined to meet the open knowledge organization demand.

The experiment aims at verifying the influence of user comprehensive reliability during continuous time intervals after the bonus-penalty factor is introduced into URDCM. The experiment will observe the influence of user direct behavior scale on UDR in continuous interval times.

Let parameter b₁=b₂ =0.1,a =0.3, u = 0.1 the data is the same as experiment two, and the UDR s value is equal to s (a=0.3) . The detail is shown in Table 7.

From Table 7 it can be seen that user reliability is different at time t₁, t₂, t₃, t₄ and t₅, user level evolves from ordinary user to excellent user and then excellent degenerates into advanced user, and advanced user degenerates into ordinary user.

Table 7

User Reliability introducing the bonus-penalty factor.

Different levels of users have different positivity constrains. According to Table 1, at time intervals t₂ ~t₃ and t₃ ~ t₄, user positivity is lower, therefore, at time t₃ and t₄, <0, user reliability is modified through the bonus-penalty factor, and C^t_u< s , and the level of user u is degenerated from advanced user into ordinary user.

Although user positivity is normal and user direct behavior has no influence on the user reliability at time interval C_u^tt₄ ~ t₅, it is still affected by user positivity at time intervals t₂ ~t₃ and t₃ ~ t₄, and revised by the bonus-penalty factor ε. Similarly, if ε >0, C^t_u is affected by user positivity too, and C_u^t ≥ s. So comparing with PR, the method can accurately and dynamically reflect the changes of user behavior positivity in the process of open knowledge organization and then better improve the enthusiasm of user participating in knowledge organization.