Search for Hidden Knowledge in Collective Intelligence dealing Indeterminacy Ontology of Folksonomy with Linguistic Pragmatics and Quantum Logic

TL;DR

This paper explores methods to uncover and make explicit the tacit, hidden knowledge within collective intelligence systems by integrating folksonomies, linguistic pragmatics, and quantum logic into semantic search and information retrieval.

Contribution

It introduces a novel approach combining quantum-inspired vector space models and folksonomy analysis to reveal implicit knowledge in collaborative environments.

Findings

Quantum-inspired vector space models improve retrieval of tacit knowledge.

Folksonomy-based semantic search effectively uncovers hidden collective insights.

Prototype demonstrates enhanced visualization and retrieval in social network contexts.

Abstract

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.