Information Technology Reference
In-Depth Information
3.2.4.2 Repeat from step 3.2.3
3.2.5 Take the resulting sequence of filtering as a current representation of the
knowledge about and ontology of the text
The learned E
ci
's ontology is distributed along the chain of E
ce
s.
3.2.6 Take the resulting E
ci
as the internal representation of the new text event
The specific acquired, concrete knowledge is now condensed in the E
ci
. This provides a good
representation of the original text and its keywording (López De Luise, 2005).
Real texts include contradictions and ambiguities. As previously shown (López De Luise,
2007b), they are processed and handled despite potentially inadequate contextual
information. The algorithm does not include detailed clause analysis of or encode linguistic
knowledge about the context because these components complicate the process and make it
less automatic.
Furthermore, using the p
o
metric can distinguish the following Writing Profiles: general
document, Web forum, Web index and blogs. This metric is therefore independent of
document size and mentioned text styles (López De Luise, 2007c). Consequently, it is useful
to define the quality of the text that is being learned and to decide whether to accept it as a
source of knowledge.
3.3 Gelernter's perspective on reasoning
Section 3.2.3. defines that the clustering algorithms must be used first hard clusterings and
afterwards fuzzy. It is not a trivial restriction. Its goal is to organize learning across a range
from specific concrete data to abstract and fuzzy information. The filters are therefore
organized as a sequence from simple k-means clustering to fuzzy clustering. This approach
is compatible with Gelernter's belief that thinking is not a static algorithm that applies to
every situation. Thinking requires a set of diverse algorithms that are not limited to
reasoning. Some of these algorithms are sharp and deep, allowing clear manipulation of
concrete objects, but there are other algorithms with different properties.
David Gelernter Theory (Gelernter, 2010) states that thinking is not the same as reasoning.
When your mind wanders, you are still thinking. Your mind is still at work. This free
association is an important part of human thought. No computer will be able to think like a
man unless it can perform free association.
People have three common misconceptions:
3.3.1 The belief that “thinking” is the same as “reasoning”
There are several activities in the mind that are not reasoning. The brain keeps working even
when the mind is wandering.
3.3.2 The belief that reality and thoughts are different and separated things
Reality is conceptualized as external while the mental landscape created by thoughts is seen
as internal and mental. According to Gelernter, both are essentially the same although the
attentional focus varies.
