The Universal Pantograph Representation is the essence of networking

(at least not for implementations). Not WikiPedia:

Topic Maps is an ISO standard for the representation and interchange of knowledge, with an emphasis on the findability of information. The standard is formally known as ISO/IEC 13250:2003.

A topic map can represent information using topics (representing any concept, from people, countries, and organizations to software modules, individual files, and events), associations (which represent the relationships between them), and occurrences (which represent relationships between topics and information resources relevant to them). They are thus similar to semantic networks and both concept and mind maps in many respects. In loose usage all those concepts are often used synonymously, though only topic maps are standardized.

Nor this (Topic Maps Reference Model, 13250-5)

The Topic Maps family of standards is designed to facilitate the gathering of all the information
about a subject at a single location. The information about a subject includes its relationships
to other subjects; such relationships may also be treated as subjects (subject-centric) [And subject-centering].

Nor this (Topic Maps — Data Model (for review 2006-06-18)):

Topic Maps is a technology for encoding knowledge and connecting this encoded knowledge to relevant information resources. Topic maps are organized around topics, which represent subjects of discourse; associations, representing relationships between the subjects; and occurrences, which connect the subjects to pertinent information resources.

Topic maps may be represented in many ways: using Topic Maps syntaxes in files, inside databases, as internal data structures in running programs, and even mentally in the minds of humans. All these forms are different ways of representing the same abstract structure. It is that structure which this part of ISO/IEC13250 defines, in the form of a data model.

Nor this (ISO/IEC 13250, Topic Maps (Second Edition)):

A set of one or more interrelated documents that employs the notation defined by
this International Standard is called a topic map. In general, the structural information conveyed
by topic maps includes:
—groupings of addressable information objects around topics (‘occurrences’), and
—relationships between topics (‘associations’).
A topic map defines a multidimensional topic space — a space in which the locations are topics,
and in which the distances between topics are measurable in terms of the number of intervening
topics which must be visited in order to get from one topic to another, and the kinds of
relationships that define the path from one topic to another, if any, through the intervening topics, if
any.

Nor this:

One of the features of Topic Maps, specifically of TMDM, is to offer a wide variety to express subject-related information and relationships. Alone for connecting information you have full-blown associations, specialized ones called occurrences and even more specialized ones called names. And then you have all the topic identification, reification stuff and other warts (which shall better not be mentioned here).

Nor this (TMRA):

Topic Maps is a semantic technology designed for the integration of information, and is as such closely connected with other information-centric technologies.

Nor this (1996 presentation):

The purpose of a Topic Map-based hyperdocument is to interconnect semantically heterogeneous information. Conference Proceedings seemed to us to be a good sample of a type of hyperdocument that is adapted to a Topic Map.

A Topic Map allows readers to navigate following topics that can appear in multiple documents. Rather than just being a simple term, a topic is a link that contains a title and is pointing to places in the documents where there are occurrences of this topic. These places, otherwise called anchors, can be grouped following various roles they play, and the anchor roles orient the navigation (e.g., definition, mention, example, etc.).

Multi-document Indexes A Topic Map is functionnally equivalent to multi-document indexes, glossaries, and thesauri. Topics are organized in types, each instance of a topic type has a title, and each occurrence of a given topic in a document is described including the semantics of the anchor role.

Nor this (2002):

The Topic Maps Reference Model takes the position that the minimum set of structural features that must be common to all knowledge, in order to allow all kinds of knowledge to be aggregated, are a set of constraints on the structure of semantic networks. The kind of semantic network that is defined by the Reference Model is called a “topic map graph”.

In a topic map graph, every node is a surrogate for exactly one subject (as in “subject of conversation”), and no two nodes are surrogates for the same subject. All nodes are connected to each other by nondirectional arcs. The Reference Model provides exactly four kinds of arcs, each of which is used in the same very specific way in each “assertion”. In the Reference Model, assertions are the primary units of knowledge. Every assertion is a set of specific nodes interconnected in specific ways by specific kinds of arcs. Assertions represent relationships between subjects, and in each assertion, each related subject plays a specific role (called a “role type”), which is itself a subject. Each assertion can itself be an instance of an “assertion type”, which is also a subject.

In general, the Topic Maps Reference Model is designed in such a way as to increase the likelihood that knowledge will be found and used when needed, that the creators and maintainers of knowledge will be rewarded for their efforts and contributions, and that the opportunity to be in the knowledge aggregation business will be very widely distributed, instead of being concentrated in relatively few organizations.

The general impact is to increase the knowledge economy’s diversity.

Nor this (2004):

Maps of territories

• Topic maps, as the name applies, are maps
• Maps, in the usual geographical sense, are applied to particular physical territories
• A geographic map is a view of a territory (an expression of knowledge) about a territory, and about othe things, too, as needed to provide context
• Same is true for topic maps, except that the mapped “territories” are information resources (corpora and combinations of corpora

Nor this:

1. The Topic Maps saga, which began in 1993, teaches that powerful subject-based indexing of information is so compelling that it sells itself. In subject-based indexing, entries in indexes are designed to be findable, and, once found, each listing has all that’s known about that particular subject. Customers typically react quickly and positively to subject-based indexes of their complex information resources. They see the value proposition intuitively; it speaks for itself. The selling power of it is awesome. Among other things, it compels customers to acknowledge the importance of careful thinking about ontologies. It compels them to buy ontological services. It’s good business for people like us.

2. I have a special message for you if you must maintain a specialized ontology and, at the same time, you must demonstrate that there is a straightforward way to integrate your knowledge with diverse other knowledge resources whose ontologies, if any, are not under your control. My message is that there is a useful approach, that not only provides political cover, but also happens to work.

