Knowledge Engineer A knowledge engineer integrates knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise. Overview[edit] Often knowledge engineers are employed to translate the information elicited from domain experts into terms which cannot be easily communicated by the highly technalized domain expert (ESDG 2000). Knowledge engineers interpret and organize information on how to make systems decisions (Aylett & Doniat 2002). The term "Knowledge engineer" first appeared in the 1980s in the first wave of commercialization of AI – the purpose of the job is to work with a client who wants an expert system created for them or their business. Validation & verification with knowledge engineers[edit] Validation is the process of ensuring that something is correct or conforms to a certain standard. It is important that a knowledge engineer incorporates validation procedures into their systems within the program code. References[edit]
Zeno's paradoxes Zeno's arguments are perhaps the first examples of a method of proof called reductio ad absurdum also known as proof by contradiction. They are also credited as a source of the dialectic method used by Socrates.[3] Some mathematicians and historians, such as Carl Boyer, hold that Zeno's paradoxes are simply mathematical problems, for which modern calculus provides a mathematical solution.[4] Some philosophers, however, say that Zeno's paradoxes and their variations (see Thomson's lamp) remain relevant metaphysical problems.[5][6][7] The origins of the paradoxes are somewhat unclear. Diogenes Laertius, a fourth source for information about Zeno and his teachings, citing Favorinus, says that Zeno's teacher Parmenides was the first to introduce the Achilles and the tortoise paradox. Paradoxes of motion[edit] Achilles and the tortoise[edit] Distance vs. time, assuming the tortoise to run at Achilles' half speed Dichotomy paradox[edit] Suppose Homer wants to catch a stationary bus.
Knowledge Management Yes, knowledge management is the hottest subject of the day. The question is: what is this activity called knowledge management, and why is it so important to each and every one of us? The following writings, articles, and links offer some emerging perspectives in response to these questions. As you read on, you can determine whether it all makes any sense or not. Content Developing a Context Like water, this rising tide of data can be viewed as an abundant, vital and necessary resource. Before attempting to address the question of knowledge management, it's probably appropriate to develop some perspective regarding this stuff called knowledge, which there seems to be such a desire to manage, really is. A collection of data is not information. The idea is that information, knowledge, and wisdom are more than simply collections. We begin with data, which is just a meaningless point in space and time, without reference to either space or time. An Example A Continuum Extending the Concept
Knowledge Community A knowledge community is community construct, stemming from the convergence of knowledge management as a field of study and social exchange theory. Formerly known as a discourse community and having evolved from forums and web forums, knowledge communities are now often referred to as a community of practice or virtual community of practice. As with any field of study, there are various points of view on the motivations, organizing principles and subsequent structure of knowledge communities. Perspectives[edit] As a web or virtual construct, knowledge communities can be said to have evolved from bulletin board systems, web forums and online discourse communities through the 80s and 90s. Stemming from social exchange theory, a well-established perspective is to view knowledge communities as a type of exchange. Knowledge communities can also be viewed as a method by which to do organizational or process innovation. Organizational behavior and structure[edit] Pitfalls[edit] References[edit]
DIKW Pyramid The DIKW Pyramid, also known variously as the "DIKW Hierarchy", "Wisdom Hierarchy", the "Knowledge Hierarchy", the "Information Hierarchy", and the "Knowledge Pyramid",[1] refers loosely to a class of models[2] for representing purported structural and/or functional relationships between data, information, knowledge, and wisdom. "Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge".[1] History[edit] "The presentation of the relationships among data, information, knowledge, and sometimes wisdom in a hierarchical arrangement has been part of the language of information science for many years. Although it is uncertain when and by whom those relationships were first presented, the ubiquity of the notion of a hierarchy is embedded in the use of the acronym DIKW as a shorthand representation for the data-to-information-to-knowledge-to-wisdom transformation. Data, Information, Knowledge, Wisdom[edit] Description[edit] Data[edit]
Collaborative network A collaborative network, is a network consisting of a variety of entities (e.g. organizations and people) that are largely autonomous, geographically distributed, and heterogeneous in terms of their operating environment, culture, social capital and goals, but that collaborate to better achieve common or compatible goals, and whose interactions are supported by computer networks. The discipline of collaborative networks focuses on the structure, behavior, and evolving dynamics of networks of autonomous entities that collaborate to better achieve common or compatible goals.[1][2] There are several manifestations of collaborative networks, e.g.:[1] Virtual enterprise (VE).Virtual Organization (VO).Dynamic Virtual Organization.Extended Enterprise.VO Breeding environment (VBE).Professional virtual community (PVC).Business Ecosystem.Virtual manufacturing network Applications[edit] Elements[edit] The seven essential elements of collaborative networks: Reference models[edit] Challenges[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. 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. Key elements of networked knowledge systems[6] include: 1. 2. 3. 4. See also[edit] Notes[edit] Clippinger, J.
The Problem with the Data-Information-Knowledge-Wisdom Hierarchy - David Weinberger by David Weinberger | 9:00 AM February 2, 2010 The data-information-knowledge-wisdom hierarchy seemed like a really great idea when it was first proposed. But its rapid acceptance was in fact a sign of how worried we were about the real value of the information systems we had built at such great expense. What looks like a logical progression is actually a desperate cry for help. The DIKW hierarchy (as it came to be known) was brought to prominence by Russell Ackoff in his address accepting the presidency of the International Society for General Systems Research in 1989. Where is the Life we have lost in living? Those lines come from the poem “The Rock” by T.S. The DIKW sequence made immediate sense because it extends what every Computer Science 101 class learns: information is a refinement of mere data. But, the info-to-knowledge move is far more problematic than the data-to-info one. So, what is “knowledge” in the DIKW pyramid? And humbug.
Open innovation Open innovation is a term promoted by Henry Chesbrough, adjunct professor and faculty director of the Center for Open Innovation at the Haas School of Business at the University of California,[1] in a book of the same name,[2] though the idea and discussion about some consequences (especially the interfirm cooperation in R&D) date as far back as the 1960s[citation needed]. Some instances of open innovation are Open collaboration,[3] a pattern of collaboration, innovation, and production. The concept is also related to user innovation, cumulative innovation, know-how trading, mass innovation and distributed innovation. “Open innovation is a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology”.[2] Alternatively, it is "innovating with partners by sharing risk and sharing reward. Advantages[edit] Disadvantages[edit] Models of open innovation[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. 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). Szulanski's doctoral dissertation ("Exploring internal stickiness: Impediments to the transfer of best practice within the firm") proposed that knowledge transfer within a firm is inhibited by factors other than a lack of incentive. Knowledge transfer includes, but encompasses more than, technology transfer. Knowledge transfer between public and private domains[edit] Knowledge transfer in landscape ecology[edit] Types of knowledge[edit]
Five Best Mind Mapping Tools Mapping the Knowledge Society | SocInfo As part of my work and research as a Google sponsored fellow at the Reuters Digital Vision program at Stanford University, and in cooperation with several colleagues from the private and social sectors and international organizations, we have developed a series of visual representations of processes, frameworks and ecosystems supporting the Knowledge Society and Human Development through Information and Communication Technologies (ICT4Dev). In putting together these conceptual maps, I have to acknowledge and thank the collaboration, feedback and suggestions from my Digital Vision friends and colleagues, in particular Steven Ketchpel, Margarita Quihuis, José Arocha, Mans Olof-Ors, and Sham Bathija. I hope these "maps" are of interest to others and can be put to use in the formulation of strategies for a significant impact of information and communication technologies (ICT) on the Sustainable Human Development processes and the construction of an equitave Knowledge Society.