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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|How collective intelligence emerges: Knowledge creation process in Wikipedia from microscopic viewpoint||Kangpyo Lee||Proceedings of the Workshop on Advanced Visual Interfaces AVI||English||2014||The Wikipedia, one of the richest human knowledge repositories on the Internet, has been developed by collective intelligence. To gain insight into Wikipedia, one asks how initial ideas emerge and develop to become a concrete article through the online collaborative process? Led by this question, the author performed a microscopic observation of the knowledge creation process on the recent article, "Fukushima Daiichi nuclear disaster." The author collected not only the revision history of the article but also investigated interactions between collaborators by making a user-paragraph network to reveal an intellectual intervention of multiple authors. The knowledge creation process on the Wikipedia article was categorized into 4 major steps and 6 phases from the beginning to the intellectual balance point where only revisions were made. To represent this phenomenon, the author developed a visaphor (digital visual metaphor) to digitally represent the article's evolving concepts and characteristics. Then the author created a dynamic digital information visualization using particle effects and network graph structures. The visaphor reveals the interaction between users and their collaborative efforts as they created and revised paragraphs and debated aspects of the article.||0||0|
|ICHPEDIA, a case study in community engagement in the safeguarding of ICH online||Park S.C.||International Journal of Intangible Heritage||English||2014||This article presents a new paradigm of safeguarding methods through digital platforms and technology. Since 2010, a group of researchers in Korea have been developing a new experimental methodology of inventorying intangible cultural heritage (ICH) utilising a new concept of collective intelligence and advanced information technologies. The research team established Ichpedia, a web-based ICH encyclopedia and archive. The purpose of Ichpedia is four fold. First, it functions as the most efficient digital ICH inventorying system available using modern information technologies. It is possible to record and retain the dynamic features of ICH through the use of multi-media resources. Secondly, Ichpedia can facilitate interactivity between information providers and users so that ICH communities, groups and individuals can directly access the system as information providers or editors. Using the functions of the system, their voices can easily be disseminated to the public. Such cooperative work will encourage more awareness and identification of the fragility of ICH. Ichpedia will therefore be instrumental in improving the understanding of ICH communities and individuals and finding better safeguarding methods. Thirdly, Ichpedia can reduce the economic burden of establishing a highly efficient database system. It is easy and simple to use but offers high efficiency compared to other web-based ICH encyclopedias worldwide. Ichpedia has the advantage of being the least expensive option for the development and maintenance of such a system. Lastly, it is hoped that Ichpedia will pave the way for digital innovation in the area of ICH recording with the free and open distribution of the digital platform and technologies.||0||0|
|Determinants of collective intelligence quality: Comparison between Wiki and Q&A services in English and Korean users||Joo J.
|Service Business||English||2013||Although web-enabled collective intelligence (CI) plays a critical role in organizational innovation and collaboration, the dubious quality of CI is still a substantial problem faced by many CI services. Thus, it is important to identify determinants of CI quality and to analyze the relationship between CI quality and its usefulness. One of the most successful services of web-enabled CI is Wikipedia accessible all over the world. Another type of CI service is Naver KnowledgeiN, a typical and popular CI site offering question and answer (Q&A) services in Korea. Wikipedia is a multilingual and web-based encyclopedia. Thus, it is necessary to study the influence relationships among CI quality, its determinants, and CI usefulness according to different CI type and languages. In this paper, we propose a new research model reflecting multi-dimensional factors related to CI quality from user's perspective. To test a total of 15 hypotheses drawn from the research model, a total of 691 responses were collected from Wikipedia and KnowledgeiN users in South Korea and US. Expertise of contributors, community size, and diversity of contributors were identified as determinants of perceived CI quality. Perceived CI quality has significantly influenced on perceived CI usefulness from user's perspective. CI type and different language partially play a role of moderators. The expertise of contributors plays a more important role in CI quality in the case of Q&A services such as KnowledgeiN compared to Wiki services such as Wikipedia. This implies that Q&A service requires more expertise and experiences in particular areas rather than the case of Wiki service to improve service quality. The relationship between community size and perceived CI quality was different according to CI type. The community size has a greater effect on CI quality in case of Wiki service than that of Q&A service. The number of contributors in Wikipedia is important because Wiki is an encyclopedia service which is edited and revised repeatedly from many contributors while the answer given in Naver KnowledgeiN cannot be edited by others. Finally, CI quality has a greater effect on its usefulness in case of Wiki service rather than Q&A service. In this paper, we suggested implications for practitioners and theorists.||0||0|
|Identifying, understanding and detecting recurring, harmful behavior patterns in collaborative wikipedia editing - Doctoral proposal||Flock F.
|WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web||English||2013||In this doctoral proposal, we describe an approach to identify recurring, collective behavioral mechanisms in the collaborative interactions of Wikipedia editors that have the potential to undermine the ideals of quality, neutrality and completeness of article content. We outline how we plan to parametrize these patterns in order to understand their emergence and evolution and measure their effective impact on content production in Wikipedia. On top of these results we intend to build end-user tools to increase the transparency of the evolution of articles and equip editors with more elaborated quality monitors. We also sketch out our evaluation plans and report on already accomplished tasks.||0||0|
|Modeling and simulation on collective intelligence in future internet-A study of wikipedia||Du S.
|Information Technology Journal||English||2013||Under the background of Web 2.0, network's socialization generates collective intelligence which can enrich human beings wisdom. However, what is the main factor that influences the performance of this behavior is still in research. In this study, the effect of number of Internet users that is represented by quantity, quality and variety of User-generated Content (UGC) is brought forward. Regarding Wikipedia as a study case, this study uses Agent-based modeling methodology and real data of Wikipedia for about 10 years to establish and simulate the model. The results verify that the size of group is indeed a necessary condition to generate collective intelligence. When the number of participants in Wikipedia reaches about 400000, the quantity of UGC increases exponentially, the quality of UGC reaches a satisfactory level and the variety of UGC can be guaranteed. This insight gives significance to show when mass collaboration will lead to collective intelligence which is an innovation than before.||0||0|
|Social computing: Its evolving definition and modeling in the context of collective intelligence||Yoshifumi Masunaga||Proceedings of the 2012 ASE International Conference on Social Informatics, SocialInformatics 2012||English||2013||gSocial computingh is a keyword in contemporary society. However, if we ask anew what the term social computing means, we realize that its definition, meaning and modeling have not necessarily been clarified. This paper first investigates when questions are raised about gsocial computing.h We find that the oldest Wikipedia article on social computing was written on January 21, 2005. However, it is found that a major rewrite was done on October 17, 2007, which caused a great change in its definition. It seems that the reason for this change is the idea of collective intelligence that has been popularized in James Surowiecki's book, The Wisdom of Crowds. In order to examine how the concept of social computing is accepted by and has infiltrated the web society, we performed an analysis of the search engine results page (SERP) using Google, specifying the search keyword as gsocial computing.h This paper investigates a formal model of social computing, which is described in contrast with the traditional computing scheme. Based on this model, we investigate the relationship between social computing and computer science, and we conclude that the Wikipedia article on social computing that states gsocial computing is a general term for an area of computer science ch is inaccurate.||0||0|
|Mass Collaboration or Mass Amateurism? A comparative study on the quality of scientific information produced using Wiki tools and concepts||Fernando Rodrigues||Universidade Évora||Portuguese||December 2012||With this PhD dissertation, we intend to contribute to a better understanding of the Wiki phenomenon as a knowledge management system which aggregates private knowledge. We also wish to check to what extent information generated through anonymous and freely bestowed mass collaboration is reliable as opposed to the traditional approach.
In order to achieve that goal, we develop a comparative study between Wikipedia and Encyclopaedia Britannica with regard to accuracy, depth and detail of information in both, in order to confront the quality of the knowledge repository produced by them. That will allow us to reach a conclusion about the efficacy of the business models behind them.
