Changes between Version 9 and Version 10 of JuergeN/Philosophy


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Timestamp:
24.11.2012 17:57:49 (8 years ago)
Author:
JuergeN
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  • JuergeN/Philosophy

    v9 v10  
    5757 
    5858 
    59 == Association - Aggregation - Composition == 
     59== Association - Cardinality - Aggregation - Composition == 
    6060 
    6161Two related items can share one or many associations (cardinality). As any item can be consisting of a subset of other items, the question is if it is an aggregation of items or a composition of items.  
    6262 
    63 Cardinality (from wikipedia): 
     63'''Cardinality''' (from wikipedia): 
    6464 
    6565''"In mathematics, the cardinality of a set is a measure of the "number of elements of the set". For example, the set A = {2, 4, 6} contains 3 elements, and therefore A has a cardinality of 3. There are two approaches to cardinality – one which compares sets directly using bijections and injections, and another which uses cardinal numbers."'' 
    6666 
    67 Aggregation versus Composition (from wikipedia): 
     67'''Aggregation''' versus '''Composition''' (from wikipedia): 
    6868 
    6969''"Aggregation differs from ordinary composition in that it does not imply ownership. In composition, when the owning object is destroyed, so are the contained objects. In aggregation, this is not necessarily true. For example, a university owns various departments (e.g., chemistry), and each department has a number of professors. If the university closes, the departments will no longer exist, but the professors in those departments will continue to exist. Therefore, a University can be seen as a composition of departments, whereas departments have an aggregation of professors. In addition, a Professor could work in more than one department, but a department could not be part of more than one university."'' 
     
