AA>.... As an example, consider the
class of relationship, which can expressed by as many names as
‘connection’, ‘association’, ‘link’, ‘reference’, ‘regard’, ‘tie’,
‘bond’; or indicated by as many verbs as ‘to relate’, ‘associate’,
link’, ‘link up’, ‘connect’, ‘tie-in’, ‘colligate’, ‘refer’, pertain’,
‘concern’, ‘bear on’, etc. Or, take the class of events expressed by as
many words as ‘happening’, ‘occurrence’, ‘occurrent’, ‘contingency’,
‘outcome’, ‘effect’, ‘issue’, ‘upshot’, ‘result’, etc....
I don't know what class of relationship that is. Those terms do not
seem to make it clear to me. For
reason is reference and regards in the same group?
Some reason may be at hand but it may not be agreeable or pertinent.
There is a certain problem that issues from matters of agreement. We
call it disagreement. That is what motivates us all towards more
reliable methods of reaching suitable agreements using more reliable
distinctions and more pertinent knowledge.
In this regard, we
have extensive research into many languages and terminologies over more
than twenty years and we
have developed and tested very rich semantics. From the start we
always considered how to validate our understandings of certain
semantic features and characteristics, and moreover we were directly
concerned in its usefulness in advanced information systems.
We discovered that our understandings could not
be validated by linguists. Most other kinds of researchers and
scientists in computational
linguistics and artificial intelligence, had little time for something this far from their
believe that each thought reaction (every interpretation of a pattern)
can be seen as a (regular, repeating) relationship (emerging,
obtaining) between the regular forces and objects in the field of
psychophysiolgical impulses) and a
corresponding (universal) system of
and forces in the mind of the interpreter. This correspondence would be
observable as something like fidelity and relevance or pertinence
The conjecture is that repetitive sounds and visual clues organize our
world as it were. We determined to discover how that organizational
mechanism works. In terms of language, over the objections of many
linguists, we believe that the phonemes of any word are signs that
refer to abstract objects that are somehow related to the properties of
the object to which the word refers.
word X refers to object A
each phoneme P of word X refers to an abstract object BP
abstract object BP is related to property T of object A
Let me just show you how the natural concept of two-sidedness in
nature can be articulated with single distinctive and repetitive
sounds/clues. This can
be demonstrated using English language personal pronouns (I,
and he) that represent a universal
abstract category we call assignment:
Assignment can be seen as the abstract process necessary to
distinguishing identity (i.e., A=A). Polarity is here used to distinguish
the different persons in a sensory manner.
polarizes the first side (first person, I) and inversely
polarizes the second side (targets the second person, you (n,p)).
places equal polarity on both sides (first and second
person) and gives us the concept we and us.
polarity (no focus) on both sides (n,n)
(neither first nor second) expresses the third person, he.
Thereby, incredible as it may sound, we
found a logically complete way of distinguishing all things
by such process-polarity pairs. Next, we ask, is that understanding of
abstraction valid, is it really universal, and if so, is it useful?
believe that the human mind constantly interprets such abstract objects
and that the resulting interpretations also can be abstract objects
that may in turn be reinterpreted. Both the original abstract objects
and their successive interpretations are related to the properties of
the object to which the word refers.
abstract object BP is interpreted as abstract object B’P
abstract object B’P is related to property T’ of object A
In addition, we believe that the morphology of a word, its structure,
is also a sign that refers to an abstract object structure that is
somehow related to the structure of the object to which the word
refers. The human mind also constantly interprets and reinterprets this
abstract object structure.
structure of word X refers to an abstract object structure S
abstract object structure S is related to structural property TS of
abstract object structure S is interpreted as abstract object
abstract object structure S’ is related to structural property TS’ of
The repeated interpretation of the abstract objects to which the
phonemes of a word refer, in light of the repeated interpretation of
the abstract structure to which the morphology of that word refers,
will establish more and more relationships in the human mind to the
properties of the object to which that word refers. That is our story
of cognitive growth by reinterpretation.
We found that
thousands of three-consonant word roots of Old Arabic are in fact
structured signs that refer to triples of process-polarity pairs.
Higher-order process control precedence rules dictate control
structures within each triple, giving us root interpretation mappings.
We find that these process-polarity
pairs seem to organize the terms of modern
languages as well as the ancient semitic languages where we first
We found several rules that we
implemented as analytical tools for computing these relationships by
parsing them from texts or messages, or by measuring relations between
a specific question and its specific answer; between a query or text.
In order to provide examples, let us examine one rule, we found that we
Inward at Interface.
- If we have two bipoles of the same
category in a word stem, such as the case represented by the
and d in mold, middle and model, their affinity
causes them to combine into a dual-action tool with specific semantic
are four bipoles of this type that can be paired in six
ways (without repetition and without regard for sequence). If we
examine English terminology with a single pattern designated as (p,n)+(p,p) We have six
types of dual-action rules or tools indicated by these letters
in a stem structure (j+w,j+v,i+v,r+b,m+d,s+c).
