Towards an Increase of Collective Intelligence within Organizations Using Trust and Reputation Models

Scarlat, Emil; Maries, Iulia

doi:10.1007/978-3-642-04441-0_12

Cited by 21 publications

(9 citation statements)

References 5 publications

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“…Only Nandi & Bernstein have proposed a technique which was based on logs from virtual shops for computing similarity between products [26]. However, a number of works have addressed the semantic similarity measurement [16], [28], [30], [34], [35], and the use of WI techniques for solving computational problems [19], [36], [37] separately.…”

Section: Related Workmentioning

confidence: 99%

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Semantic similarity measurement using historical google search patterns

Martinez‐Gil¹,

Montes²

2017

Preprint

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Computing the similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do that, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.

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Section: Related Workmentioning

confidence: 99%

“…On the other hand, Collective Intelligence (CI) is an active field of research that explores the potential of collaborative work in order to solve complex problems [36]. Scientists from the fields of sociology, mass behavior, and computer science have made important contributions to this field.…”

mentioning

confidence: 99%

Semantic similarity measurement using historical google search patterns

Martinez‐Gil¹,

Montes²

2017

Preprint

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show abstract

“…According to Luft (2010), the CI concept assumes that individuals constantly adapt to their environment. They collaborate to survive, so in this case, CI is a collaboration process created through intelligence (knowledge) based interaction of individuals (O'Reilly, 2005;Gregg, 2010;Gibson, 2006;Scarlat and Măries, 2009;Flett, 2010). Nguyen (2008) ads competition to this understanding, as during competition excessive amount of information and knowledge is shared in order to be over the competitor.…”

Section: The Concept Of Collective Intelligencementioning

confidence: 99%

“…Collective intelligence (hereinafter -CI) is a fundamentally different way of viewing how applications can support human interaction and decision making (Gregg, 2010). The CI is trying to suggest another way of thinking about effectiveness, profitability or teamwork in the knowledge societies (Scarlat and Măries, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…The CI can improve competitiveness within organizations in the context of a global market and collective performance has become a critical factor in the organization's development (Scarlat and Măries, 2009). As a collective is more intelligent than one single member (Nguyen, 2008) and group intelligence is not strongly tied to either the average intelligence of the members or the team's smartest member (Johnson, 2010), it is worthy to research CI's possibilities in the public sector, where decisions, affecting a mass of people, are being made every day.…”

Section: Introductionmentioning

confidence: 99%

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