SYNOPSIS

9.2.1 Summary of the Work Conducted

This research work has proposed and implemented a computational framework for extracting a community model based on VC tracking data and following TM, SMM and CCen. The exploitation of the CM enabled us to provide intelligent support for knowledge sharing to VCs.

In Chapter 1 we proposed three research questions. This research addressed these questions in the following way:

(i) How to extract a computational model to represent the functioning and evolution of the community as a whole by using semantically enhanced tracking data?

We have (a) formalised the input data to capture essential information about members, activities and resources, which will be represented in the CM; (b) developed algorithms to extract a CM based on tracking data and semantically enriched this data by using an ontology;

(ii) By using that model how can intelligent functionality be provided to support the development of TM, building of SMM and monitoring of CCen?

We have (c) developed graph-based algorithms to analyse the extracted CM and identify knowledge sharing patterns, both static and time-dependent; (d) employed the CM and the algorithms for detecting knowledge sharing patterns in a mechanism for generating notification messages aimed in supporting knowledge sharing in a VC.

(iii) How can intelligent support of the above processes affect the functioning of the community?

We ran an evaluation (e) in a real, active knowledge sharing VC. Results evidence that by supporting the development of TM, building of SMM and monitoring CCen in a VC it can be beneficial for knowledge sharing in a VC.

The rest of this section will discuss in more detail how the above questions have been addressed in this thesis.

(a) Input Formalisation (Chapter 4). Formalisation of the input took into account the simplicity and generality of the approach so it can be used in other knowledge sharing applications. Input data include, information about users (member Id, email, date joined the community), activity data (uploading/downloading), resources (name, keywords (tags), description, rating) and an ontology representing the VC domain.

(b) Community model extraction mechanism (Chapter 4). We have described a general model for VCs that consists of individual user models of the community members, several relationship graphs, a list of popular and peripheral topics, and a list of the cognitively central members. Generic community tracking data have been used to extract this model, together with an ontology used to extract semantic relationship graphs. The algorithms for extracting relationship graphs have been kept flexible and can be adjusted according to the input data at hand. A study with archival data from an existing VC was conducted. Patterns of community

behaviour were manually detected, and provided as the basis for automatic detection of community patterns and dynamic community-tailored support.

(c) Definition of knowledge sharing patterns (Chapter 5 and Chapter 6). Static knowledge sharing behaviour patterns in a VC have been defined, following selected processes (TM, SMM, CCen) important for the effective functioning of closely-knit communities. We have demonstrated with a study how these patterns can be detected and used to provide community-tailored support. Static knowledge sharing patterns can be useful in identifying problematic cases, especially during the start-up phase of a VC. Furthermore, the CM have been employed for detecting community change patterns to identify when intelligent support is needed to support a community to sustain. The results from a study conducted based on archival data show how pattern detection can be used to generate notifications that may help a VC to sustain.

(d) Generation of notification messages (Chapter 7). Adaptive notification generation mechanism has been defined aiming at supporting CCenM, CPerM, the development of TM and the establishment of SMM. The formalisation of adaptive notification mechanism defines why and how a notification is generated, according to detected knowledge sharing patterns.

The formalisation of the above aspects inform the generation of notifications that can be adapted in different closely-knit VCs. In this thesis, we have demonstrated how tracking data extracted from a widely used knowledge sharing system - BSCW - can be used in designing and extracting a CM and providing community-tailored support. Archival data from an existing VC have been used to validate the algorithms for extracting a CM and detecting knowledge sharing patterns.

(e) Experimental Study (Chapter 8). Following the framework defined in this thesis we have used tracking data of a real and active VC to validate the notification generation mechanism. Hence, we have extracted a CM, employed the algorithms to extract knowledge sharing patterns, and used the detections as input for generation of personalised notification messages to VC members. An experimental study has been performed to identify the effect of the notification mechanism, so it can be employed in supporting knowledge sharing in VCs. The results of the evaluation (Chapter 8) show that notification messages can have a positive effect on members (both newcomers and oldtimers). Two formats of notification messages (general and personalised) have been generated to VC members. The second message format (personalised information for each member pointing at relevant members and providing links to resources in the VC) was preferred by members. In both cases, members rated the notification messages as relevant to them. In general, notification messages can be used for motivating members to keep active in the VC and, in the case of newcomers, to upload and download resources. The confidence of members slightly increased after receiving notifications and a slow

development of TM and SMM was shown in members’ comments. Members reported that they were becoming aware of the resources and people available in the VC. Some newcomers and oldtimers increased their activity after receiving notification messages. Finally, the results show evidence that monitoring the CCen of members can be used to support the knowledge sharing in a closely knit VC. The evaluation also pointed out improvements and possible applications of the framework, which are discussed in Section 9.5.

9.2.2 Generality of the Proposed Approach

The generality of the approach presented in this thesis can be discussed following the main components of the proposed framework:

Input formalisation: The input data considered has been kept in a generic format to be in line with any conventional knowledge sharing system. The proposed data descriptions are easily applied to knowledge sharing systems that have members sharing and rating resources and providing keywords (tags) and/or some metadata associated with each resource. The resource metadata followed the Dublin Core13 metadata schema, which is a conventional standard for metadata description. The implementation of the algorithms included input data stored in a MySQL database. The tables can be directly used to store the same data format in any domain. The ontology developed reflects the topics of interest in the community. The ontology using for the algorithms in this thesis was built using Protégé14 and encoded in OWL. The ontology can be reused in any other system or exploited by algorithms that can reason through an OWL ontology. A WordNet similarity measure was employed as an input for the CM to be extracted. The algorithm, taken from a third party, has not been purpose built for this work. It has been extended to detect similarity between resource keywords. The algorithm and the proposed extension are written in Java, and can be used straightaway in any other Java based application.

Community model extraction mechanism: The community modelling extraction mechanism contains four parts, namely: individual user models, relationships model, peripheral/central topics, list of CCenM. Although the algorithms for extracting the CM depend on people sharing resources in the VC and the provided keywords of shared resource, they are generic and can be used by a different system that allows resource sharing. The general structure of the relationship graphs and the use of a relational database modelling approach for their representation, facilitate their implementation and easy integration in different systems. The algorithms used to extract the relationship graphs are depended on the tracking data extracted from BSCW that provided information about who has downloaded a resource and who has

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uploaded the resource. This does not affect the generality of the approach since the framework describes how similar relationship types can be defined and implemented when having different format of tracking data, using different programming languages and technologies. In the IUM, the interests of members have been extracted here according to keywords of the resources members have read and/or uploaded. Even if keywords are not available other representations (e.g. tags) can be used to represent the interests of a member. To extract the CCen of a member, the relationship types have been used. Having tracking data from a different platform, suitable relationship types can be defined and used for the extraction of the CCen.

Definition of knowledge sharing patterns: Graph-based patterns were employed in defining community knowledge sharing patterns. The definitions were kept generic; however they are strongly depended on the relationship types defined. The types considered in this thesis are applicable broadly to any closely-knit community for knowledge sharing. The graph-based pattern detection approach is applicable to other relationships detected in VCs. An exhaustive list of knowledge sharing patterns has not been provided, and is beyond the scope of this work. Knowledge sharing patterns can vary from one community to another. Using the graph-based approach presented in this research, further patterns can be defined.

Generation of notification messages: Similarly to the detection of knowledge sharing patterns, notifications have been formalised in a generic way. Although the generation of notifications is depended on the detection of specific patterns, the approach is general, given that suitable patterns have been defined relevant to the VC. The definitions of the notifications provide the foundations upon relevant notifications to be defined according to the specific community.