| | 63 | Cardinality (from wikipedia): |
| | 64 | |
| | 65 | ''"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."'' |
| | 66 | |
| | 67 | Aggregation versus Composition (from wikipedia): |
| | 68 | |
| | 69 | ''"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."'' |
| | 70 | |
| | 71 | ''In DeepaMehta a composed item (topic) can be either an aggregation or a composition. A composed item is called ''''composite.'''''' |
| | 89 | Topicmaps (from wikipedia): |
| | 90 | |
| | 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 |
| | 93 | |
| | 94 | A topic map represents information using |
| | 95 | |
| | 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. |
| | 99 | |
| | 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. |
| | 101 | |
| | 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. |
| | 103 | |
| | 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. |
| | 105 | |
| | 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."'' |
| | 107 | |
| | 108 | |
