Thieme Hennis's Library tagged collective-intelligence →  View Popular

This paper investigates empirically for the first time the impact of experts' agreement on voter turnout. Data were obtained when voters, advised by four independent experts, had to decide on the next move against a chess world champion. Analyzing total voter turnout as well as individual voters' behavior in a panel data subsample, and employing OLS median and ARMAX regression techniques, we find that the degree of expert agreement matters for voter turnout. Higher voting costs are also shown to have a statistically significant negative impact on participation.

www.sciencedirect.com/science - Preview

collective-intelligence collaboration research

How can people and computers be connected so that -collectively- they act more intelligently than any individuals, groups, or computers have ever done before?

The Center for Collective Intelligence brings together faculty from across MIT to conduct research on how new communications technologies are changing the way people work together.

cci.mit.edu - Preview

MIT collective-intelligence research

abstract of the international conference april 27-29, 2007 @ mit about
"Collaboration and Collective Intelligence". some interesting resources and issues.

web.mit.edu/...MiT5_plenary2.html - Preview

MIT conferences collaboration collective-intelligence

The handbook consists of three main Parts: Theory, Case studies and Howto. Additionally, two appendices contain descriptions of software packages and student projects. Finally, the book has a bibliography with 400 references to related work. The first Part introduces the involved subtopics of collective intelligence - for example, self organisation, emergence, analysis and design. We also explain why and how to use simulation for researching collective intelligence. Finally, we give an overview of involved models and techniques to be used for analysis and design studies. The second Part overviews 27 research studies into the analysis and design of collective intelligence - including, for example, artificial societies, agent-based computational economics, collective robotics and insect-based computing. The third Part explains how to conduct a scientific simulation study and how to describe it in a report.

sci.collectivae.net - Preview

self-organization collective-intelligence research manual books simulation

interesting blog post about the need for syndicated web ratings.

blog.citizendium.org/...gs-an-idea-whose-time-has-come - Preview

collective-intelligence quality knowledge-management self-organization

One of the main approaches for creating metadata for learn-
ing resources in adaptive e-learning systems has been through the use of
semantic web ontologies. This approach is limiting because it doesn’t usu-
ally address a requirement for the support of annotators or the require-
ment for significant effort by annotators in learning ontological metadata
domains and technologies. This paper proposes a method of addressing
these shortfalls, by incorporating techniques used on the hugely popular
collaborative tagging websites, such as del.icio.us
1
. By extending on a
natural language ontology, we aim to achieve simplicity in metadata au-
thoring while maintaining the expressiveness of a lightweight ontology.
The goal of the approach is to facilitate metadata creation such that
new metadata creators (such as students) may become involved with
creating machine consumable metadata about learning objects, for use
in adaptive e-learning systems.

www.win.tue.nl/...oks_mccalla_SWEL2006_final.pdf - Preview

tagging folksonomies knowledge-management paper collective-intelligence metadata semantic search learning learningobjects

User profiles can be used to identify persons inside a community with
similar interests. Folksonomy systems allow users to individually tag the objects of a common set (e.g., web pages). In this paper, we propose to create user profiles from the data available in such folksonomy systems by letting users specify the most relevant objects in the system. Instead of using the objects directly to represent the user profile, we propose to use the tags associated with the specified objects to build the user profile. We have designed a prototype for the research domain to use such tag-based profiles in finding persons with similar interests.
The combination of tag-based profiles with standard recommender system technology has resulted in a new kind of recommender system to recommend related publications, keywords, and persons.

ftp.informatik.rwth-aachen.de/...paper41.pdf - Preview

tagging folksonomies knowledge-management paper collective-intelligence metadata semantic search

This paper explores the system of categories that is used to classify articles in Wikipedia. It is compared to collaborative tagging systems like del.icio.us and to hierarchical classification like the Dewey Decimal Classification (DDC). Specifics and commonalitiess of these systems of subject indexing are exposed. Analysis of structural and statistical properties (descriptors per record, records per descriptor, descriptor levels) shows that the category system of Wikimedia is a thesaurus that combines collaborative tagging and hierarchical subject indexing in a special way.

arxiv.org/0604036 - Preview

tagging folksonomies knowledge-management paper collective-intelligence metadata semantic search wikipedia

Tagging has arisen as way to enable users to contribute to a loose
taxonomy characterizing web pages, pictures, products and other
things. We propose tagging people in order to help individuals
keep track of each other while contributing to a loose
characterization of their friends and colleagues. “Fringe
Contacts” is a reference system designed to test whether people-
tagging is a viable and useful approach.

tlau.org/...www06-tagging-fc.pdf - Preview

tagging folksonomies knowledge-management paper collective-intelligence metadata semantic search

This paper presents a novel approach to Tag-Cloud’s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.

nosolousabilidad.com/...improving_tagclouds.pdf - Preview

visualization tagging folksonomies knowledge-management paper collective-intelligence metadata semantic search

Yahoo! employee describes and defines criteria and algorithms for a collaborative tag system: Since tags are created by
individual users in a free form, one important problem facing
tagging is to identify most appropriate tags, while eliminating
noise and spam. For this purpose, we define a set of general
criteria for a good tagging system. These criteria include high
coverage of multiple facets to ensure good recall, least effort to
reduce the cost involved in browsing, and high popularity to
ensure tag quality. We propose a collaborative tag suggestion
algorithm using these criteria to spot high-quality tags.

www.semanticmetadata.net/...13.pdf - Preview

tagging folksonomies knowledge-management paper collective-intelligence metadata semantic search

This paper gives an overview of current trends in manual indexing on the Web. Along with a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization systems or as something completely new. This paper shows that tagging should better be seen as a popular form of manual indexing on the Web. Difference between controlled and free indexing blurs with sufficient feedback mechanisms. A revised typology of tagging systems is presented that includes different user roles and knowledge organization systems with hierarchical relationships and vocabulary control. A detailed bibliography of current research in collaborative tagging is included.

arxiv.org/0701072v2 - Preview

tagging folksonomies knowledge-management paper collective-intelligence metadata semantic search

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.

arxiv.org/0508082 - Preview

collaboration tagging research article search collective-intelligence metadata semantic

We preserve the best of the old - books by leading experts that are rigorously reviewed and developed to the highest standards. Then we flip it all on its head. Our books are free online. We offer convenient, low-cost choices for students – print, audio, by-the-chapter, and more. Our books are open for instructors to mix, mash, and make their own. Our books are the hub of a social learning network where students learn from the book and each other.

www.flatworldknowledge.com - Preview

knowledge collective-intelligence oer oer-initiative open-education open_access

cool blogpost explaining Wisdom of Crowds, a criticism on how it is conceived by many.

headrush.typepad.com/...the_dumbness_of.html - Preview

blogpost collective-intelligence web2.0