    8787== Topicmap - Workspace - Domain == 
    8888 
    89 Topicmaps (from wikipedia): 
     89'''Topicmap''' (from wikipedia): 
    9090 
    91 ''"Topic Maps is a standard for the representation and interchange of knowledge, with an emphasis on the findability of information. Topic maps were originally developed in the late 1990s as a way to represent back-of-the-book index structures so that multiple indexes from different sources could be merged. However, the developers quickly realized that with a little additional generalization, they could create a meta-model with potentially far wider application. The ISO standard is formally known as ISO/IEC 13250:2003. 
    92 TopicMapKeyConcepts2.PNG 
     91''"Topic Maps is a standard for the representation and interchange of knowledge, with an emphasis on the findability of information. Topic maps were originally developed in the late 1990s as a way to represent back-of-the-book index structures so that multiple indexes from different sources could be merged. However, the developers quickly realized that with a little additional generalization, they could create a meta-model with potentially far wider application. The ISO standard is formally known as ISO/IEC 13250:2003.'' 
    9392 
    94 A topic map represents information using 
     93''A topic map represents information using topics, representing any concept, from people, countries, and organizations to software modules, individual files, and events,associations, representing hypergraph relationships between topics, and occurrences representing information resources relevant to a particular topic.'' 
    9594 
    96     topics, representing any concept, from people, countries, and organizations to software modules, individual files, and events, 
    97     associations, representing hypergraph relationships between topics, and 
    98     occurrences representing information resources relevant to a particular topic. 
     95''Topic Maps are similar to concept maps and mind maps in many respects, though only Topic Maps are standardized. Topic Maps are a form of semantic web technology, and some work has been undertaken on interoperability between the W3C's RDF/OWL/SPARQL family of semantic web standards and the ISO's family of Topic Maps standards.'' 
    9996 
    100 Topic Maps are similar to concept maps and mind maps in many respects, though only Topic Maps are standardized. Topic Maps are a form of semantic web technology, and some work has been undertaken on interoperability between the W3C's RDF/OWL/SPARQL family of semantic web standards and the ISO's family of Topic Maps standards. 
     97''The semantic expressivity of Topic Maps is, in many ways, equivalent to that of RDF, but the major differences are that Topic Maps (i) provide a higher level of semantic abstraction (providing a template of topics, associations and occurrences, while RDF only provides a template of two arguments linked by one relationship) and (hence) (ii) allow n-ary relationships (hypergraphs) between any number of nodes, while RDF is limited to triplets.'' 
    10198 
    102 The semantic expressivity of Topic Maps is, in many ways, equivalent to that of RDF, but the major differences are that Topic Maps (i) provide a higher level of semantic abstraction (providing a template of topics, associations and occurrences, while RDF only provides a template of two arguments linked by one relationship) and (hence) (ii) allow n-ary relationships (hypergraphs) between any number of nodes, while RDF is limited to triplets. 
     99''Topics, associations, and occurrences can all be typed, where the types must be defined by the one or more creators of the topic map(s). The definitions of allowed types is known as the ontology of the topic map.'' 
    103100 
    104 Topics, associations, and occurrences can all be typed, where the types must be defined by the one or more creators of the topic map(s). The definitions of allowed types is known as the ontology of the topic map. 
     101''Topic Maps explicitly support the concept of merging of identity between multiple topics or topic maps. Furthermore, because ontologies are topic maps themselves, they can also be merged thus allowing for the automated integration of information from diverse sources into a coherent new topic map. Features such as subject identifiers (URIs given to topics) and PSIs (Published Subject Indicators) are used to control merging between differing taxonomies. Scoping on names provides a way to organise the various names given to a particular topic by different sources."'' 
    105102 
    106 Topic Maps explicitly support the concept of merging of identity between multiple topics or topic maps. Furthermore, because ontologies are topic maps themselves, they can also be merged thus allowing for the automated integration of information from diverse sources into a coherent new topic map. Features such as subject identifiers (URIs given to topics) and PSIs (Published Subject Indicators) are used to control merging between differing taxonomies. Scoping on names provides a way to organise the various names given to a particular topic by different sources."'' 
     103In DeepaMehta one can create an unlimmited number of topicmaps. A topicmap can have different types of "backgrounds" or types of maps, like a canvas, a grid, a picture, an image, a geomap, a diagram, a timeline and so on. Depending on the type of map and associated functions topics can be placed freely or by computing their position in the map.  
    107104 
    108105 
    109 == Distance - Relevance == 
     106'''Workspace''' 
     107 
     108Until today in DeepaMehta a workspace has several functions. It defines the domain (field of knowledge/interest) and the level of privacy (shared, private, etc.). Topics, Topic Types and Topic Maps are connected to a workspace which can be private or shared. I really think we should divide these two essential aspects into ''workspace'' and ''domain'', suggesting these types of workspaces: 
     109 
     110 * private (4 owners eyes only) = ''private'' 
     111 * privately shared (creator/owner may invite other users) = ''shared'' 
     112 * commonly shared (any users may invite other users into the goup) = ''common'' 
     113 * public (all content is shared with everyone) = ''public''  
     114 
     115I should also be able to decide, if the shared content will be shared "read only" or "read/write".  
     116 
     117 
     118'''Domain''' (from wikipedia):  
     119 
     120''"Domain knowledge is valid knowledge used to refer to an area of human endeavour, an autonomous computer activity, or other specialized discipline."'' 
     121 
     122I would like to introduce the term '''domain''' to DeepaMehta, describing the context or field of knowledge in which I create and share topictypes ontologies and content (topics). They should be associated to the domain where they are created but also possibly be associated to one or many other domains. 
     123 
     124I think this is especially necessary in term of collaboration. To merge a domain and work in the same domain still has nothing todo with the question if newly created content shall remain private or be shared, common or public (ro/rw).  
     125 
     126  
     127== Link - Copy - Move == 
     128 
     129Following the idea that a workspace can be private, shared, common or public, the question is how content can be transferred between workspaces and domains. I would suggest three possible options: 
     130 
     131 * link = Content stays in the workspace/domain it was created but is visible in the linked domain/workspace. Changes will apply to the original content.  
     132 * copy = Content is copied to any other workspace/domain. Changes will apply only to the edited copy. 
     133 * move = Content is moved to any other workspace/domain. Changes will apply to the original content in the new domain/workspace. 
     134 
     135 
     136== Versioning == 
     137 
     138In terms of a semmantic and topic map based system, content has many layers. Content can be the data of a topic, its semmantic associations to other topics and its positioning in the map. In terms of versioning all three layers need to be versioned if you really want to be able to look backwards in the process of content creation. 
     139 
     140== Finding - Distance - Relevance == 
     141 
     142When looking for content, I would like to find the most relevant information in first place. Relevance could be addressed by various things, amongs others the distance in the graph (also through aging in time), other peoples associations, 'learning' the results, etc. If DeepaMehta wants to map the universe, it will need to filter and forget a lot, if poeple shall still be able to use it.