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4.   VSM Examples

Here are lots of explained, interactive examples.

Make sure to have read the  ‘VSM in a nutshell’ summary   at least! :


  1.  How to use VSM-boxes !
  2.  Short Story’s examples
  3.  Full Story’s and Various examples
  4.  Various biological use cases:
  5.  Various use cases:  inspired by you

1. How to interact with the VSM-box (prototype version)

2. Short Story’s examples, and more

Tridents:  John and the chicken

It’s not the chicken that would happen to be using the fork:

… nor John who is using the fork:

… using it for something else, as in this example:


Two chickens and more context

= Key demo:  You can always add more context details, to any VSM-term. You do this by adding more terms, and by attaching them with a tri/bident. And you can do this recursively!  
Like: “chicken” → “burnt chicken” (or: “chicken [is / has_attribute] burnt”), and “burnt” → “very burnt”. Likewise you could say something more about “newspaper”; or about any other term.

Tridents:  a simple biological case

…as on the VSM summary page.

This is the very same example,  only to demonstrate that the preposition in was just an easier-to-read ‘avatar’ for the same concept is-located-in.
(Again, you can mousehover to verify that they have the same Class ID).

Here we added a little more context to ‘A’.

The biological showcase

3. Full Story’s and Various examples


The trident can miss any of its three legs, and become a bident:

● Bident without Object leg:  (for relations that have no object):

● Bident without Subject leg. – And compare the structural similarity with the case where a Subject is known:

…and how artificial it would be, to capture this if bidents were not available:

● Bident without Relation leg:  (or: with an implicit relation):

…from which the computer can easily infer: (once it knows that the term ‘white’ (or its ID) is classified as a Color):

Note: we indicated the head VSM-term in the above two VSM-phrases, with dashes above it. Because “mouse” is the concept that we ‘focus on’; and whose meaning is equivalent in both examples.

Bidents: more examples

This shows a bident that specifies: “to-strike  a-match”.  (Or more precisely: “a-striking(-of)   a-match”).
It is not said who strikes the match, so this subunit has no Subject term.

Two implicit relations made explicit, based on the fact that “big” represents a ‘size’, and “very” represents a ‘qualifier’. As explained in the main text.

Here (as mentioned in the main text), the made-explicit relation is associated with the attribute “gene”‘s base concept “expression” instead.  For this to be a valid sentence, the software that supports the interpretation of this type of deduction should know about such a rule, though.

An example with two types of bidents.

Bidents: with numbers

The implicit relation in the 1st sentence was made explicit in the second sentence: it is “having count”, based on the fact that “5” represents a numeric concept.

The term “approximately” modifies the term “5”.

Note! :  this illustrates that “5” is not always a concept that represents ‘an exact 5’ !
•  It is the idea of a ‘Five’, which can be placed in a certain context. This context can be “exactly …”, and one could make that the default assumed context. Or it could for example be “approximately …”, or a ‘five’ with some specified error margin (see a bit further).
•  (This is also why we represent “5” as a ‘specific concept’ here (blue VSM-term), and not as a red ‘Data’ term. (See later)).
•  One could quip:  « It’s Five , Jim, but not as we know it! »

An implicit-vs-explicit example of measurement units and values, expressed with VSM.

  1. The simple explanation (this is enough for most people):
    This says:  “X” is declared to have a certain concentration, given in “mg/l”; and that concentration is declared to have the amount “5″.

  2. A deeper explanation of the semantics behind it. And thoughts on implicit vs. explicit context.
    (See also VSM Principle 4, later on the VSMGraphs page). 
    Conceptually, this phrase is built up like this:
    • Step 1. Consider the VSM-term “X”, before it is connected, i.e. while it stands on its own. At that point it is not yet stated explicitly how many / how much / what kind / etc. of “X” we are dealing with here. Because it is not yet placed in (connected with) any explicit context. So it could be anything. Still, it is a blue VSM-term and thus it is a specific “X”, meaning that it comes embedded in some implicit context. So it is not “X” in general, but some specific (amount/kind/…) “X” that we have in mind. – So next, we will connect other VSM-terms to it, and make some of that implicit context explicit.
    • Step 2. By connecting the rightmost bident, we specify (make explicit) that it’s an “X” that comes in some concentration, and that it’s one that will be given in a unit: “mg/l”.
    • Step 3. By connecting the leftmost bident, we specify that this concentration (which also could have been anything up till then) has the amount of “5″.
    • Note: any term always has more implicit context, much of which we may never even be able to make explicit. For example: What temperature was X at? Which other molecules or impurities interacted with X during the experiment? Did X come from a particular manufacturer? Was it pre-processed? How, and by who? And even: what isotopes did X’s atoms consist of? Etc. – So there is always more ‘context’ present.
      All these things are what make up any specific VSM-term’s implicit context. They are absent details, but they are still what make the VSM-term a ‘specific thing’ in our minds.

