Knowledge Acquisiton

Knowledge Acquisition

Knowledge acquisition includes the elicitation, collection, analysis, modeling and validation of knowledge for knowledge engineering and knowledge management projects.
Issues in Knowledge Acquisition
Some of the most important issues in knowledge acquisition are as follows:
Most knowledge is in the heads of experts
Experts have vast amounts of knowledge
Experts have a lot of tacit knowledge
They don't know all that they know and use
Tacit knowledge is hard (impossible) to describe
Experts are very busy and valuable people
Each expert doesn't know everything
Knowledge has a "shelf life"

Requirements for KA Techniques
Because of these issues, techniques are required which:
Take experts off the job for short time periods
Allow non-experts to understand the knowledge
Focus on the essential knowledge
Can capture tacit knowledge
Allow knowledge to be collated from different experts
Allow knowledge to be validated and maintained

KA Techniques
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, analyzing and modeling knowledge:
Protocol-generation techniques include various types of interviews (unstructured, semi-structured and structured), reporting techniques (such as self-report and shadowing) and observational techniques
Protocol analysis techniques are used with transcripts of interviews or other text-based information to identify various types of knowledge, such as goals, decisions, relationships and attributes. This acts as a bridge between the use of protocol-based techniques and knowledge modeling techniques.
Hierarchy-generation techniques, such as laddering, are used to build taxonomies or other hierarchical structures such as goal trees and decision networks.
Matrix-based techniques involve the construction of grids indicating such things as problems encountered against possible solutions. Important types include the use of frames for representing the properties of concepts and the repertory grid technique used to elicit, rate, analyze and categories the properties of concepts.
Sorting techniques are used for capturing the way people compare and order concepts, and can lead to the revelation of knowledge about classes, properties and priorities.
Limited-information and constrained-processing tasks are techniques that either limit the time and/or information available to the expert when performing tasks. For instance, the twenty-questions technique provides an efficient way of accessing the key information in a domain in a prioritized order.
Diagram-based techniques include the generation and use of concept maps, state transition networks, event diagrams and process maps. The use of these is particularly important in capturing the "what, how, when, who and why" of tasks and events.


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 summarized as follows.
Conduct an initial interview with the expert in order to (a) scope what knowledge is to be acquired, (b) determine what purpose the knowledge is to be put, (c) gain some understanding of key terminology, and (d) build a rapport with the expert. This interview (as with all session with experts) is recorded on either audiotape or videotape.
Transcribe the initial interview and analyze the resulting protocol. Create a concept ladder of the resulting knowledge to provide a broad representation of the knowledge in the domain. Use the ladder to produce a set of questions which cover the essential issues across the domain and which serve the goals of the knowledge acquisition project.
Conduct a semi-structured interview with the expert using the pre-prepared questions to provide structure and focus.
Transcribe the semi-structured interview and analyse the resulting protocol for the knowledge types present. Typically these would be concepts, attributes, values, relationships, tasks and rules.
Represent these knowledge elements using the most appropriate knowledge models, e.g. ladders, grids, network diagrams, hypertext, etc. In addition, document anecdotes, illustrations and explanations in a structured manner using hypertext and template headings.
Use the resulting knowledge models and structured text with contrived techniques such as laddering, think aloud problem-solving, twenty questions and repertory grid to allow the expert to modify and expand on the knowledge already captured.
Repeat the analysis, model building and acquisition sessions until the expert and knowledge engineer are happy that the goals of the project have been realised.
Validate the knowledge acquired with other experts, and make modifications where necessary.
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).
Recent Developments
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 Common KADS. At the heart of Common KADS is the notion that knowledge engineering projects should be model-driven. At the level of project management, Common KADS advises the use of six high-level models: the organization 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, Common KADS 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 Common KADS, 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.