A picture is worth a thousand words The expression "Use a picture. It's worth a thousand words." appears in a 1911 newspaper article quoting newspaper editor Arthur Brisbane discussing journalism and publicity.[1] 1913 newspaper advertisement A similar phrase, "One Look Is Worth A Thousand Words", appears in a 1913 newspaper advertisement for the Piqua Auto Supply House of Piqua, Ohio.[2] An early use of the exact phrase appears in an 1918 newspaper advertisement for the San Antonio Light which says: One of the Nation's Greatest Editors Says: One Picture is Worth a Thousand Words The San Antonio Light's Pictorial Magazine of the War Exemplifies the truth of the above statement—judging from the warm reception it has received at the hands of the Sunday Light readers.[3] It is believed by some that the modern use of the phrase stems from an article by Fred R. Another ad by Barnard appears in the March 10, 1927 issue with the phrase "One Picture Worth Ten Thousand Words," where it is labeled a Chinese proverb (一圖勝萬言).
OWL Web Ontology Language Overview W3C Recommendation 10 February 2004 New Version Available: OWL 2 (Document Status Update, 12 November 2009) The OWL Working Group has produced a W3C Recommendation for a new version of OWL which adds features to this 2004 version, while remaining compatible. Please see OWL 2 Document Overview for an introduction to OWL 2 and a guide to the OWL 2 document set. This version: Latest version: Previous version: Editors: Deborah L. Frank van Harmelen (Vrije Universiteit, Amsterdam) Frank.van.Harmelen@cs.vu.nl Please refer to the errata for this document, which may include some normative corrections. See also translations. Copyright © 2004 W3C® (MIT, ERCIM, Keio), All Rights Reserved. Abstract The OWL Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. 1. 2. 3.
Inference Inference is the act or process of deriving logical conclusions from premises known or assumed to be true.[1] The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic. Alternatively, inference may be defined as the non-logical, but rational means, through observation of patterns of facts, to indirectly see new meanings and contexts for understanding. Of particular use to this application of inference are anomalies and symbols. Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference. Statistical inference uses mathematics to draw conclusions in the presence of uncertainty. Examples[edit] Greek philosophers defined a number of syllogisms, correct three part inferences, that can be used as building blocks for more complex reasoning. Now we turn to an invalid form. ? (where ? ? P.
Kynapse Kynapse is the artificial intelligence middleware product, developed by Kynogon, which was bought by Autodesk in 2008 and called Autodesk Kynapse. In 2011, it has been re-engineered and rebranded Autodesk Navigation[3]. Features[edit] Usage[edit] References[edit] External links[edit] Official website Sociology of knowledge The sociology of knowledge is the study of the relationship between human thought and the social context within which it arises, and of the effects prevailing ideas have on societies. It is not a specialized area of sociology but instead deals with broad fundamental questions about the extent and limits of social influences on individual's lives and the social-cultural basics of our knowledge about the world.[1] Complementary to the sociology of knowledge is the sociology of ignorance[2] including the study of nescience, ignorance, knowledge gaps or non-knowledge as inherent features of knowledge making.[3] [4] [5] The sociology of knowledge was pioneered primarily by the sociologists Émile Durkheim and Marcel Mauss at the end of the 19th and beginning of the 20th centuries. Their works deal directly with how conceptual thought, language, and logic could be influenced by the sociological milieu out of which they arise. Schools[edit] Émile Durkheim[edit] Karl Mannheim[edit] Robert K.
OWL at Manchester Subjective logic Subjective logic is a type of probabilistic logic that explicitly takes uncertainty and belief ownership into account. In general, subjective logic is suitable for modeling and analysing situations involving uncertainty and incomplete knowledge.[1][2] For example, it can be used for modeling trust networks and for analysing Bayesian networks. Arguments in subjective logic are subjective opinions about propositions. A binomial opinion applies to a single proposition, and can be represented as a Beta distribution. A multinomial opinion applies to a collection of propositions, and can be represented as a Dirichlet distribution. A fundamental aspect of the human condition is that nobody can ever determine with absolute certainty whether a proposition about the world is true or false. Subjective opinions[edit] Subjective opinions express subjective beliefs about the truth of propositions with degrees of uncertainty, and can indicate subjective belief ownership whenever required. where . Let and
Knowledge management Process of creating, sharing, using and managing the knowledge and information of an organization Knowledge management (KM) is the collection of methods relating to creating, sharing, using and managing the knowledge and information of an organization.[1] It refers to a multidisciplinary approach to achieve organizational objectives by making the best use of knowledge.[2] An established discipline since 1991,[3] KM includes courses taught in the fields of business administration, information systems, management, library, and information science.[3][4] Other fields may contribute to KM research, including information and media, computer science, public health and public policy.[5] Several universities offer dedicated master's degrees in knowledge management. History[edit] In 1999, the term personal knowledge management was introduced; it refers to the management of knowledge at the individual level.[12] Research[edit] Dimensions[edit] Strategies[edit] Motivations[edit] KM technologies[edit]
Information literacy The United States National Forum on Information Literacy defines information literacy as " ... the ability to know when there is a need for information, to be able to identify, locate, evaluate, and effectively use that information for the issue or problem at hand."[1][2] Other definitions incorporate aspects of "skepticism, judgement, free thinking, questioning, and understanding... A number of efforts have been made to better define the concept and its relationship to other skills and forms of literacy. History of the concept[edit] The phrase information literacy first appeared in print in a 1974 report by Paul G. The Presidential Committee on Information Literacy released a report on January 10, 1989, outlining the importance of information literacy, opportunities to develop information literacy, and an Information Age School. The Alexandria Proclamation linked Information literacy with lifelong learning. On May 28, 2009, U.S. Presidential Committee on Information Literacy[edit]
Wiki - Semantic Web Standards Defeasible reasoning Defeasible reasoning is a kind of reasoning that is based on reasons that are defeasible, as opposed to the indefeasible reasons of deductive logic. Defeasible reasoning is a particular kind of non-demonstrative reasoning, where the reasoning does not produce a full, complete, or final demonstration of a claim, i.e., where fallibility and corrigibility of a conclusion are acknowledged. In other words defeasible reasoning produces a contingent statement or claim. Other kinds of non-demonstrative reasoning are probabilistic reasoning, inductive reasoning, statistical reasoning, abductive reasoning, and paraconsistent reasoning. Defeasible reasoning is also a kind of ampliative reasoning because its conclusions reach beyond the pure meanings of the premises. The differences between these kinds of reasoning correspond to differences about the conditional that each kind of reasoning uses, and on what premise (or on what authority) the conditional is adopted: History[edit] Specificity[edit]
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