We will use a representative random sample which is composed by the articles that are comprised in both encyclopedias. Each pair of articles was previously reformatted and then graded by an expert in its subject area. At the same time, we collected a small convenience sample which only integrates Management articles. Each pair of articles was graded by several experts in order to determine the uncertainty associated with having diverse gradings of the same article and apply it to the evaluations carried out by just one expert. The conclusion was that the average quality of the Wikipedia articles which were analysed was superior to its peers’ and that this difference was statistically significant.
An inquiry was conducted within the academia which certified that traditional information sources were used by a minority as the first approach to seeking information. This inquiry also made clear that reliance on these sources was considerably larger than reliance on information obtained through Wikipedia. This quality perception, as well as the diametrically opposed results of its evaluation through a blind test, reinforces the evaluating panel’s exemption.
However much the chosen sample is representative of the universe to be studied, results have depended on the evaluators’ personal opinion and chosen criteria. This means that the reproducibility of this study’s conclusions using a different grading panel cannot be guaranteed. Nevertheless, this is not enough of a reason to reject the study results obtained through more than five hundred evaluations.This thesis is thus an attempt to help clarifying this topic and contributing to a better perception of the quality of a tool which is daily used by millions of people, of the mass collaboration which feeds it and of the collaborative software that supports it.
|A model for information growth in collective wisdom processes||Sanmay Das
|ACM Transactions on Knowledge Discovery from Data||English||2012||Collaborative media such as wikis have become enormously successful venues for information creation. Articles accrue information through the asynchronous editing of users who arrive both seeking information and possibly able to contribute information. Most articles stabilize to high-quality, trusted sources of information representing the collective wisdom of all the users who edited the article. We propose a model for information growth which relies on two main observations: (i) as an article's quality improves, it attracts visitors at a faster rate (a rich-get-richer phenomenon); and, simultaneously, (ii) the chances that a new visitor will improve the article drops (there is only so much that can be said about a particular topic). Our model is able to reproduce many features of the edit dynamics observed on Wikipedia; in particular, it captures the observed rise in the edit rate, followed by 1/t decay. Despite differences in the media, we also document similar features in the comment rates for a segment of the LiveJournal blogosphere.||0||0|
|Collective intelligence model: How to describe collective intelligence||Georgi S.
|Advances in Intelligent and Soft Computing||English||2012||A large number of scientific research exists, describing forms of collective intelligence (e.g. Wikipedia). But there are only few publications that describe how different forms of collective intelligence be described in general. In this paper, we therefore describe an approach how to characterise different forms of collective intelligence. We draw from existing research and build a comprehensive model and identify further characteristics to describe collective intelligence in a fine-grained manner. We propose a model with different characteristics, like form of cooperation, organisational pattern, and decision making process, which distinctively describe forms of collective intelligence and suggest possible attribute values.||0||0|
|Community optimization: Function optimization by a simulated web community||Veenhuis C.B.||International Conference on Intelligent Systems Design and Applications, ISDA||English||2012||In recent years a number of web-technology supported communities of humans have been developed. Such a web community is able to let emerge a collective intelligence with a higher performance in solving problems than the single members of the community. Based on the successes of collective intelligence systems like Wikipedia, the web encyclopedia, the question arises, whether such a collaborative web community could also be capable of function optimization. This paper introduces an optimization algorithm called Community Optimization (CO), which optimizes a function by simulating a collaborative web community, which edits or improves an article-base, or, more general, a knowledge-base. In order to realize this, CO implements a behavioral model derived from the human behavior that can be observed within certain types of web communities (e.g., Wikipedia or open source communities). The introduced CO method is applied to four well-known benchmark problems. CO significantly outperformed the Fully Informed Particle Swarm Optimization as well as two Differential Evolution approaches in all four cases especially in higher dimensions.||0||0|
|Edit conflict resolution in wikiBOK: A wiki-based bok formulation-aid system for new disciplines||Yoshifumi Masunaga