It is straightforward to represent such things with VSM. – Though still:

This illustrates an error margin given to a measurement.

Some percentage examples.  (Note again that “percentage” and “%” have the same Class ID).

Tridents: various examples

Here of course, the bident’s implicit relation could be automatically deduced to be something like: “timespan-of-month-<subject>-limited-to-day-given-by-number-<object>”.

Remember that any VSM-term represents a ‘noun’-concept. So here, “reacts-to” (after being connected with A and B like that) represents: “the reaction of A to B’.

Tridents: biological examples

The last three VSM-terms here represent the natural language phrase ‘TuMV-infected Arabidopsis’.

Notice the difference between the previous example and this one. In the previous one, we used the three terms “expression of PAD4“, and we connected “is” to the “expression” term. That’s because it is the “expression” that is increased, not the gene PAD4.
So here at first sight, one might want to use three terms “start of S-phase” and similarly connect “at” to “start”, to say that the ‘translocation happens-at some start’. But somehow it feels like “S-phase” is the main concept here, or at least, after we simply specified it to a more specific part of itself.
So instead, it’s better to consider “start-of” as an attribute that has the same meaning as “initial”, and use it with a bident, in a sense of further narrowing down the meaning of “S-phase”, i.e.: “initial  S-phase”. This is also consistent with similar structures like “5 dogs”, or “half(-of) dogs”.

So we created a bident unit that can be read as “S-phase [further-specified-to] start-part” here, (or as a more specific “S-phase [interval-limited-to-subinterval-as] start-part”).
In contrast, in the previous example we have a trident unit “expression  of(=pertaining-to)  PAD4“. If we would use a bident there, it would be something like “PAD4 [limited-to-attribute?] expression”. But it does not make semantic sense to narrow down the meaning of PAD4 to a property that it is associated with (instead of an own property that specifies what it can be, like in “mutated gene”). It does not change that PAD4 still represents a gene. – So there we must further connect to “expression”.

This explanation can be summarized by using this insight:  ‘adding context‘ means ‘narrowing down the meaning of a VSM-term, as to what range of possible meanings it may represent‘. Therefore:

“start (of)“ narrows down the meaning of “S-phase” itself, and then we further connect to this narrowed-down “S-phase” concept itself;  while “expression” is a distinct concept related to “PAD4“, so we further connect to “expression”.

(A first shot at creating one line of some protocol description).
The bident that connects ‘drug X‘ and ‘mg/l‘ has the implicit relation ‘having concentration‘, because the attribute term ‘mg/l‘ would have been classified as a ‘concentration’.

(Note: The connector order, which is automatically generated by the prototype, may not be optimal here. The “… containing …” connector would better have been placed under the “… placed-in …” one. Maybe that more intuitive ordering has something to do with “tumor-cells” actually being the Head of the VSM-phrase).

List connector

Another list-relation. Since all VSM-terms should be thought of as nouns, or ‘thing’-concepts, this one should be thought of as something like ‘the either-or-ness of …’.

Here, “between” is linked to the meaning “to-be-located-between”, or in noun-form: “the-being-located-between”.  It could be read as: “the-moving (by X)   is-located-between   the-and’ness-of (…)”.

Another way. This “between” is linked to the meaning “the-location-between (a given list of items)”, which can be used as a list-relation.  It could be read as: “the-moving (by X)   is-located-at   the-betweenness-of:  A,  B,  C”.
Still, it may be best for a curation system to work with a vocabulary that only includes one of these two meanings for “between”, and to make it easy for a user to enter phrases like this uniformely.

Coreference connector

(New to biology? Hover the “ubiquitinates” term to see its description).

This example was explained in detail in the Full Story –  read it!
In the following two VSM-sentences, we will make coreferences to terms in the above sentence.

