Benefits of detecting and replacing kind N networks: revealing implied content

Added documentation to the sub-framework of detection of replacable partitial networks, and rewrote parts of initialization for a few classes. Actually, what I am talking here about is the kind N networks detection.

Kind N networks, in the MOM terminology, are such ones of four or more nodes, two predecessors (cf. image: nodes A and B), two successors (nodes C and D), and each of the predecessors connected to each of the successors. That looks like a mixture of an "N" and an "X" character.

As the X often gets used to indicate something unknown, but here is not anything unknown, that kind of network got called the N network. -- Add a pair of successor and predecessor, and it still looks somewhat like that X-N mixture. As it features more but two base and two top points, we call it a pure kind N network: any MOM sub-network that consists of an equal number of predecessor and successor nodes (and wires all the predecessor to the successor nodes) is called a pure kind N network. Thus, the N network, of course, is also a pure kind N network.

Then, there are chances, a network features more successor nodes but predecessor ones. If the number of predecessor nodes is >= 2, that kind of net gets called a kind W network, because of its shape. -- It gets called the W network, if it sports only two predecessor and exactly three successor nodes. If a graph features only a single predecessor node, it's a kind V fan. Similar to the naming scheme for the W network, a kind V fan gets called the V fan, if its shape matches the letter: If it features a single predecessor and exactly two successor nodes.

Put upside down, we get a kind M network, the M network, a kind A fan and the A fan, resepectively.

Because kind W/M networks follow the pure kind N network approach by wiring each predecessor with each successor node, all together -- kind W, kind M and pure kind N networks get summarized under the generic "kind N network" label.


So, the efforts done were to detect any kind N networks within a larger MOM network. Why? -- The complete wiring of each predecessor node with each successor node results in a situation, that the content of each successor node equals the content of all predecessor nodes together (plus the content of any separate predecessor nodes the successors don't share with their neighbours of that kind N network). The image aside shows it: Nodes F and G share the content of B, C and D. A becomes part of F only, so does E for G. -- That sharing of common predecessor nodes implies two things:

• First, as any MOM node represents the merged content of its predecessor nodes, we could replace the heavy wiring by adding a new node and wire link all predecessor nodes of the kind N network to that newly added node and it to all the successor nodes. That way the number of edges needed to administer could get reduced from a * b to only a + b.
• Second, as now it might be obvious, that wiring of all the predecessor nodes to each of the successor nodes was nothing different but an implication. A not explied (?)/explicited (?) notion. By adding the node, we make it explicit.

So, detecting kind N networks offers the chance to detect implicit content as well as to decrease the number of edges to administer. That was accomplished yet a few days ago.

Now, part of it was rewritten and all of it documented. Now that sub-framework for kind N net detection needs to get spread into separate class files and put into a sub-directory or sub-directory hierarchy.

      
Updates: none so far

From the Information Science local point of view (upgraded)

Information Science and its predecessor sciences like documentation or library science tackle one big problem in information: stay able to retrieve pieces of information once stored.

In ancient days the pieces of information mainly were material, i.e. not computer-indexable. For example, books were such a kind of material.

Common approach from the information science point of view is to assign each of the books with a set of keywords: When you want to retrieve one of them later, you go ahead, choose some of the keywords, and lookup them in a catalogue which itself refers to the books
theirselves.

To be able to handle this all, you need at least three kinds of storage:

1. a storage for the books, e.g. a kind of library, organized in a way to stay able to at least locate the shelf a particular book is placed in
2. a storage for the catalogue
3. and, most important: a storage for the keywords.

The keywords theirselves have to be stored somewhere. If you neglect this part of the task, one day you will apply this keyword to the book and another day another keyword, but both meaning the same -- i.e. being synonyms.

So, what result originates from that?

