Knowledge Acquisitioning

Definition The transfer and transformation of potential problem solving expertise from some knowledge source to a program

How Done?? Could be achieved by a computer program that creates associations using a large body of case data Knowledge Elicitation: Series of interviews between the domain expert and the knowledge engineer Interaction between a domain expert and a computer program

Poor Productivity Knowledge Elicitation results in poor productivity ( 2-5 rules per day) because of: –The knowledge engineer has to learn something about the domain before communicating with the domain expert –Experts think less in terms of general principles, and more in terms of typical objects –Finding a good notation for expressing domain knowledge is a very difficult

Stages of Knowledge Acquisition Identification : –Identify the class of problems that the system is expected to solve, and the criteria that solutions must meet Conceptualization: –Uncover the key concepts and the relationships between them, which include: Data characteristics, flow of information and the domain structure.

Cont … Formalization: –Understand the nature of the search space, and the character of the search that will be conducted. –Important issues: completeness and certainty of information Implementation: –Turning the formalization of knowledge to an executable program –Important issues: Data Structures, modules independency Testing: –Evaluation by running the program on a large and representative sample of test cases –Possible errors are missing, incomplete or incorrect rules

KADS KADS is framework for a modeling approach of knowledge engineering. Knowledge-Based systems are not just containers of knowledge They are operational model that exhibits some desired behavior and impacts real-world phenomena Knowledge elicitation is not just eliciting domain knowledge but also interpreting this data with respect of some conceptual framework, and formalize it in such a way that the program can actually use the knowledge

KADS Principles Introduction of multiple models to cope with knowledge engineering complexity KADS four-layer framework for modeling the required expertise The reusability of generic model components The process of differentiating simple models into more complex ones The importance of structure-perceiving transformation of models of expertise into design and implementation

Cont … The motivation of KADS is to manage complexity. The first principle provides multiple models to help knowledge engineering in facing some issues: –Defining the problem that the expert system is meant to solve –Defining the function of the expert system –Defining the tasks that must be performed to fulfill the expert system ’ s function

KADS Models Organizational model of the environment in which the system will function Application model of the problem to be solved Task model which shows how the function will be fulfilled by breaking the desired behavior down into component tasks Model of cooperation or communication which is responsible for decomposing the problem solving behavior into primitive tasks and then distributing the tasks across agents Model of expertise which is the analysis of different kind of knowledge Design Model that suggests computational techniques and representation mechanisms

Previous KADS Version models Knowledge conceptualization –Formal description of knowledge using concepts and conceptual relations Epistemological analysis –Uncover properties of the conceptual knowledge Logical analysis –Knowledge about how to perform reasoning of tasks Implementation analysis –Deals with mechanisms upon which other levels of analysis are based

KADS four-layer framework It is used in differentiating between different kinds of knowledge according the roles they play in problem solving Knowledge category: –Strategic: controls execution of tasks –Task: what tasks to perform –Inference: how to reason in a domain –Domain: concepts, property and relations

Ontological analysis This approach describes systems in terms of entities, relations between them and transformations between entities Ontological analysis assumes that the problem can be reduced to a search problem, but doesn ’ t focus upon the method of search ( OPAL System ) These analyses are abstract, but they are valuable in structuring an ill-structured task such as the hard task of finding a suitable framework for organizing the knowledge in the process of knowledge elicitation

Cont … Main categories for structuring domain knowledge: –Static ontology: consist of domain entities with their properties and relations –Dynamic ontology: defines the states in problem solving, and how one state can be transformed to another –Epistemic ontology: knowledge that guides and constraints state transformations