The “it” here refers to the Instance ID of the first occurrence in the previous sentence. – You can mouse-hover to check that the “it” term here has the same Class ID as the “device” term above), …
(Note: the demo here does not yet support adding inter-sentence links manually).

… while the “device” term here, refers to the Instance ID of the second occurrence (the “device” that also beeps) (see Full Story).

Note that here, the term “device” (and not “activates”) is connected to “in  China”. So this sentence expresses not necessarily that Eve is in China too (she could activate it remotely, via the internet), nor necessarily that the activation is in China (the activation-switch could happen anywhere cyberspace).  – Sure one may argue that the activation is in China too (it could be inferred), but definitely, this sentence does not say that Eve is there too). – This illustrates again why VSM connectors connect to single terms specifically, and not to entire ‘triples’ as in RDF.

These show that VSM can be used to store any ‘information’, in the sense of: ‘anything one can think of’, or ‘any idea that you can form in your mind’- The second sentence shows that one can express that another sentence, or even something specific in another sentence, is untrue…
(Mouse-hover to check that “that”‘s Referring-InstanceID refers to “sees”‘s InstanceID above).

… or one can assign a likelihood to the above, because (as in real life) things are rarely black and white.

Interchangeable vs. non-interchangeable connections

Advanced topic.  See the main text for a detailed explanation!
These are the main text’s examples, but as interactive VSM-boxes:

… which just uses another label for the child concept, and adds some extra specifying context (“female”) to the parent concept.


Filling it in:

The user can still extend template, with extra terms and connectors.

A second example of the same template filled in.

Another template. Click on the second empty field to see that autocomplete can really make life easy for curators. Here it would immediately suggest three often-used terms. Still, any other term can still be entered by typing it as usual.

Head: semantic point of view

Written in natural language like that, “eats” would be the ‘head’ or focal concept of the sentence: the term one you’d really refer to if you’d follow it up with e.g. ‘I saw that‘.

But also “John” can be the head concept. The sentence here says:
‘The John, who eats a chicken with a fork’ (e.g. as answer to a question).
It conveys the same information, but stresses another one of the five concepts.

And the same goes for the other three terms. E.g.: ‘The chicken, that is eaten by John with a fork’, etc.:

This illustrate that yes, it can make sense to focus on the ‘with’ (=‘using’) concept too. Just like you can focus on any concept in a VSM-sentence.

This rephrases part of the earlier sentence, saying that ‘The use of the fork, by John, resembles abuse’.

General and Data term types  (experimental ideas)

These sentences will be clear once you understand VSMGraphs, which I may write more about in the future. As mentioned before, the blue / yellow / red colored VSM-terms represent Specific (=default) / General / Data terms, respectively. (And black / white / rectangular nodes in VSMGraphs).

A classification as in an ontology, or concept hierarchy. Between general concepts, not specific ones.

A classification, with as context the taxonomy version that declares this relation.

A description of a general concept.

A general concept has a label. It could be connected to many different labels like this.
It could be connected to a standardized name for it, with a “has-main-alias” relation.

Add-the-connectors-yourself example

Try it yourself! Because it’s easy and fun to add VSM-connectors  (if you know a bit of the biology).
It’s like diagramming sentences in high school, but simpler :

(Remember to click a second time above a same term,
to skip the relation-leg and make a bident).

Compacted terms, semantically equivalent structures: paraphrases

These should be mappable onto each other using some graph-equivalency algorithm.

Mapping sentences like these, for which the determination of equivalence needs a form of automated reasoning, may be more programming work. Compare to the three ‘Levels’ of the SMBL language.

Unwrapping a GO term, leading to Semantic Structure Transparency

Some of the long Gene Ontology (GO) terms could be shown using structurally more transparent VSM structures:

Because that’s what a subject-omitting bident does: it makes phrases like: “Relation-(of)   Object”.
So here it made: “regulation-(of)   (Object-term)”.   (As in: “[unknown]   regulates   (Object)”).

By using GO terms’ existing OWL-definitions, and so analyzing their internal conceptual structure, it should be possible to show GO annotations (as well as larger protein interaction models) as simpler, readable VSM-sentences.

English special terms

Statements like ‘if … then …’ have to be reformulated for VSM, by using a single “if-then” ‘relation’ VSM-term.

Past tense of verbs. A similar structure could be for negations (“does‑not  buy”). Or for modal verbs (“may  buy”), although one could also make a structure with “buy” as object of the “may” relation.