|Proceedings - 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust and 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012||English||2012||A body of knowledge (BOK) of an academic field is an indispensable aid not only to people's understanding of the entirety of a targeted academic field, but also to designing a perfect curriculum of that field for educational purposes. However, in contrast to a BOK for a mature discipline such as computer science, the formulation of a BOK for a new discipline, such as social informatics, life science, and sustainability science, is difficult because academics in such a new discipline cannot present it in its entirety par advance. Therefore, a bottom-up and open collaborative approach based on collective intelligence seems promising, and contrasts strongly with the traditional style in which a BOK is formulated: by the authorities in the field in a top-down manner. WikiBOK is a wiki-based body of knowledge (BOK) formulation-aid system for new disciplines. It is developed based on BOK+, which is a novel BOK formulation principle for new disciplines that enables us to construct a BOK in a bottom-up manner. As its name indicates, WikiBOK uses Semantic Media Wiki (SMW) to facilitate its fundamental functions. A rich graphical user interface is provided using open source graph visualization software. The main objective of this paper is to illustrate how edit conflicts are resolved in WikiBOK. Needless to say, edit conflicts are unavoidable when WikiBOKers collaborate to formulate a BOK-tree. A WikiBOK edit conflict resolution principle is shown, and the WikiBOK Edit Conflict Resolver is implemented based on this principle. Social Informatics BOK (SIBOK) is under construction using WikiBOK.||0||0|
|Knowledge expansion support by related search keyword generation based on wikipedia category and pointwise mutual information||Kawauchi S.
|Journal of Advanced Computational Intelligence and Intelligent Informatics||English||2012||When users use search engines to acquire knowledge on certain subjects in unknown domains, they often refer to the related search keywords that are generated on the frequency of use as search keywords. However, such searches by reference to related search keywords may not always turn out to be useful for the expansion of knowledge on the research subjects. We, therefore, propose a new method to generate related search keywords by means of Wikipedia. In the proposed method, users first searchWikipedia pages of the same title with the queries input by users to extract information on the category of the pages. Next, obtain the sets of pages that fall into the category and extract related page groups from the pages contained in any plural product sets of pages. Then, calculate pointwise mutual information or tf-idf for the keywords extracted from each page to make either information of higher values associated with search keywords. We have confirmed effectiveness of the proposed method through comparison with related search keywords generated by Google as well as through subjective evaluation experiments.||0||0|
|Survey on statics of Wikipedia||Deyi Li
|Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University||Chinese||2012||This paper mainly focuses on the Wikipedia, a collaborative editing pattern in Web 2. 0. The articles, editors and the editing relationships between the two ones are three important components in Wikipedia statistical analysis. We collected different kinds of statistical tools, methods and results, and further analyzed the problems in the current statistics researches and discussed the possible resolutions.||0||0|
|A Framework for Adopting Collaboration 2.0 Tools for Virtual Group Decision Making||Turban E.
|Group Decision and Negotiation||English||2011||Decision making in virtual teams is gaining momentum due to globalization, mobility of employees, and the need for collective and rapid decision making by members who are in different locations. These factors resulted in a proliferation of virtual team software support tools for decision making, the latest of which is social software (also known as collaboration 2.0), which includes tools such as wikis, blogs, microblogs, discussion forums, and social networking platforms. This paper describes the potential use of collaboration 2.0 software for improving the process and the specific tasks in virtual group decision making. The paper proposes a framework for exploring the fitness between social software and the major activities in the group decision making process and how such tools can be successfully adopted. Specifically, we use a fit-viability model to help assessing whether social software fit a decision task and what organizational factors are important for such tools to be effective. Representative research issues related to the use of such tools are also presented. © 2010 Springer Science+Business Media B.V.||0||0|
|A Research for the Centrality of Article Edit Collective in Wikipedia||Dongjie Zhao
|Quality of articles in Wikipedia||Deyi Li
|Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University||Chinese||2011||The recent research of wikipediais is firs briefly analyzed, especially on the statistics of quality of articles in Wikipedia. Then the automatic evaluating methods of article quality are discussed. The methods mainly include two kinds: the correlation-based analysis and cooperation modeling. Furthermore, we present the open problems of automatic quality evaluation and the possiblepromotions of collective intelligence.||0||0|
|Technology-mediated social participation: The next 25 years of HCI challenges||Shneiderman B.||Lecture Notes in Computer Science||English||2011||The dramatic success of social media such as Facebook, Twitter, YouTube, blogs, and traditional discussion groups empowers individuals to become active in local and global communities. Some enthusiasts believe that with modest redesign, these technologies can be harnessed to support national priorities such as healthcare/wellness, disaster response, community safety, energy sustainability, etc. However, accomplishing these ambitious goals will require long-term research to develop validated scientific theories and reliable, secure, and scalable technology strategies. The enduring questions of how to motivate participation, increase social trust, and promote collaboration remain grand challenges even as the technology rapidly evolves. This talk invites researchers across multiple disciplines to participate in redefining our discipline of Human-Computer Interaction (HCI) along more social lines to answer vital research questions while creating inspirational prototypes, conducting innovative evaluations, and developing robust technologies. By placing greater emphasis on social media, the HCI community could constructively influence these historic changes.||0||0|
|A resource allocation framework for collective intelligence system engineering||Vergados D.J.
