Knowledge Representation

To represent knowledge, we need a code, a sort of a coordinate system, which is as small and as general as possible. But there are many aspects to the meaning of a single sentence. So why not begin with capturing just the Physical aspect – the physicality – in the meaning of the sentence?

For example, consider the 3 sentences : 

  • The flight landed at Mumbai airport.
  • John crashed onto the bed at night.
  • The ball fell onto the ground.

All the above three will be the same in that representation scheme since in all the cases something is touching something, coming from above.

The respective representations would be : 

Flight {touching + above} Mumbai airport.

John {touching + above} bed.

Ball {touching + above} ground.


The word-set/list of the Connectors in the scheme would be something like – 

{Touch}

{Split or cut}

{Adjacent – left / right / up / down} 

{Inside, outside / above / under or below}

{Volume+ (expand) / Volume- (contract)}

{Sheared}

{Parallel / skewed}

……etc. and their combinations.

Basically, all the standard terms we encounter in Mechanics.


Another example of representation – 

The book is on the table which is besides the sofa.

Book {left, right, up, down, inside, outside} table {left, right, up, down, inside, outside} sofa.  

So if I have to represent the arrangement of objects in my room, I just have to first bring forth the items, place the brace-bracket connector between pairs, and just “ON” the requisite switches.

If I “ON” a different combination of switches, I get a different possible arrangement in my room.

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Another broad Linguistically inspired, Commonsense-based Knowledge Representation scheme – 

We want the machine to interpret the given sentence. So why not move towards it by doing this?

Focus on the smallest group of words i.e. 2 i.e. a pair of words, coming together in a sentence.

Enumerate the kinds of pairs of words that occur together i.e. consecutively in a sentence. 

  1. Noun – verb : John (human) kicked, bullet (inanimate) travelled
  2. Verb – noun : travelling bullet, moving car
  3. Adj. – noun : smooth road
  4. Verb – adverb : moved fast 
  5. Adverb – verb : slowly captured

Now, write all that is there commonsensical-ly, to each pair !

For e.g. – Human – verb (type 1 above).

Human did an action

Human had an intention

It served a purpose for the human

Had an effect on the human

Had an effect on something on which the action was done

It cost the human something

Human most probably knew how to do it

….etc. 

(Also, take the dictionary meanings of the words and imbibe the aspects of the contents of the meanings into the ‘all that is there to the pair’. )

The above list will have to be human-brain-stormed and hand-coded into the machine. 

This will help towards the computer truly having a sense of understanding and interpretation of the contents of a sentence. 

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