The relation “‘s” is the same as the possessive “of”. The trident is only added in inverse order, because the terms are ordered like that too in readable English.

Quantifiers for logics and mathematics  (experimental section)

One should be able to express anything in VSM, so logical expressions are a valid subset too. E.g.:

The leftmost triple can be read as:  “the-being-smaller  is-valid-for-all  x”,   which explains the peculiar order of the trident’s relation/object/subject-legs.  – Still, the trident was added with three clicks in the normal order of Subject, Relation, and Object.

The same example, now with a quantifier symbol.

The VSM-sentence looks weird (perhaps), but it’s because we’re used to see mathematical expressions in that order. Here is the exact same sentence but with terms reordered (and same connections).

It says: ‘x is smaller than y,  and that is valid for some y,  and that second thing is valid for all x’.

It should be possible to make an example for integrals, or any other mathematical formula too.

Styled VSM-terms

Just to show that, unlike a normal text-based controlled language, the auto-completed VSM-terms can apply proper styling:

Like superscript text for ions; or for showing human gene names in their standard, italicized way.

4. Various biological use cases

Binding of two proteins which then together activate a third

If you’d want to represent this on the biochemical-reaction level, then you may actually be dealing with two units of information here: “A and B bind-to-form some-complex”, and “that-complex activates C”.
This could be captured with an overarching ‘and’ list-connector on top of both units. However, it is probably better to limit one VSM-sentence to one unit of information. So it could be represented by two VSM-sentences then, whereby the VSM-term “that-complex” in the second VSM-sentence would refer to the “some-complex” term in the first one.
 (Curation software should be able to handle inter-sentence references then).

More simply, however, it can be represented with a single VSM-sentence too. If you do not need to explicitly focus on the preparatory step of “A binds B”, then you can just assume that a molecular complex of some A bound to some B already exists, and you just express that that activates C. We can use a list-connector, with a list-relation that expresses “molecularly-bound-unit-of …”, see:

Or, if you’d want to represent this on the biological-process level instead, then you can make this:

Note that it would be interesting if there existed an algorithm that could map variants like these onto each other…  e.g. before a user runs a query from either a reaction-level or a process-level perspective…

Proteins and cofactors examples

= ‘The fact that a protein A is bound to a ‘cofactor’ molecule B, is required for that specific protein A (bound to B) to bind to protein C’.

= ‘Some protein A that was not bound to some cofactor molecule, did not bind to protein C’.

BEL conversion examples

This is a literal conversion of statements in BEL (Biological Expression Language) :
  SET CellLine = "U266"
  proteinAbundance(HGNC:IL6) increases rnaAbundance(HGNC:ENO1)
Or line 2 in short-form:   p(HGNC:IL6) -> r(HGNC:ENO1)

BEL uses two lines for this. The second line is conveniently short when written in short-form.
The VSM-sentence may be quick to construct when using VSM-templates, which in addition supports the curator with convenient autocomplete functionality. And the VSM-sentence is easier to read as one quasi-natural-language sentence, as one unit of information.

Also, compared to controlled languages (like BEL), VSM does not need to be regularly updated with new rules to gain more power of expression. Only the controlled vocabularies that are plugged in to the VSM-box need to be updated, externally, and that happens to them all the time anyway.

This corresponds to the three BEL statements (from under here) :
  SET Anatomy = "cardiovascular system"
  SET MeSHDisease = "Stroke"
  abundance(CHEBI:corticosteroid) decreases biologicalProcess(MESHD:Inflammation)

A VSM-sentence represents this as one fluently readable unit of information.
Types/classifications (CHEBI, MeSH Disease, etc) automatically got linked to what the curator chose via autocomplete, and are all embedded in VSM-terms.
Just mouse-hover them to see.

In addition and importantly: VSM enables to specify more precisely how the ‘“stroke” context’ relates to the rest of the information: with the relation “after” (found in the plain English text under here).
 (Note that the relation could have been “before” too, i.e. describing a preventative measure for at-risk patients. But a simple ‘MeSHDisease = …’ context with BEL does not capture this level of detail.  It is easy to specify this with VSM).

And more?

5. Various use cases  

If you have interesting examples, let me know!  I may add it here.

Read about VSM‘s implications and roll-out on

or go back to How VSM Works or the Summary page