Systematic knowledge management and knowledge discovery

Bulletin of the American Society for Information Science, Oct/Nov 2000 by Jurisica, Igor

The volume and complexity of relevant information is ever increasing, and we need to handle it effectively. If we fail, we will not find what we need when we need it. To support this process, it is beneficial to extend our notion of information systems. In general, data are values for observable, measurable or calculable attributes. Data in context is information. Knowledge is validated and actionable information. The trend is to support knowledge management and facilitate knowledge sharing by building computerized information systems with richer and more flexible representation structures, supplemented by new services such as cooperative query processing, similarity-based retrieval and knowledge discovery. This trend also includes support for knowledge representation schema evolution, integration and coexistence of unstructured, semi-structured, structured and hyper-- structured information.

Although many approaches to knowledge organization are available, it is a challenge to organize evolving domains, since relying only on humans to create relationships among individual knowledge sources is not sufficient. It is not scalable, and it may be subjective. In order to support systematic knowledge management we need to complement traditional knowledge management techniques with approaches that automate parts of the process. Tools for information quality control help us to find missing, unexpected, incorrect or incomplete information. In addition, introducing knowledge-discovery systems helps to automate organizing, utilizing and evolving large knowledge repositories.

Research in the area of data warehouses and organizational and business knowledge management has generated important results. However, there are several reasons why traditional techniques for managing information are inadequate for knowledge management. Knowledge management systems support representation, organization, acquisition, creation, usage and evolution of knowledge in its many forms. But knowledge is complex, and we want to support knowledge management in many domains that are characterized by complex data and information, many unknowns, lack of complete theories and rapid knowledge evolution as a result of scientific discoveries. Human experts also need to be considered. When the theory is lacking, much of the reasoning process is based on experience. Experts remember positive cases for possible reuse of solutions and can recall negative experiences for avoiding potentially unsuccessful results. Thus, storing and reasoning with experiences may facilitate efficient and effective knowledge management in highly evolving domains.

Knowledge Management Systems

In order to support efficient and effective knowledge management, we must organize computer-- represented knowledge into structures that are semantically meaningful and computationally efficient. The meaning of information is captured by conceptual information models, which offer semantic terms for modeling applications and structuring information. In general, the models comprise the following:

Primitive concepts that describe an application, e.g., entity, activity, agent and goal;

Abstraction mechanisms that are used to organize the information, e.g., generalization, aggregation and classification; and

Operations that can access, update and process information, i.e., they provide knowledge management operations.

Defining a conceptual model requires making assumptions about the application to be modeled. For example, if the application consists of interrelated entities, such as patient, ailment, treatment, etc., then we need to include terms such as entity and relationship into the conceptual model. In addition, the semantics of these concepts and their relationship is used to define knowledge management operations, such as navigation, search, retrieval, update and reasoning, Selection of appropriate concepts for modeling the world in which the system must support the operations is referred to as ontology. We can identify four broad groups of ontologies: static, dynamic, intentional and social.

Abstraction mechanisms support organization of knowledge and its effective use. As a result, knowledge management operations can be performed more efficiently. Abstraction involves suppression of irrelevant detail. The relevancy depends on the task and the use of information, and thus it changes with the context. There are six main abstraction mechanisms: classification, generalization, aggregation, contextualization, materialization and normalization.

Knowledge management support in complex and dynamic domains benefits from extending traditional approaches with automated methods. The next section describes knowledge-- discovery techniques that are useful in determining conceptual models and can help with information system optimization and domain evolution.

Knowledge Discovery

The process of finding useful patterns in data has been referred to as data mining, knowledge extraction, information discovery, information harvesting, data archaeology and data pattern processing. The phrase knowledge discovery was coined at the first Knowledge Discovery in Databases workshop in 1989. Although it has similar goals to data mining, knowledge discovery emphasizes the end product of the process, which is knowledge. Thus, the pattern should be novel and in the form that the human users will be able to understand and use it. Knowledge discovery (KD) usually employs statistical data analysis methods but also methods in pattern recognition and artificial intelligence (AI). Database management systems (DB) ensure that the process is scalable (see Figure 1).