Using the pattern, we have derived the
taxonomy and analyzed below eighteen (18) distinct semantic
classifications from all the
English word stems of this type that we extracted from a spelling
dictionary of more than thirty-thousand popular and often used words.
Terms of this type that are related to
mental activity include "brain", "mind",
Here is the rest of the analysis. As
many examples as could be found for each
of the abstract concepts are given. As you may notice many words are
assigned to many of the semantic classifications. Where we found
Arabic word roots representing a
variation as the English terms do, they are included in variation
titles (triple letters in
parenthesis represent 3500 year old Arabic roots, capital letters
represent Arabic consonants not available in the Roman alphabet):
1. Join and
Repeat--Multiple, Multiply, Coherent Group (rbO, srb)
branch, brigade, brother (multiple), scion
(breed), tandem (join two), tremendous (a
lot), modulus (multiples), democracy
(group rule), demography, demagogue
(group leader), comrade (of one's group), academy
(assembly), decimal (multiple, 10), December
(10), endemic (of certain group), algebra,
rabies (repeats joining=biting).
Connection--Discontinue, Break, Pieces (qTO, zbr)
debris, secede, sect (break away),
slice, modicum, dime
(piece), abrupt, abrogate.
Connection--Hurt, Cut, Scrape (grH, kST, sHg)
scar, scrape, abrade, abrasive, scratch,
sick (hurt), scare (damage pending), scissors,
incision, indemnity, malady.
condemn, malediction, demon.
Connection--Destroy (hdm, dmr)
doom, decimate, succumb,
murder, homicide, armageddon.
Possession--Take Away, Deprive
bare, barren, demote,
discriminate (both sc and dm).
Connection--Repair, Heal, Bridge (gbr)
remedy, redeem, bridge, medium
(bridge), mediate, arbitrate.
Interaction--Reaction, Dynamics, Social
association, burn (reaction with free
energy n), brand, rub, rubber
(keeps responding), scan (repeated sensing), dynamic,
drama, verb, adverb, acrobat,
6. Inward to
medium, mediate, meddle,
median, intermediate, moderate
(middle), abdomen (middle), meridian,
demi (half, middle).
7. Repeat Combining --
Structure, Construct, Building (rkb)
module, rib (structural part), modulate
(combine in a regular manner), model, mode,
modus, mold, modify (redo
combining), made, amend, dome
(building), domicile, domestic,
condominium, diagram, scheme,
sculpture (structure), dummy (constructed
figure), melody (repeated combination), madrigal,
8a. Repeat and
Continue--Pile up, Excess, Persist (brg, bVr, gbr, Cbr)
Burst (excessive stream
s), brutal (excessive attack t), burden
(pile-up), bear (persevere, pile up on oneself),
burgeon (overflow), bright, brilliant,
-berg (pile up), barricade (pile-up),
dominate (excess in applying force n), adamant
(persist), rebel (persistent negation l), mad
(excess), dam (pile-up), scream (excessive
sound), screech, robust (durable, persists),
mound, dumpy, boredom
(excess), doldrums, bedlam, acerbic.
8b. Repeat and
Continue--Smooth, Fine, Flow, Fluid (mrd)
Breeze, brook, humid,
damp, mud, meander (flow),
emerald (fine), dolomite, diamond,
jewel, dame (fine), damsel,
Convergence--Near, New (Qrb)
(near time), juvenile (new), precise
Convergence--Brief, Shrink (brd)
midget, timid (shrink), abridge,
10a. Back off from
Interface--Border, Limit, Restraint (Hjz)
border, brim, brow (border), brace
(restraint with structure c) and brake (restraint by applying
force k), dam (barring water), dampen,
smolder, dumb (limited speech or
intelligence), dummy (dumb), modest
(moral restraint), delimit, dimension
(limit), demure (restrained), demarcate,
10b. Back off from
Interface--Stay Aloof, Fly, Flee (hrb)
nomad (aloof from towns), seclude,
escape, abscond, timid (flees), abroad.
10c: Back off from
Interface--Before, Front, Ahead (Qdm, Qbl)
bra, brave (goes ahead).
10d. Back off from
Interface--Stand out, Emerge, Show (brz)
syndrome (what shows), prodrome, dream
Contact--Hit, Knock, Fight (Drb, Hrb, QrO, brQ)
bruise, dilemma, drum, amber
(chargeable by rubbing).
12. Enter Junction--Go
in, Take in, Drink (Srb, Qbl)
Bore (go in), breathe
(take in), bury (put in), beer (drink),
brew (make a drink), bar (place for drinks), admit
(take in), dimple (goes in), absorb, suck,
Connection--Extend, Fabric (zrb, mhd)
(fabric), ribbon (fabric), broad (extended), ivy
(repeats connection), branch (repeat connection), modem,
derm- (skin), drum (skin), denim,
damask, dimension (extent), diameter
(extent), domain (extent), demur
Equal--Reciprocate, Praise (Hmd)
(bartered), accommodate, scale (balance), medal
(reward), (re)commend, admire.