Assumed you associated two books X and Y very similar in content with two synonymous but different keywords, A and B. One day you want to know something about a topic that is covered by both books X and Y, but you don't know about that. You directly go to the catalogue. You pick up a search keyword that accidently fell into your mind. Say B.
You look up the appropriate catalogue card and find Y. -- That there is a closely related X book you don't even get aware of. So you fetch the Y book, but the X book remains in shelf. Possibly it would have been valuable to find X as well.

Therefore keywords get stored theirselves too.

The main goal of a keyword storage is similar to the other pieces of information storages: To stay able to find the wanted contents, i.e. the keywords -- and to find exactly the keywords wanted. "Keywords wanted" are those that might be applied to one or more books.

The appropriate tool for keyword or, more precisely, term (as in "search term") storage is a terminology. It mentions every word that was applied to at least one piece of information -- e.g. book -- of the pieces of information storage -- e.g. library. (In a converse, to keep administrative work load small, there's the suggest to choose only keywords already listed in the terminology, to associate books with.)

In a simple case, a terminology might be an alphabetical list of terms. Even better a taxonomy is: For each of the terms it offers an orientation help: Usually there are broader and narrower variants of terms: a mammal is treated to be broader than a dog or cat or cow or horse or something else which is a mammal. (In fact, taxonomies refer to items but list the labels of the items. Taxonomies are closely related to ontologies.)

So, a taxonomy offers is a relationships to identify the location of a given term in the whole taxonomy. Less common than is a are has a relationships, like the ones applied between car and something like wheels, motor, front window, doors etc. Both of these relationships are called hierarchical relationships.

My diploma thesis was about the thesaurus kind of taxonomy, so I currently I am not sure if this applies for the classification kind as well: There is at least one more kind of relationship -- the associative one. It relates terms at each other that don't belong to a hierarchical order, but somehow have something to do with each other, like bird and bird cage. (Admittedly, they might be related using a has a relationship, but I never came across such an assignment.)

Synonyms in taxonomies get treated a special way

Synonyms in taxonomies get treated a special way: They get collected to a single class. Each of the items of that class is (/treated to be, compare to administrators' cheats above) synonymous with every other item of the class.

In classifications there are just classes related to each other, being representative for the terms belonging to the class. In thesauri there are no such classes. During thesaurus creation sets of synonymous terms get identified. One "most significant/common" term gets chosen to be the term representative for all the other synonyms. This "most significant/common" one is called "descriptor", while the others become non-descriptors.

Why all the fuss about the synonym details?

If you look up a synonym you get redirected to the class or descriptor the synonym belongs to. None of the books/other pieces of information ever gets keyworded by a synonym. That solves the problem of searching the X and Y books mentioned above one day by the A and another day by the B search term: Either A points
to B or B to A or both to a third term, say C. And both the books are associated by the "most significant/common" term, e.g. C. So, either if you chose A or B as your search term, you always find all the relevant pieces of information/books, e.g. X and Y.


So far the common part of terminologies.


But there is a usability problem in the widespread is a approach.<<

Glossary

• to retrieve
  • to find again
• class
  • a set of synonymous words
• synonym
  • a word meaning the same as another word
  • sometimes there is a difference between them both, but the one applying them doesn't notice
  • terminology administrators sometimes think it is not worth the effort to keep "very similar" terms distinct, so they merge them into a single class by claiming the very words were synonyms
• catalogue card
  • associations between keywords are stored on catalogue cards which, as a whole, make up the catalogue itself

---

Updates: 20070624: Tagged the posting. Updated the posting style (layout) to my current style, such as using blockquotes when appropriate, more precise word picks, better grammar.

From the Information Science local point of view

Information Science and its predecessor sciences like documentation or library science tackle one big problem in information: stay able to retrieve pieces of information once stored.

In ancient days the pieces of information mainly were material, i.e. not computer-indexable. For example, books were such a kind of material.

Common approach from the information science point of view is to associate each of the books with a set of keywords: When you want to retrieve one of them later, you go ahead, choose some of the keywords, and lookup them in a catalogue which itself refers to the books itself.

