Issues in Knowledge Acquisition
Some of the most important issues in knowledge acquisition are as follows:
Requirements for KA Techniques
Because of these issues, techniques are required which:
Many techniques have been developed to help elicit knowledge from an expert. These are referred to as knowledge elicitation or knowledge acquisition (KA) techniques. The term "KA techniques" is commonly used.
The following list gives a brief introduction to the types of techniques used for acquiring, analysing and modelling knowledge:
Differential Access Hypothesis
Why have so many techniques? The answer 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, and has been shown experimentally to have supporting evidence.
Comparison of KA Techniques
The figure below presents the various techniques described above and shows the types of knowledge they are mainly aimed at eliciting. The vertical axis on the figure represents the dimension from object knowledge to process knowledge, and the horizontal axis represents the dimension from explicit knowledge to tacit knowledge.
Typical Use of KA Techniques
How and when are the many techniques described above used in a knowledge acquisition project? To illustrate the general process, a simple method will be described. This method starts with the use of natural techniques, then moves to using more contrived techniques. It is summarised as follows.
This is a very brief coverage of what happens. It does not assume any previous knowledge has been gathered, nor that any generic knowledge can be applied. In reality, the aim would be to re-use as much previously acquired knowledge as possible. Techniques have been developed to assist this, such as the use of ontologies and problem-solving models. These provide generic knowledge to suggest ideas to the expert such as general classes of objects in the domain and general ways in which tasks are performed. This re-use of knowledge is the essence of making the knowledge acquisition process as efficient and effective as possible. This is an evolving process. Hence, as more knowledge is gathered and abstracted to produce generic knowledge, the whole process becomes more efficient. In practice, knowledge engineers often mix this theory-driven (top-down) approach with a data-driven (bottom-up) approach (discussed later).
A number of recent developments are continuing to improve the efficiency of the knowledge acquisition process. Four of these developments are examined below.
First, methodologies have been introduced that provide frameworks and generic knowledge to help guide knowledge acquisition activities and ensure the development of each expert system is performed in an efficient manner. A leading methodology is CommonKADS. At the heart of CommonKADS is the notion that knowledge engineering projects should be model-driven. At the level of project management, CommonKADS advises the use of six high-level models: the organisation model, the task model, the agent model, the expertise model, the communications model and the design model. To aid development of these models, a number of generic models of problem-solving activities are included. Each of these generic models describe the roles that knowledge play in the tasks, hence provide guidance on what types of knowledge to focus upon. As a project proceeds, CommonKADS follows a spiral approach to system development such that phases of reviewing, risk assessment, planning and monitoring are visited and re-visited. This provides for rapid prototyping of the system, such that risk is managed and there is more flexibility in dealing with uncertainty and change.
A second important development is the creation and use of ontologies. Although there is a lack of unanimity in the exact definition of the term ontology, it is generally regarded as a formalised representation of the knowledge in a domain taken from a particular perspective or conceptualisation. The main use of an ontology is to share and communicate knowledge, both between people and between computer systems. A number of generic ontologies have been constructed, each having application across a number of domains which enables the re-use of knowledge. In this way, a project need not start with a blank sheet of paper, but with a number of skeletal frameworks that can act as predefined structures for the knowledge being acquired. As with the problem-solving models of CommonKADS, ontologies also provide guidance to the knowledge engineer in the types of knowledge to be investigated.
A third development has been an increasing use of software tools to aid the acquisition process. Software packages, such as PCPACK, contain a number of tools to help the knowledge engineer analyse, structure and store the knowledge required. The use of various modelling tools and a central database of knowledge can provide various representational views of the domain. Software tools can also enforce good knowledge engineering discipline on the user, so that even novice practitioners can be aided to perform knowledge acquisition projects. Software storage and indexing systems can also facilitate the re-use and transfer of knowledge from project to project. More recently, software systems that make use of generic ontologies are under development to provide for automatic analysis and structuring of knowledge.
A fourth recent development is the use of knowledge engineering principles and techniques in contexts other than the development of expert systems. A notable use of the technology in another field is as an aid to knowledge management within organisational contexts. Knowledge management is a strategy whereby the knowledge within an organisation is treated as a key asset to be managed in the most effective way possible. This approach has been a major influence in the past few years as companies recognise the vital need to manage their knowledge assets. A number of principles and techniques from knowledge engineering have been successfully transferred to aid in knowledge management initiatives, such as the construction of web sites for company intranet systems. This is an important precedent for the aim of this thesis to apply practices from knowledge engineering to the realm of personal knowledge.