Knowledge Management   Knowledge Management (KM) is a field that began as a means to consciously collect, manage, and share the key knowledge of many people in a systemic manner. By bringing common ad hoc activities into a more systemic process, the goal is to provide both wider sharing of useful knowledge among large groups of people and to pinpoint the knowledge people need during their work to greatly improve their productivity and effectiveness.   Knowledge differs from information and data in a critical way. Data is transactional information which, when presented independently, provides little contribution to decision-making and action-taking. In contrast, information is content that is necessary for people to have in order to perform their jobs. Information is typically data that has been assembled in some meaningful way. Finally, knowledge is a step more value-added than information in that knowledge is content which contains user experiences around it. Knowledge provides insights that move the user of that knowledge to make decisions and take action.   The same text item can simultaneously be data, information, and knowledge depending on the situation's context and the person's experience and perspective. The distinction depends on prior knowledge, and task focus. For example, the phrase “Set the soldering iron to 200 degrees” can be information from manual for general use or knowledge from expert for specific manufacturing process. Similarly, the phrase “10000 units shipped yesterday” can be simple data for logistics, information for a shipping manager, or knowledge for competitor monitoring market share.   As with all large scale management fields, KM must overcome the greatest single challenge to effective management, namely, organizing a very large amount of continuously increasing information into a small enough group that a variety of different people find useful and easy to use while being up-to-date and accurate. A big part of this challenge can use the same good management practices of any large office environment to distribute tasks and use teams to define requirements. Yet, the remainder of the challenge requires activities and tool specific to the nature of KM.   Knowledge differs from simple information or data since it conveys the context, timeliness, confidence, and relationships among the individual pieces of information. In particular, the contextual and confidence nature of knowledge is the fuzzy difference between when words can change from being information for one person to knowledge for another. People "know" something when they believe confidently that they can use the knowledge in a specific context to make decisions or take actions.   Yet, this need for context and confidence is what makes using KM in practice so difficult since people "know" things at a personal level. Trying to understand and capture the personal views and needs of everyone in a group is extremely difficult, but doing that and then packaging and tagging the bits and pieces for others to find and use at a later date is almost impossible. KM as a management method therefore does not try to get all knowledge but only the key knowledge that is relevant and useful to the organization, most people, and in the most important activities. Even with this distillation and prioritization, it is a daunting challenge to package and tag knowledge into understandable, findable, and reusable units. Solving this challenge requires good housekeeping, like organizing a warehouse or even a kitchen pantry.   The way this is done is to define the way you want to organize things (e.g. by type, age, cost, etc) and the titles you will use to group like items (e.g. fruit, meat, dairy). The way to organize things is defined by an ontology which is a conceptual map of the main ideas and the relationships among the ideas. Once this conceptual map is made a set of titles can be created within each idea to be more specific. This structured set of titles constitutes the taxonomy. This paper describes the basics of ontologies and taxonomies for KM and how to develop and implement them.   Ontologies and taxonomies provide a structure to the concepts and language used to organize knowledge. Without them, the knowledge will inevitably be difficult to find and reuse as people have very different perspectives on how the knowledge is related in the context of their situations.   Ontologies specify the primary concepts and the relationships among the concepts in a particular domain. The term means several things depending on the field in which it is used. In philosophy, ontology is concerned with the metaphysical nature and relationships of being. In contrast, computer science uses ontologies to describe specific conceptual terms and relationships in a standardized machine readable format. Any KM effort must grapple with the challenge that there are several viable and valid perspectives on any given topic or business domain. To make the knowledge useful and an effective enabler of organizational success, the KM manager must create a single shared understanding among people of what the knowledge means to the organization within the context of its business domain and how it is intended to be used. An ontology provides this unifying map of concepts and relationships. The ontology can be represented either graphically or in a structured text format. The former is usually used when the primary goal is to forge a shared understanding of the domain and provide guidance to the members of the group. The latter approach is most often used for computer applications that perform language analysis and concept matching, such as the goal of greater automated semantic capabilities on the Internet (i.e. the Semantic Web).   Taxonomies are the classification scheme used to categorize a set of information items. They represent an agreed vocabulary of topics arranged around a particular theme. Although they can have either a hierarchical or non-hierarchical structure, we typically encounter hierarchical taxonomies such as in libraries, biology, or military organizations. This type has a tree-like structure with nodes branching into sub-nodes where each node represents a topic with a few descriptive words. For example, the familiar Dewey Decimal System was introduced in 1876 as a general catalog of knowledge and is the most common system used in libraries.   The need to classify information is not new. One of the first large organized cataloguing and classification projects was in the center of ancient knowledge at the library in Alexandria, Egypt. Its first bibliographer Callimachus compiled the Pinakes, a 120 volume subject catalog of all the library’s books. He is considered the founding father of librarians since he did not just list the books, but included the author, data on the text, and comments on authenticity to guide users [6]. However, many others throughout history solved the classification problem by strictly limiting the number of books by religious, political, or economic reasons, and then organizing the set by acquisition date, size, or other simple criteria. Thus, classifying information becomes more important as the number of items increases and people have more trouble remembering what they have and where to find it. This is now crucial as we buckle under the immense volume of information available to everyone by the electronic networking of the world. We have become the fabled man dying of thirst while at sea as we search for the one or two items that answer our needs from within this sea of information. Indeed, KM is specifically focused on not only giving people the right information, but going to the trouble of distilling it into validated contextually connected knowledge that fuses information and data from a variety of distinct topical areas. In order to classify information a framework must be defined. There are many existing standards from the Federal Government, consortia, and professional societies. For example, the Defense Technical Information Center (DTIC) has a technology taxonomy while the Standard Subject Identification Code (SSIC) is the standard for all DOD information including memorandums and records management. Similarly, the Library of Congress Classification (LOCC) is a commonly used general purpose system. However, taxonomies inevitably have a central theme that guides how the tree structure is arranged. For example, the LOCC and Dewey Decimal System are built from a perspective of classifying knowledge itself in a general purpose manner. Thus, the major LOCC headings include topics such as: Philosophy, Psychology, Religion; Auxiliary Sciences of History; History (General); and Fine Arts. In contrast, DTIC’s major headings are more focused on technical issues and include: Aviation; Agriculture; Chemistry; and Electrotechnology and Fluidics. Clearly, trying to find a technology issue will be easier with DTIC than LOCC.