Philosophy

For me DeepaMehta is an attempt to understand and describe some fundamental ideas about Life, the Universe and Everything (thx D.A.!). Behind many ideas and concepts we try to implement into the software, there often are endless discussions and deep thoughts about general/universal principals. To me this philosophic exchange is as important as the vision of its meaningful implementation into a software based tool for collaboration - which I really hope we will achieve some day.

Type - Instance

Type–token distinction (source: Wikipedia)

"In disciplines such as philosophy and knowledge representation, the type–token distinction is a distinction that separates a concept from the objects which are particular instances of the concept. For example, the particular bicycle in your garage is a token of the type of thing known as "The bicycle." Whereas, the bicycle in your garage is in a particular place at a particular time, that is not true of "the bicycle" as used in the sentence: "The bicycle has become more popular recently." In logic, the distinction is used to clarify the meaning of symbols of formal languages.

Types are often understood ontologically as being concepts. They do not exist anywhere in particular because they are not physical objects. Types may have many tokens. However, types are not directly producible as tokens are. You may, for instance, show someone the bicycle in your garage, but you cannot show someone "The bicycle." Tokens always exist at a particular place and time and may be shown to exist as a concrete physical object.

It can be quite useful to distinguish between an abstract "type" of thing, and the various physical "tokens" or examples of that thing. This type-token distinction is illustrated by way of examples. If we say that two people "have the same car", we may mean that they have the same type of car (e.g. the same brand and model), or the same particular token of the car (e.g. they share a single vehicle). This distinction is useful in other ways, during discussion of language. In the phrase "Grendel is Grendel is Grendel is Grendel", there are only two types of words ("Grendel" and "is") but there are seven tokens (four "Grendel" and three "is" tokens)."

In DeepaMehta a token is called an 'instance.'

Entity Type : A collection of entities that share common properties or characteristics. Entity Instance: A single occurrence of a particular entity type is called entity instance.

Item - Entity - Property

Everything (every item or entity) can have an endless amount of properties. Every property can be an item in itself.

In DeepaMehta we call an item or entity a 'topic'.

Atoms - Holons

The idea of the atom is that it is the impartible smallest part that everything is made of. Today we know that science does not know much about the smalles part yet, because they find smaller and smaller parts every once in while. And maybe it the end it all comes down to energy and imagination anyways.

Ken Wilber (source: Wikipedia): Holons

"A key idea of Wilber's is to study and categorize items in terms of their nature as a holon, a term deriving from the writings of Arthur Koestler. He observed that it seems every entity and concept shares a dual role: being both an autonomous, self-reliant unit (whole entity) unto itself, and also a part of one (or more) other wholes. Examples include the way in which a cell in an organism is both a whole as a cell and and at the same time a part of another whole, the organism. Likewise a letter is a self-existing entity and simultaneously an integral part of a word, which then is part of a sentence, which is part of a paragraph, which is part of a page; and so on. Everything from quarks to matter to energy to ideas can be looked at in this way. The relation between individuals and society is not the same as between cells and organisms though, because individual holons can be members but not parts of social holons."

If we tried to describe things on the level of its atoms (smallest part), they would be meaningless, because we could only count the atoms in the end. So what ever the smallest part may be, what makes them relevant are their linkages.

In DeepaMehta we call the linkage an 'association.

Whole - Part

If an item can be the result of a subset of other items and every item again can be part of other items itself, then we need to define a role for each item in its relationship to other items. The role can be 'whole' or 'part'.

Graph - Node - Edge

Graph (from wikipedia):

"In mathematics, a graph is a representation of a set of objects where some pairs of the objects are connected by links. The interconnected objects are represented by mathematical abstractions called vertices, and the links that connect some pairs of vertices are called edges. ... The edges may be directed or undirected. For example, if the vertices represent people at a party, and there is an edge between two people if they shake hands, then this is an undirected graph, because if person A shook hands with person B, then person B also shook hands with person A. On the other hand, if the vertices represent people at a party, and there is an edge from person A to person B when person A knows of person B, then this graph is directed, because knowledge of someone is not necessarily a symmetric relation (that is, one person knowing another person does not necessarily imply the reverse; for example, many fans may know of a celebrity, but the celebrity is unlikely to know of all their fans). This latter type of graph is called a directed graph and the edges are called directed edges or arcs. Vertices are also called nodes or points, and edges are also called lines or arcs. Graphs are the basic subject studied by graph theory. The word "graph" was first used in this sense by J.J. Sylvester in 1878. ... In mathematics, a multigraph or pseudograph is a graph which is permitted to have multiple edges, (also called "parallel edges"[1]), that is, edges that have the same end nodes. Thus two vertices may be connected by more than one edge. ... There are two distinct notions of multiple edges. One says that, as in graphs without multiple edges, the identity of an edge is defined by the nodes it connects, but the same edge can occur several times between these nodes. Alternatively, one defines edges to be first-class entities like nodes, each having its own identity independent of the nodes it connects."

In DeepaMehta two items that are linked with each other share an 'association' which is seen to be a node itself.

Association - Cardinality - Aggregation - Composition

Two related items can share one or many associations. 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. Wheter an item (instance) can have one or many associations of the same type of item is defined through the possible cardinality (one or many).

Cardinality (from wikipedia):

"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."

Aggregation versus Composition (from wikipedia):

"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."

In DeepaMehta a composed item (topic) can be either an aggregation or a composition. A composed item is called 'composite.'

Sequence

tbd.

Facets

tbd.

Private - Shared - Public

Any content can either be private, shared or public. Private content is content only the creator can see and edit. Shared content is content that is shared with other people (a group). It makes sense to differenciate if the content is privately shared or commonly shared.

Privately shared content is content that the owner shares with other people she invites. The decision about who she shares the content with remains her private decision.

Commonly shared content is content the creator shares with other people who may then themselves decide with whom else they want to share the content (distributed content sharing).

Public content is content that is available to everyone.

Shared content can be shared read only or read/write ( = edit, delete).

Topicmap - Workspace - Domain

Topicmap (from wikipedia):

"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.

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.

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.

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.

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.

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."

In 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.

Workspace

Until 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:

• private (for the owner's eyes only) = private
• privately shared (creator/owner may invite other users) = shared
• commonly shared (any users may invite other users into the goup) = common
• public (all content is shared with everyone) = public

I should also be able to decide, if the shared content will be shared "read only" or "read/write".

Domain (from wikipedia):

"Domain knowledge is valid knowledge used to refer to an area of human endeavour, an autonomous computer activity, or other specialized discipline."

I 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.

I 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).

Link - Copy - Move

Following 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:

• link = Content stays in the workspace/domain it was created but is visible through a proxy-item in the linked domain/workspace. Changes will apply to the original content.
• copy = Content is copied to any other workspace/domain. Changes will apply only to the edited copy.
• move = Content is moved to any other workspace/domain. Changes will apply to the original content in the new domain/workspace.

Versioning

In terms of a semantic and topic map based system, content has many layers. Content can be the data of a topic, its semantic 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.

Finding - Distance - Relevance

When 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.