|Proceedings of the International Conference on Management of Emergent Digital EcoSystems, MEDES'10||English||2010||In this paper, we present a framework for engineering collective intelligence systems that will be used by web communities. The proposed framework enables the development of communitydriven, self-regulating CI systems, which adapt their functionality to the activity and goals of the web community. The above engineering methodology is applied on the design of a popular web system, namely Wikipedia, to illustrate the way that the functionality of the latter could be improved, in terms of better and more prompt article quality production. The preliminary evaluation results of this application, obtained through simulation modeling are promising. Copyright 2010 ACM.||0||0|
|A wiki-based collective intelligence approach to formulate a Body of Knowledge (BOK) for a new discipline||Yoshifumi Masunaga
|WikiSym 2010||English||2010||This paper describes a wiki-based collective intelligence approach to provide a system environment that enables users to formulate a body of knowledge (BOK) for a new discipline, such as social informatics. When the targeted discipline is mature, for example, computer science, its BOK can be straightforwardly formulated by a task force using a top-down approach. However, in the case of a new discipline, it is presumed that nobody has a comprehensive understanding of it; therefore, the formulation of BOK in such a field can be carried out using a bottom-up approach. In other words, a collective intelligence approach supporting such work seems promising. This paper proposes the BOK+ which is a novel BOK formulation principle for new disciplines. To realize this principle, the BOK Constructor is designed and prototyped where Semantic MediaWiki (SMW) is used to provide its basic functions. The BOK Constructor consists of a BOK Editor, SMW, Uploader, and BOK Miner. Most of the fundamental functions of the BOK Constructor, with the exception of the BOK Miner, were implemented. We validated that the BOK Constructor serves its intended purpose.||0||0|
|Beyond Wikipedia: Coordination and Conflict in Online Production Groups||Aniket Kittur
Robert E. Kraut
|Computer-Supported Cooperative Work||English||2010||Online production groups have the potential to transform the way that knowledge is produced and disseminated. One of the most widely used forms of online production is the wiki, which has been used in domains ranging from science to education to enterprise. We examined the development of and interactions between coordination and conflict in a sample of 6811 wiki production groups. We investigated the influence of four coordination mechanisms: intra-article communication, inter-user communication, concentration of workgroup structure, and policy and procedures. We also examined the growth of conflict, finding the density of users in an information space to be a significant predictor. Finally, we analyzed the effectiveness of the four coordination mechanisms on managing conflict, finding differences in how each scaled to large numbers of contributors. Our results suggest that coordination mechanisms effective for managing conflict are not always the same as those effective for managing task quality, and that designers must take into account the social benefits of coordination mechanisms in addition to their production benefits.||0||4|
|Collective wisdom: Information growth in wikis and blogs||Sanmay Das
|Proceedings of the ACM Conference on Electronic Commerce||English||2010||Wikis and blogs have become enormously successful media for collaborative information creation. Articles and posts accrue information through the asynchronous editing of users who arrive both seeking information and possibly able to contribute information. Most articles stabilize to high quality, trusted sources of information representing the collective wisdom of all the users who edited the article. We propose a model for information growth which relies on two main observations: (i) as an article's quality improves, it attracts visitors at a faster rate (a rich get richer phenomenon); and, simultaneously, (ii) the chances that a new visitor will improve the article drops (there is only so much that can be said about a particular topic). Our model is able to reproduce many features of the edit dynamics observed on Wikipedia and on blogs collected from LiveJournal; in particular, it captures the observed rise in the edit rate, followed by 1/t decay.||0||0|
|The implications of information democracy and digital socialism for public libraries||Oguz E.S.
