Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Search for Hidden Knowledge in Collective Intelligence dealing
  Indeterminacy Ontology of Folksonomy with Linguistic Pragmatics and Quantum
  Logic   Information retrieval is not only the most frequent application executed on
the Web but it is also the base of different types of applications. Considering
collective intelligence of groups of individuals as a framework for evaluating
and incorporating new experiences and information we often cannot retrieve such
knowledge being tacit. Tacit knowledge underlies many competitive capabilities
and it is hard to articulate on discrete ontology structure. It is unstructured
or unorganized, and therefore remains hidden. Developing generic solutions that
can find the hidden knowledge is extremely complex. Moreover this will be a
great challenge for the developers of semantic technologies. This work aims to
explore ways to make explicit and available the tacit knowledge hidden in the
collective intelligence of a collaborative environment within organizations.
The environment was defined by folksonomies supported by a faceted semantic
search. Vector space model which incorporates an analogy with the mathematical
apparatus of quantum theory is adopted for the representation and manipulation
of the meaning of folksonomy. Vector space retrieval has been proven efficiency
when there isn't a data behavioural because it bears ranking algorithms
involving a small number of types of elements and few operations. A solution to
find what the user has in mind when posing a query could be based on "joint
meaning" understood as a joint construal of the creator of the contents and the
reader of the contents. The joint meaning was proposed to deal with vagueness
on ontology of folksonomy indeterminacy, incompleteness and inconsistencies on
collective intelligence. A proof-of concept prototype was built for
collaborative environment as evolution of the actual social networks (like
Facebook, LinkedIn,..) using the information visualization on a RIA application
with Semantic Web techniques and technologies.

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The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org