(Here is a contrasting approach on “sells itself.”)

Nor this (academic context)

Topic Maps (TM) [3] are a semantic web technology that provides a flexible and intuitive modeling paradigm for defining a conceptual navigation layer that supports finding of web resources of various kinds, such as documents, images, database records, audio/video clips, etc. The advantage of using the TM technology for developing digital collections is twofold: from one side it provides a convenient and intuitive presentation of interrelated concepts embedded in information resources, and from another, the digital content is in a standards-based format, which makes it interchangeable and interoperable. Basically, topic maps are collections of topics, associations, resources, and scopes. In the TM model the concepts are reified in topics, and they can be categorized using types. Topic maps describe by means of topics what a resource is about. Associations express semantic relationships between topics, and the extent of validity of topics, associations, and resources is called scope. Topic Maps can be viewed as a method for structuring and organizing information on the semantic and metadata level.

Nor this:

Topic maps are a new ISO standard for describing knowledge structures and associating them with information resources. As such they constitute an enabling technology for knowledge management. Dubbed “the GPS of the information universe”, topic maps are also destined to provide powerful new ways of navigating large and interconnected corpora.

While it is possible to represent immensely complex structures using topic maps, the basic concepts of the model — Topics, Associations, and Occurrences (TAO) — are easily grasped. This paper provides a non-technical introduction to these and other concepts (the IFS and BUTS of topic maps), relating them to things that are familiar to all of us from the realms of publishing and information management, and attempting to convey some idea of the uses to which topic maps will be put in the future.

[I]nstead of simply replicating the features of a printed index, the topic map model generalizes them, extending them in many directions at once and thereby enabling navigation in hitherto undreamt of ways. With topic maps a user can wander at leisure through a multidimensional topic space of knowledge before deciding which information resources are relevant, instead of wading through volumes or megabytes of data in order to find what he or she is looking for. Similarly, queries based on topic maps can be much more accurate than simple full text searching. From being a useful but often underused adjunct to the main body of information, indexes (when based on topic maps) look set to become the sine qua non of information delivery and consumption.

The generality and expressive power of the topic map model bring with it other advantages that go far beyond those traditionally associated with indexes. The close similarity to semantic nets gives an idea of how topic maps, even without any occurrences connecting them to an information pool, can become valuable resources in their own right. This in turn opens up new business opportunities for creating and selling “portable topic maps” that can be overlaid on multiple information pools.

Nor this:

ITM also permits automatic creation and maintenance of Topic Maps using linguistic components for extraction of knowledge from text. For example linguistic analysis of the string “Alcatel to acquire Lucent” would result in the creation of the topics “Alcatel” and “Lucent” of class “Company”, and the creation of an association of type “Acquisition” where the Topic “Alcatel” would have the role of acquirer and the Topic “Lucent” the role acquired.

ITM permits the use of quite voluminous Topic Maps (several million Topics and Associations) for industrial applications. The solution has already been successfully deployed worldwide

Nor this:

NetworkedPlanet’s products are based on the creation of a Topic Map knowledge structure separate from the content store. The topic map structure is flexible enough to represent both domain knowledge and document classification schemes. Unlike a traditional relational database schema, however, the topic map knowledge structure is entirely data-driven, allowing an unparalleled degree of freedom to refine and update schemas without impacting existing applications.

Although separate from the resources, the topic map maintains links to the resources that relate to the subjects described in the topic map, effectively providing a map or index to the content. The relationships between knowledge items enable the navigation of knowledge-space independently of the content.

Nor this:

“Topic map” is an ISO standard for describing knowledge structures and associating them with information resources. They were created to be an ontology framework for information retrieval. Topic Maps have a rich semantic model that is well designed to support information retrieval in general, but can also be used for an almost unlimited range of other applications.

Nor this:

Technology for describing knowledge structures and using them to improve the findability of information, defined in ISO 13250 :2007.

Perhaps I’m showing my so-pre-millenial dot-com-bust heritage in even seeking an “elevator speech” — not a snowclone, oddly enough — for topic maps, but that’s not what people I’ve talked to about topic maps in Drupal say.

Compare the following social networking applications — node-centric and graph-structured, as in the nature of their subject matter they must be:

LinkedIn: LinkedIn brings together your professional network. Stay in touch, Discover job & business opportunities, Get expert business advice.

Friendster: See who’s on Friendster. Search over 60 million profiles

FaceBook: Facebook is a social utility that connects you with the people around you.

MySpace: A place for friends.

NetFlix: Movies for you.

Here are the words:

a, advice, around, brings, business, connects, discover, expert, FaceBook, for, friends, Friendster, get, in, is, job, LinkedIn, million, movies, MySpace, NetFlix, network, on, opportunities, over, people, place, professional, profiles, search, see, social, stay, that, the, together, touch, utility, who’s, with, you, your.

Verbs: bring, connect, discover, get, is, search, see, stay, touch.

Prepositions: around, for, in, on, over, with, together

Nouns: advice, business, expert, job, movies, network, opportunities, people, place, professional, profile, utility

Pronouns: You.

One might also compare citation analysis sites which, again by virtue of their subject matter must be node-centric and graph-structured (though not, I would argue, “search” sites like Google or Lexis).

UDPATE Googling about for “elevator speech,” I find this fascinating implicit riposte from a Unitarian Universalist (“One who knocks at your door for no apparent reason”).

UPDATE Funny:

“6 minutes ago”?! Frightening.