Inference Engine An Inference Engine is a tool from Artificial Intelligence. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applied logical rules to the knowledge base and deduced new knowledge. Architecture[edit] The logic that an inference engine uses is typically represented as IF-THEN rules. A simple example of Modus Ponens often used in introductory logic books is "If you are human then you are mortal". Rule1: Human(x) => Mortal(x) A trivial example of how this rule would be used in an inference engine is as follows. This innovation of integrating the inference engine with a user interface led to the second early advancement of expert systems: explanation capabilities. An inference engine cycles through three sequential steps: match rules, select rules, and execute rules. Implementations[edit] See also[edit] References[edit]
Knowledge Base A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems which were the first knowledge-based systems. The original use of the term knowledge-base was to describe one of the two sub-systems of a knowledge-based system. A knowledge-based system consists of a knowledge-base that represents facts about the world and an inference engine that can reason about those facts and use rules and other forms of logic to deduce new facts or highlight inconsistencies.[1] The term 'knowledge-base' was to distinguish from the more common widely used term database. Flat data. Early expert systems also had little need for multiple users or the complexity that comes with requiring transactional properties on data. The volume requirements were also different for a knowledge-base compared to a conventional database. See also[edit] Notes[edit]
Knowledge-Based Systems Knowledge-Based systems were first developed by Artificial Intelligence researchers. These early knowledge-based systems were primarily expert systems. In fact the term is often used synonymously with expert systems. The difference is in the view taken to describe the system. The first knowledge-based systems were rule based expert systems. Acquisition & Maintenance. As knowledge-based systems became more complex the techniques used to represent the knowledge base became more sophisticated. Another advancement was the development of special purpose automated reasoning systems called classifiers. The most recent advancement of knowledge-based systems has been to adopt the technologies for the development of systems that use the Internet. See also[edit] References[edit] External links[edit] Akerkar RA and Sajja Priti Srinivas (2009).
Knowledge Modeling Knowledge modeling is a process of creating a computer interpretable model of knowledge or standard specifications about a kind of process and/or about a kind of facility or product. The resulting knowledge model can only be computer interpretable when it is expressed in some knowledge representation language or data structure that enables the knowledge to be interpreted by software and to be stored in a database or data exchange file.Knowledge-based engineering or knowledge-aided design is a process of computer-aided usage of such knowledge models for the design of products, facilities or processes. The design of products or facilities then uses the knowledge model to guide the creation of the facility or product that need to be designed. In other words it used knowledge about a kind of object to create a product model of an (imaginary) individual object. Similarly, a knowledge model of a process is basically a specification of the sequence of process stages.
Legal Case Management The terms Legal case management (LCM) or matter management refer to a subset of law practice management and cover a range of approaches and technologies used by law firms and courts to leverage knowledge and methodologies for managing the life cycle of a case or matter more effectively.[1][2] Generally, the terms refer to the sophisticated information management and workflow practices that are tailored to meet the legal field's specific needs and requirements. As attorneys and law firms compete for clients they are routinely challenged to deliver services at lower costs with greater efficiency, thus firms develop practice-specific processes and utilize contemporary technologies to assist in meeting such challenges. Law practice management processes and technologies include case and matter management, time and billing, litigation support, research, communication and collaboration, data mining and modeling, and data security, storage, and archive accessibility. e-Discovery systems[edit]
Knowledge Sharing Knowledge Sharing is an activity through which knowledge (i.e., information, skills, or expertise) is exchanged among people, friends, families, communities (e.g., Wikipedia), or organizations.[1][2] Knowledge Flow[edit] Although knowledge is commonly treated as an object, Snowden has argued it is more appropriate to teach it as both a flow and a thing.[8] Knowledge as a flow can be related to the concept of tacit knowledge, discovered by Ludwik Hirszfeld[9] which was later further explicated by Nonaka.[10][11] While the difficulty of sharing knowledge is in transferring knowledge from one entity to another,[12][13] it may prove profitable for organizations to acknowledge the difficulties of knowledge transfer and its paradoxicality, adopting new knowledge management strategies accordingly.[8] Explicit Knowledge Sharing[edit] Tacit Knowledge Sharing[edit] Embedded Knowledge Sharing[edit] Importance of Knowledge Sharing in Organizations[edit] Challenges in Knowledge Sharing[edit] See also[edit]
Knowledge Transfer In organizational theory, knowledge transfer is the practical problem of transferring knowledge from one part of the organization to another. Like knowledge management, knowledge transfer seeks to organize, create, capture or distribute knowledge and ensure its availability for future users. It is considered to be more than just a communication problem. If it were merely that, then a memorandum, an e-mail or a meeting would accomplish the knowledge transfer. Knowledge transfer is more complex because (1) knowledge resides in organizational members, tools, tasks, and their subnetworks[1] and (2) much knowledge in organizations is tacit or hard to articulate.[2] The subject has been taken up under the title of knowledge management since the 1990s. Background[edit] Argote & Ingram (2000) define knowledge transfer as "the process through which one unit (e.g., group, department, or division) is affected by the experience of another"[1] (p. 151). Knowledge transfer in landscape ecology[edit]
Knowledge Ecosystem The idea of a knowledge ecosystem is an approach to knowledge management which claims to foster the dynamic evolution of knowledge interactions between entities to improve decision-making and innovation through improved evolutionary networks of collaboration.[1][2] In contrast to purely directive management efforts that attempt either to manage or direct outcomes, knowledge ecosystems espouse that knowledge strategies should focus more on enabling self-organization in response to changing environments.[3] The suitability between knowledge and problems confronted defines the degree of "fitness" of a knowledge ecosystem. Articles discussing such ecological approaches typically incorporate elements of complex adaptive systems theory. Known implementation considerations of knowledge ecosystem include the Canadian Government.[4] Key Elements[edit] To understand knowledge ecology as a productive operation, it is helpful to focus on the knowledge ecosystem that lies at its core. 1. 2. 3. 4.