|Communications in Computer and Information Science||English||2010||In these times, public libraries in many countries have increasingly come under pressure from developments within the information landscape. Thus, not least because of the massive digitization of information resources, the proliferation and popularity of search engines, in particular Google, and the booming technologies of Web 2.0, public libraries find themselves in a very complex situation. In fact, the easy-to-use technologies of Web 2.0 challenge the basic principles of information services provision undertaken by libraries. The new digital information environment and social software tools such as blogs, wikis and social networking sites have fuelled a discussion of the future of public libraries as information providers. After all there seems to be a need for public libraries to reorient their aims and objectives and to redefine their service identity. At the same time search engines, and especially Google, are increasingly coming under scrutiny. Thus, analysis results referred to show that the conception of information and the underlying purpose of Google differ from those of public libraries. Further, an increasing amount of criticism is being directed at collaborative spaces (typically Wikipedia) and social networks (e.g. MySpace) and it is pointed out that these social media are not that innocent and unproblematic. In discussing the survival of public libraries and devising an updated role for libraries in the age of Google and social media, attention should be given to fleshing out a new vision for the public library as a provider of alternative information and as an institution supporting information democracy.||0||0|
|Collective intelligence approach for formulating a BOK of social informatics, an interdisciplinary field of study||Yoshifumi Masunaga
|WikiSym||English||2009||This presentation shows a collective intelligence approach for formulating a body of knowledge (BOK) of social informatics (SI), a relatively new interdisciplinary field of study, by implementing a BOK constructor based on Semantic MediaWiki. Copyright||0||0|
|Collective intelligence system engineering||Ioanna Lykourentzou
|Proceedings of the International Conference on Management of Emergent Digital EcoSystems, MEDES '09||English||2009||Collective intelligence (CI) is an emerging research field which aims at combining human and machine intelligence, to improve community processes usually performed by large groups. CI systems may be collaborative, like Wikipedia, or competitive, like a number of recently established problem-solving companies that attempt to find solutions to difficult R&D or marketing problems drawing on the competition among web users. The benefits that CI systems earn user communities, combined with the fact that they share a number of basic common characteristics, open up the prospect for the design of a general methodology that will allow the efficient development and evaluation of CI. In the present work, an attempt is made to establish the analytical foundations and main challenges for the design and construction of a generic collective intelligence system. First, collective intelligence systems are categorized into active and passive and specific examples of each category are provided. Then, the basic modeling framework of CI systems is described. This includes concepts such as the set of possible user actions, the CI system state and the individual and community objectives. Additional functions, which estimate the expected user actions, the future state of the system, as well as the level of objective fulfillment, are also established. In addition, certain key issues that need to be considered prior to system launch are also described. The proposed framework is expected to promote efficient CI design, so that the benefit gained by the community and the individuals through the use of CI systems, will be maximized. Copyright 2009 ACM.||0||0|
|Coordination in collective intelligence: The role of team structure and task interdependence||Aniket Kittur
|Conference on Human Factors in Computing Systems - Proceedings||English||2009||The success of Wikipedia has demonstrated the power of peer production in knowledge building. However, unlike many other examples of collective intelligence, tasks in Wikipedia can be deeply interdependent and may incur high coordination costs among editors. Increasing the number of editors increases the resources available to the system, but it also raises the costs of coordination. This suggests that the dependencies of tasks in Wikipedia may determine whether they benefit from increasing the number of editors involved. Specifically, we hypothesize that adding editors may benefit low-coordination tasks but have negative consequences for tasks requiring a high degree of coordination. Furthermore, concentrating the work to reduce coordination dependencies should enable more efficient work by many editors. Analyses of both article ratings and article review comments provide support for both hypotheses. These results suggest ways to better harness the efforts of many editors in social collaborative systems involving high coordination tasks. Copyright 2009 ACM.||0||0|
|Cyber engineering co-intelligence digital ecosystem: The GOFASS methodology||Leong P.