To be able to handle this all, you need at least three kinds of storage:

1. a storage for the books, e.g. a kind of library, organized in a way to stay able to at least locate the shelf a particular book is placed in
2. a storage for the catalogue
3. and, most important: a storage for the keywords.

The keywords theirselves have to be stored somewhere. If you neglect this part of the task, one day you will apply this keyword to the book and another day another keyword, but both meaning the same -- i.e. are synonyms.

So, what result originates from that?

Assumed you associated two contently very similar books X and Y with two synonymuos but different keywords, A and B. One day you want to know something about a topic that is covered by both books X and Y, but you don't know about that. You directly go to the catalogue. You pick up a search keyword that accidently fell into your mind. Say B. You look up the appropriate catalogue card and find Y. -- That there's a closely related X book you don't even get aware of. So you fetch the Y book, but the X book remains in shelf. Possibly it would have been valuable to find X as well.

Therefore keywords get stored theirselves too.

The main goal of a keyword storage is similar to the other pieces of information storages: To stay able to find the wanted contents, i.e. the keywords -- and to find exactly the keywords wanted. "Keywords wanted" are those that might be applied to one or more books.

The appropriate tool for keyword or, more precisely, term (as in "search term") storage is a terminology. It mentions every word that was applied to at least one piece of information -- e.g. book -- of the pieces of information storage -- e.g. library. (In a converse, to keep administrative workload small, there's the suggest to choose only keywords already listed in the terminology, to associate books with.)


In a simple case, a terminology might be an alphabetical list of terms. For each of the terms it offers an orientation help: Usually there are broader and narrower variants of terms: a mammal is treated to be broader than a dog or cat or cow or horse or something else which is a mammal. (In fact, terminologies refer to items but list the labels of these.)

So, a terminology offers is a relationships to identify the location of a given term in the whole terminology. Less common than is a are has a relationships, like the ones applied between car and something like wheels, motor, front window, doors etc. These kinds of relationships are called hierarchical relationships.

My diploma thesis was about the thesaurus kind of terminology, so I currently I am not sure if this applies for the classification kind as well: There is at least one more kind of relationship -- the associative one. It relates terms at each other that don't belong to a hierarchical order, but somehow have something to do with each other, like bird and bird cage. (Admittedly, they might be related using a has a relationship, but I never came across such an assignment.)

Synonyms in terminologies get treated a special way

Synonyms in terminologies get treated a special way: They get collected to a single class. Each of the items of that class is (/treated to be, compare to administrators' cheats above) synonymuos with each other item of the class.

In terminologies called classification there just classes related to each other, being representative for the terms belonging to the class. In thesaurus kind of terminologies there are no such classes. During thesaurus development a sets of synonymous terms get identified. One "most significant/common" term gets chosen to be the term representatively for all the other synonyms. This "most significant/common" one is called "descriptor", while the others become non-descriptors.

Why all the fuss about the synonym details?

If you lookup a synonym you get redirected to the class or descriptor the synonym belongs to. None of the books/other pieces of information ever gets keyworded by a synonym.

That solves the problem of searching the X and Y books mentioned above one day by the and another day by the B search term: Either A points to B or B to A or both to a third term, say C. And both the books are associated by the "most significant/common" term, e.g. C. So, either if you chose A or B as your search term, you always find all the relevant pieces of information/books, e.g. X and Y.

So far the common part of terminologies.

But there is a usability problem in the widespread is a approach.

Glossary

to retrieve - to find again

class - a set of synonymous words

catalogue card - associations between keywords are stored on catalogue cards which, as a whole, make up the catalogue itself

synonym - a word meaning the same as another word

• sometimes there is a difference between them both, but the one applying them doesn't notice
• taxonomy administrators sometimes think it is not worth the effort to keep "very similar" terms distinct, so they merge them into a single class by claiming the very words were synonyms

---

Updates: 20070624: Tagged the posting. Updated the posting style (layout) to my current style, such as using blockquotes when appropriate, more precise word picks, better grammar.