**2.3 Virtual communities and knowledge evolution**

A virtual community (Schlichter et.al., 1998) is a group of users that are considered experts in one or more related topics. In this work the opinions from experts are important because they should have more impact than opinions from novices or occasional users.

Virtual communities of experts are constructed in terms of the knowledge tree. For each topic, the community of experts in this topic is composed of the authors of the crystallised documents on the topic, on the parent of the topic, on any of the children of the topic or on any of the sisters of the topic. There is a virtual community for each topic of the knowledge tree, and any successful author usually belongs to several related communities.

When it is started a knowledge area there is only a root node with the main topic. Probably, there will not be enough people and interactions to make the knowledge crystallisation mechanism credible. In relation with this bootstrapping problem, virtual communities have also proven to be handful. Virtual communities behave in a different way when they are just beginning. So, it is proposed a maturation process that involves several phases. Figure 3 shows this evolution.

At the beginning stages the user community work in a "supervised" mode. During this supervised phase there will be a steering committee in charge of proposing knowledge structures (initial refinements of the root node) and voting for them. The members of the steering committee are defined in the moment of creation of the knowledge area; new members can be added by consensus of the current members.

Knowledge Crystallisation Supported by the KnowCat System 191

Finally, an active community may reach the "Stable" phase. Many of the community members are not active any longer, so different rules should be applied to ensure some continuity of the crystallisation. Changes are rare, and most of the activity is consultation. Few new contributions arrive, and they will have much more difficulties to crystallise comparing to the previous phase. However, if activity raises to a minimum again, the node may switch to "Active" status, and engage in a new crystallisation

A central concept in this work is the "Knowledge Crystallisation" mechanism. With this mechanism it could be possible to have, in each moment, the best knowledge elements in a

The mentioned KnowCat knowledge elements –documents, notes, version documents, topics– are produced by the users and their lifetime depends on the patterns of their usage. Any of these elements will stay longer in the knowledge area if it is frequently used and receive favourable opinions from other users. In that case, its crystallisation degree will rise, and thus its probability to stay in the knowledge area. However, if one knowledge element is not used or it doesn't receive favourable opinions by the users, then it will eventually disappear from the knowledge area as a consequence of its crystallisation degree going

Firstly, it is shown in the next section the crystallisation of documents. Secondly, it is shown the crystallisation of annotations, and version documents. Finally, it is shown the

The Knowledge Crystallisation mechanism takes into account the users' opinions about the documents and the evolution of its received opinions in order to determinate which documents have enough acceptation during a determinate period of time. They will then

Each document has a value called "crystallisation degree" or "social acceptation degree"–or acceptation degree, for short–, which is a value between 0-1. A document "crystallise" when his acceptation degree stay for a period of time called "time for crystallising", e.g. 2 weeks,

 The explicit received opinions concerning the document are computed in the *ExplicitAcceptationDegree* value. These explicit opinions are: the received votes (ratings) and how theses votes have been received; and the received assessments, notes and their

 The implicit received opinions concerning the document are computed in the *ImplicitAcceptationDegree* value. These implicit opinions are the accesses to the

The acceptation degree, which is called as *AcceptationDegree*, of each document, *doci*, in a concrete moment ,*tj*, is calculated from the mentioned elements in the following

It is considered that the explicit opinions are more useful in order to determinate the acceptation of a document, because they are more elaborated opinions that implicit

opinions, so the *coefE* is higher than *coefI* (e.g. *coefE* = 0.9; *coefI* = 0.1).

phase.

**3. Knowledge crystallisation** 

crystallisation of the structure.

crystallise.

types.

way:

document.

**3.1 Documents' crystallisation process** 

over a determinate "crystallisation point" , e.g. 0.65. The acceptation degree of a document takes into account:

knowledge area, in opinion of the user community.

down. This mechanism is called Knowledge Crystallisation.

In this phase, descriptions (documents about some topic) may be added to the system both by the members of the steering committee and by other users that are considered as collaborators. However, only the members of the steering committee have the complete capability of voting on the documents, and thus in deciding which documents crystallise. Collaborators may have limited capability of voting, if the steering committee decides so.

#### Fig. 3. Knowledge evolution of a knowledge area

Eventually, the steering committee may decide to advance the area of knowledge to the "active" mode, possibly when a critical mass of participants and interactions is achieved. In this moment there should be a single tree structure for the area, decided by consensus. Then the steering committee is dissolved and the subsequent crystallisation of the knowledge is based on virtual communities.

During the "active" phase, when one user contribution crystallises, s/he receives a certain amount of "votes" that s/he may apply for the crystallisation of other documents (of other authors) in the virtual community where her/his crystallised document is located. As in the previous phase the descriptions may be added both by experts and collaborators; in fact all users start using the system as collaborators and when a document of a user crystallises s/he becomes an expert in related communities of the topic where the document is located.

The other aspect of knowledge crystallisation is the evolution of the structure of the knowledge tree. If a member of a virtual community proposes to add a new subject to a topic, remove a subject from a topic or move a subject from one topic to another topic, then a minimum quorum of positive votes from other members of the community will be necessary for the change to be made.

Finally, an active community may reach the "Stable" phase. Many of the community members are not active any longer, so different rules should be applied to ensure some continuity of the crystallisation. Changes are rare, and most of the activity is consultation. Few new contributions arrive, and they will have much more difficulties to crystallise comparing to the previous phase. However, if activity raises to a minimum again, the node may switch to "Active" status, and engage in a new crystallisation phase.