Commitment--Prescribe, Command, Contract, Hire (Oqd)
dominate, demand, mandate,
mandatory, administer, maid
(hired), commodore, admiral, baron.
Interface--Cover, Curtain, Protect (Hjb)
demon (hidden), secret, sacred
(protected), secure, dim
(cover, closure), bark (cover), barn.
scarce, scanty, seldom, demean,
18. Grab and
Using this kind of analysis over our
complete set of 32 process-polarity pairs, we are able to make a
complete analysis of the morphological and stem structures of the words
from many languages. A lot of work was needed to adjust for language
change and vague language when we began testing algorithms that could
take some concepts represented with English words (a question in
English) and identify possible answers in the Russian or Swedish
languages for example..
KE> Just the knowledge of the upper level
made things in the
If only it were that easy! Choose any dozen non-trivial words and look
up their definition in three dictionaries and you get confusing
accounts. Put a dozen expert and respected linguists in a room and ask
them to agree on the roots and origins of the dozen words you choose.
You will get a dozen perhaps conflicting accounts.
> lower and middle layers fit -- that, in my mind, may not have
> fit before; I learned. I did not alter my way of thinking
> in that I adapted to new facts.
JS> That is an important point: It's necessary to have guidance
on how to organize the categories of an ontology and how to
associate axioms with those categories. But that kind of
guidance could be obtained from a textbook, a set of design
tools, or a collection of examples.
The design tools for this case are
parsers, stemmers, lexicon, thesauri, NLP, etc.. I have found C-MAPS to
be excellent design tools, particularly in defining subsumption and
part-whole relations but it takes guidance and due consideration to
achieve the correct harmony of design criteria with the significant
features and characteristics to be specified.
Because pertinence of access to
organized knowledge, and the entire utility of such access and such
knowledge, is a kind of situational performance, there are measures
used to attest to that performance. Indexing, search and ranked
relevance retrieval demonstrate this kind of performance.
Determining what is relevant requires a capacity for recognizing
semiotic patterns that (tend to, or probably) distinguish the features
and characteristics considered relevant. Determining what will or
should constitute that relevance to others is not a trivial task.
There are ways of indexing keywords and
there are ways of indexing patterns and also relations between
patterns. Using a collection of examples is how Bayesian-based
and retrieval systems are trained to learn the patterns of the texts
well enough to find hit documents for queries or to perform
classification. The performance of such systems is measured by recall
and precision tests.
To test our findings and ideas, we created automatic indexing methods
that accept undefined text as input and output a latent semantic table.
No training is done.
<AA> ... The goal of ontology is to
formulate the overall patterns and fundamental laws of the universe,
while its role is to set the world models, rules, and reasoning
algorithms for advanced information technology. <snip>
inputs are compiled into compact signatures using a
special meta-language made up of our logically complete set of
pairs, and a language reference assigning about 12,000 English language
words to a finite set of a little more than 2000 root linguistic forms
and constants we chose to more completely define for use as situational
indexicals in our propositional methods.
The result is essentially a binary table that allows for the fast
and dynamic computations of relations of concepts and terms (columns)
documents (rows), all according to our methods. That makes this a form
of latent semantic
indexing, combined with analytical modeling, which is commonly defined
as "advanced information technology:".
In addition to deterministic concept and word indexing methods, based
our studies of terminologies and vocabulary, we developed a model of
knowledge extraction for the reliable identification of the instances
of specified type/subtypes in the records forming a collection. We
also added a logic framework for using Boolean and Horn logic in the
specification of types/subtypes.
In independent relevance tests, designed and hosted by the National
Institutes of Standards and Technologies (NIST) at the annual Text
REtrieval Conference, we showed that our rather deterministic model
could outperform systems based on Bayesian, keyword or those using any
other methods. The results of our performance at TREC-8 (1999) is
available at: ftp://ftp.www.readware.com/Software/Support/T8MITi.pdf
It is six pages that speaks volumes about the computational model we
developed for organizing the
knowledge (compiled from texts). The knowledge was organized for the
purpose of addressing various requests (compiled from plain
queries specified with a small set of commands). The objective is to
determine which few of about two million candidate documents of various
subjects and kinds, are pertinent to requests formulated by the
judges themselves. They should know best what they intended with their
request on what they deem relevant.
It is useful also to look at the NIST publications over the entire
conference. There you will find that in terms of relevance, these
methods captured five hundred (500) times more pertinent documents to
detailed and sophisticated queries than all other participating
systems. I would say, statistically speaking, that is significant.
Proceedings of the 8th annual Text Retrieval Conference
I am not saying that search, indexing and retrieval give us all the
answers, I am just showing how our model of relevance was developed and
validated in a text retrieval context and suggesting therefore it would
be useful for other reasoning tasks as well.