|2009 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST '09||English||2009||Co-intelligence, also known as collective or collaborative intelligence, is the harnessing of human knowledge and intelligence that allows groups of people to act together in ways that seem to be intelligent. Co-intelligence Internet applications such as Wikipedia are the first steps toward developing digital ecosystems that support collective intelligence. Peer-to-peer (P2P) systems are well fitted to co-Intelligence digital ecosystems because they allow each service client machine to act also as a service provider without any central hub in the network of cooperative relationships. However, dealing with server farms, clusters and meshes of wireless edge devices will be the norm in the next generation of computing; but most present P2P system had been designed with a fixed, wired infrastructure in mind. This paper proposes a methodology for cyber engineering an intelligent agent mediated co-intelligence digital ecosystems. Our methodology caters for co-intelligence digital ecosystems with wireless edge devices working with service-oriented information servers.||0||0|
|Metasocial wiki - Towards an interlinked knowledge in a decentralized social space||Cano A.E.
|CEUR Workshop Proceedings||English||2009||This paper introduces a new approach to semantic wikis. In this approach users coming from different social networks can be merged into a common space to enable collaboration. This approach makes use of the user's identity representation and keeping track of the user's interests according to the type of annotations encountered in the content they add.||0||0|
|The concept and framework of All Network Service (ANS)||Kyeongseo M.H.
|2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009||English||2009||This paper introduces the concept and framework of All Network Service (ANS). The concept of ANS has developed from Social Network Service (SNS) and Wiki service. Social Network Service is designed to utilize the human network offline to online. SNS helps to connect people with relations, interests and affiliation. Wiki designed to create a knowledge repository by actively and wide openly sharing information. Unlike the SNS that information has ownership by users and can be accessible depending only on the relationships Wiki shares the information with no restriction. Information on Wiki absolutely has no ownership and information is linked each other by its similarity and relativity. Although Wiki reflects the concept of web 2.0 - sharing and modifying information by multiple users who are willing to join - Wiki is merely oriented in information, not human relationship. ANS is designed to combine the concept of two services to overcome the limitations that each of them has; connecting people and sharing and accessing information with no restriction. ANS treats the information and human member as same identity within the service; same functionalities can be applied such as connecting each identity. In addition, ANS allows each identity can be modified by other identities, just like Wiki, for high quality objective information by multiple revisions. ANS provides low searching cost, highly objective information, and collaborative online society. This paper describes the limitation of SNS and Wiki, and How ANS recovers them.||0||0|
|Combining structure and semantics for ontology-based corporate wikis||Alexandre Passant
|Lecture Notes in Business Information Processing||English||2008||While wikis offer new means to collaboratively build, organize and share knowledge in organizations, such knowledge cannot be easily understood by computers in a query answering or reusability process. This paper details the features and architecture of a wiki-farm system that combines structure and semantics in order to collaboratively produce ontology-based data and immediately reuse it in wiki pages to enrich browsing and querying capabilities of the system.||0||0|
|Harnessing the wisdom of crowds in wikipedia: Quality through coordination||Aniket Kittur
|English||2008||Wikipedia's success is often attributed to the large numbers of contributors who improve the accuracy, completeness and clarity of articles while reducing bias. However, because of the coordination needed to write an article collaboratively, adding contributors is costly. We examined how the number of editors in Wikipedia and the coordination methods they use affect article quality. We distinguish between explicit coordination, in which editors plan the article through communication, and implicit coordination, in which a subset of editors structure the work by doing the majority of it. Adding more editors to an article improved article quality only when they used appropriate coordination techniques and was harmful when they did not. Implicit coordination through concentrating the work was more helpful when many editors contributed, but explicit coordination through communication was not. Both types of coordination improved quality more when an article was in a formative stage. These results demonstrate the critical importance of coordination in effectively harnessing the "wisdom of the crowd" in online production environments. Copyright 2008 ACM.||0||4|
|On Supporting HCOME-3O Ontology Argumentation Using Semantic Wiki Technology||Konstantinos Kotis||OTM||English||2008||0||0|
|Tutkimusparvi the open research swarm in Finland||Heiskanen T.
