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Knowledge engineering

Knowledge engineering
Knowledge engineering (KE) was defined in 1983 by Edward Feigenbaum, and Pamela McCorduck as follows: KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.[1] It is used in many computer science domains such as artificial intelligence,[2][3] including databases, data mining, bioinformatics, expert systems, decision support systems and geographic information systems. Various activities of KE specific for the development of a knowledge-based system: Assessment of the problemDevelopment of a knowledge-based system shell/structureAcquisition and structuring of the related information, knowledge and specific preferences (IPK model)Implementation of the structured knowledge into knowledge basesTesting and validation of the inserted knowledgeIntegration and maintenance of the systemRevision and evaluation of the system. Knowledge engineering principles[edit] Bibliography[edit] Related:  staging

Machine learning for an expert system to predict preterm birth risk. 50 Great Examples of Data Visualization Wrapping your brain around data online can be challenging, especially when dealing with huge volumes of information. And trying to find related content can also be difficult, depending on what data you’re looking for. But data visualizations can make all of that much easier, allowing you to see the concepts that you’re learning about in a more interesting, and often more useful manner. Below are 50 of the best data visualizations and tools for creating your own visualizations out there, covering everything from Digg activity to network connectivity to what’s currently happening on Twitter. Music, Movies and Other Media Narratives 2.0 visualizes music. Liveplasma is a music and movie visualization app that aims to help you discover other musicians or movies you might enjoy. Tuneglue is another music visualization service. MusicMap is similar to TuneGlue in its interface, but seems slightly more intuitive. Digg, Twitter, Delicious, and Flickr Internet Visualizations

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

Applications of artificial intelligence Artificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore," Nick Bostrom reports.[1] "Many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. Computer science[edit] AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered a part of AI. Finance[edit] Hospitals and medicine[edit] Heavy industry[edit]

Data Visualization: Modern Approaches - Smashing Magazine Data presentation can be beautiful, elegant and descriptive. There is a variety of conventional ways to visualize data – tables, histograms, pie charts and bar graphs are being used every day, in every project and on every possible occasion. However, to convey a message to your readers effectively, sometimes you need more than just a simple pie chart of your results. In fact, there are much better, profound, creative and absolutely fascinating ways to visualize data. Many of them might become ubiquitous in the next few years. So what can we expect? Let’s take a look at the most interesting modern approaches to data visualization as well as related articles, resources and tools. 1. Trendmap 20071 Informationarchitects.jp3 presents the 200 most successful websites on the web, ordered by category, proximity, success, popularity and perspective in a mindmap. 2. Newsmap4 is an application that visually reflects the constantly changing landscape of the Google News news aggregator. 3. 4. 5. 6.

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]

AI effect The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence. Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was chorus of critics to say, 'that's not thinking'."[1] AI researcher Rodney Brooks complains "Every time we figure out a piece of it, it stops being magical; we say, Oh, that's just a computation. AI is whatever hasn't been done yet[edit] As soon as AI successfully solves a problem, the problem is no longer a part of AI. Douglas Hofstadter expresses the AI effect concisely by quoting Tesler's Theorem: "AI is whatever hasn't been done yet When problems have not yet been formalised, they can still be characterised by a model of computation that includes human computation. AI applications become mainstream[edit]

Ontology learning Typically, the process starts by extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical[1] or symbolic[2] [3] techniques are used to extract relation signatures. Procedure[edit] Ontology learning is used to (semi-)automatically extract whole ontologies from natural language text.[4][5] The process is usually split into the following eight tasks, which are not all necessarily applied in every ontology learning system. Domain terminology extraction[edit] Concept discovery[edit] In the concept discovery step, terms are grouped to meaning bearing units, which correspond to an abstraction of the world and therefore to concepts. Concept hierarchy derivation[edit] In the concept hierarchy derivation step, the OL system tries to arrange the extracted concepts in a taxonomic structure. Learning of non-taxonomic relations[edit] Rule discovery[edit] Ontology population[edit] See also[edit] Jump up ^ A.

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]