Effectively capturing expertise from several heterogenous sources in a social environment is the goal of the Collaborative Knowledge Acquisition field of study, a spin-off of the Knowledge Acquisition domain. A succinct description is presented next.
The Knowledge Acquisition (KA) field deals with the process of extracting, structuring, and organising knowledge from human experts so that the problem-solving expertise can be captured and transformed into a computer-readable form. This captured knowledge forms the basis for the reasoning process of an expert system and has three main concerns: (i) involvement of appropriate human experts, (ii) proper knowledge elicitation techniques and (iii) a structured acquisition approach [Wat86][Lio92b]. The term comes from the field of Expert Systems as the task of gathering the required knowledge from human experts, turning it into a computable form and fuelling the expert system. KA is a complex task with several identified issues that capturing techniques should address [Lio92b] [MD85]:
As such, KA is a difficult and time-consuming process that frequently creates a bottleneck for building expert systems. It is possible, applying the right tools and methodologies, to improve and mitigate this bottleneck.
In [Cor89], Cordingley provides a survey of knowledge acquisition methods and procedures, with suggestions about in which circumstances different methods are useful. These methods range from informal techniques such as user observation through common social science methods (interviews, questionnaires, and discourse analysis) to more formal techniques used in KA for expert systems. The reason for so many techniques lies in the fact that there are many different types of knowledge possessed by experts, and different techniques are required to access the different types of knowledge. This is referred to as the Differential Access Hypothesis [And04], and has been shown experimentally to have supporting evidence. Most recently, new developments in methodologies [SAA00], the emergence of ontologies, improved software tools, and the expansion of knowledge management [Dav98] beyond that of expert systems have brought new insights into KA.
Knowledge acquisition in a social environment shares the same issues as seen earlier. Additionally, the developer has to rely on distributed knowledge resources (artifacts and people) where not everyone is an expert. This becomes even worse if the community scope goes beyond the team of developers and extends to the web, where other developers may have the answer for a specific problem regarding a well-known shared software artifact, API or framework.
The quality of the retrieved knowledge is evaluated by the behaviour of the community towards that knowledge. If it is useful, it is used, if not, it is abandoned. One way of capturing this behaviour is to give the community ways of expressing their intent, whether through rating or commenting. Otherwise, there are ways of implicitly capturing the com- munity behaviour, like page hits1) or social bookmarking. This is known as Collaborative Knowledge Acquisition [Lio92a], as it gathers information from several heterogeneous sources, such is the morphology of the Internet.
Systems that enable this kind of knowledge acquisition are denominated Collective Knowledge Systems. The DRIVER environment and toolset can be characterised as such a system.