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Category: Symbol

Linked Data

Linked Data is a method of publishing structured data so that it can be interlinked and become more useful through semantic queries.

Tim Berners-Lee outlined four principles of linked data in his “Linked Data” note of 2006:

 

•  Use URIs to name (identify) things. 

•  Use HTTP URIs so that these things can be looked up (interpreted, “dereferenced”). 

•  Provide useful information about what a name identifies when it’s looked up, using open standards such as RDF, SPARQL, etc. 

•  Refer to other things using their HTTP URI-based names when publishing data on the Web. 

Tim Berners-Lee gave a presentation on linked data at the TED 2009 conference. In it, he restated the linked data principles as three “extremely simple” rules:

 

•  All kinds of conceptual things, they have names now that start with HTTP. 

•  If I take one of these HTTP names and I look it up…I will get back some data in a standard format which is kind of useful data that somebody might like to know about that thing, about that event. 

•  When I get back that information it’s not just got somebody’s height and weight and when they were born, its got relationships. And when it has relationships, whenever it expresses a relationship then the other thing that it’s related to is given one of those names that starts with HTTP.

Linked Open Data (LOD) is Linked Data which is released under an open licence, which does not impede its reuse for free.

Tim Berners-Lee, Linked Data

http://www.w3.org/DesignIssues/LinkedData.html

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Concept Map

Foundation/Premise

“Novak’s work is based on the cognitive theories of David Ausubel, who stressed the importance of prior knowledge in being able to learn (or assimilate) new concepts: “The most important single factor influencing learning is what the learner already knows. Ascertain this and teach accordingly.”[8] Novak taught students as young as six years old to make concept maps to represent their response to focus questions such as “What is water?” “What causes the seasons?” In his book Learning How to Learn, Novak states that a “meaningful learning involves the assimilation of new concepts and propositions into existing cognitive structures.”

Various attempts have been made to conceptualize the process of creating concept maps. Ray McAleese, in a series of articles, has suggested that mapping is a process of off-loading. In this 1998 paper, McAleese draws on the work of Sowa[9] and a paper by Sweller & Chandler.[10] In essence, McAleese suggests that the process of making knowledge explicit, using nodes and relationships, allows the individual to become aware of what they know and as a result to be able to modify what they know.[11] Maria Birbili applies that same idea to helping young children learn to think about what they know.[12] The concept of the knowledge arena is suggestive of a virtual space where learners may explore what they know and what they do not know.
Wikipedia

Introduction, From Novak’s Organization’s Site

Concept maps are graphical tools for organizing and representing knowledge.

They include concepts, usually enclosed in circles or boxes of some type, and relationships between concepts indicated by a connecting line linking two concepts.

Words on the line, referred to as linking words or linking phrases, specify the relationship between the two concepts.

We define concept as a perceived regularity in events or objects, or records of events or objects, designated by a label.

The label for most concepts is a word, although sometimes we use symbols such as + or %, and sometimes more than one word is used.

Propositions are statements about some object or event in the universe, either naturally occurring or constructed.

Propositions contain two or more concepts connected using linking words or phrases to form a meaningful statement. Sometimes these are called semantic units, or units of meaning. Figure 1 shows an example of a concept map that describes the structure of concept maps and illustrates the above characteristics.

Another characteristic of concept maps is that the concepts are represented in a hierarchical fashion with the most inclusive, most general concepts at the top of the map and the more specific, less general concepts arranged hierarchically below. The hierarchical structure for a particular domain of knowledge also depends on the context in which that knowledge is being applied or considered. Therefore, it is best to construct concept maps with reference to some particular question we seek to answer, which we have called a focus question.

The concept map may pertain to some situation or event that we are trying to understand through the organization of knowledge in the form of a concept map, thus providing the context for the concept map.

Another important characteristic of concept maps is the inclusion of cross-links. These are relationships or links between concepts in different segments or domains of the concept map. Cross-links help us see how a concept in one domain of knowledge represented on the map is related to a concept in another domain shown on the map. In the creation of new knowledge, cross-links often represent creative leaps on the part of the knowledge producer.

There are two features of concept maps that are important in the facilitation of creative thinking:

  • the hierarchical structure that is represented in a good map and
  • the ability to search for and characterize new cross-links.

A final feature that may be added to concept maps is specific examples of events or objects that help to clarify the meaning of a given concept. Normally these are not included in ovals or boxes, since they are specific events or objects and do not represent concepts.


The Theory Underlying Concept Maps and How to Construct and Use Them

http://cmap.ihmc.us/docs/theory-of-concept-maps

Joseph D. Novak & Alberto J. Cañas

Institute for Human and Machine Cognition. Pensacola Fl, 32502 www.ihmc.us Technical Report IHMC CmapTools 2006-01 Rev 2008-01

History/Origins 

The technique of concept mapping was developed by Joseph D. Novak and his research team at Cornell University in the 1970s as a means of representing the emerging science knowledge of students.
https://www.ihmc.us/groups/jnovak/

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