|MindTrek - 12th International MindTrek Conference: Entertainment and Media in the Ubiquitous Era||English||2008||in this paper, we introduce a new kind of scientific collaboration type (open research swarm) and describe a realization (Tutkimusparvi) of this new type of scientific social network. Swarming is an experiment in selforganizing and a novel way to collaborate in the field of academic research. Open research swarms utilize the possibilities of Internet, especially the social media tools that are now available because of the web 2.0 boom. The main goal is to collectively attain rapid solutions to given challenges and to develop a distributed intellectual milieu for researchers. Transparency of the research and creative collaboration are central ideas behind open research swarms. Like Wikipedia, open research swarm is open for everyone to participate. The questions and research topics can come from open research swarm participants, from a purposed principal or from general discussions on the mass media. Copyright 2008 ACM.||0||0|
|Web 2.0 as syndication||Clarke R.||Journal of Theoretical and Applied Electronic Commerce Research||English||2008||There is considerable excitement about the notion of 'Web 2.0', particularly among Internet businesspeople. In contrast, there Is an almost complete lack of formal literature on the topic. It Is important that movements with such energy and potential be subjected to critical attention, and that industry and social commentators have the opportunity to draw on the eCommerce research literature in formulating their views. This paper assesses the available information about Web 2.0, with a view to stimulating further work that applies existing theories, proposes new ones, observes and measures phenomena, and tests the theories. The primary interpretation of the concept derives from marketers, but the complementary technical and communitarian perspectives are also considered. A common theme derived from the analysis Is that of 'syndication' of content, advertising, storage, effort and identity.||0||0|
|How and why Wikipedia works: An interview with Angela Beesley, Elisabeth Bauer, and Kizu Naoko||Dirk Riehle||Proceedings of WikiSym'06 - 2006 International Symposium on Wikis||English||2006||This article presents an interview with Angela Beesley, Elisabeth Bauer, and Kizu Naoko. All three are leading Wikipedia practitioners in the English, German, and Japanese Wikipedias and related projects. The interview focuses on how Wikipedia works and why these three practitioners believe it will keep working. The interview was conducted via email in preparation of WikiSym 2006, the 2006 International Symposium on Wikis, with the goal of furthering Wikipedia research . Interviewer was Dirk Riehle, the chair of WikiSym 2006. An online version of the article provides simplified access to URLs .||0||1|
|How and why Wikipedia works: an interview with Angela Beesley, Elisabeth Bauer, and Kizu Naoko||Dirk Riehle||WikiSym||English||2006||This article presents an interview with Angela Beesley, Elisabeth Bauer, and Kizu Naoko. All three are leading Wikipedia practitioners in the English, German, and Japanese Wikipedias and related projects. The interview focuses on how Wikipedia works and why these three practitioners believe it will keep working. The interview was conducted via email in preparation of WikiSym 2006, the 2006 International Symposium on Wikis, with the goal of furthering Wikipedia research. Interviewer was Dirk Riehle, the chair of WikiSym 2006. An online version of the article provides simplified access to URLs.||0||1|
|Wisdom of the Crowds: Decentralized Knowledge Construction in Wikipedia||Ofer Arazy
|16th Annual Workshop on Information Technologies & Systems (WITS)||2006||Recently, Nature published an article comparing the quality of Wikipedia articles to those of Encyclopedia Britannica (Giles 2005). The article, which gained much public attention, provides evidence for Wikipedia quality, but does not provide an explanation of the underlying source of that quality. Wikipedia, and wikis in general, aggregate information from a large and diverse author-base, where authors are free to modify any article. Building upon Surowiecki's (2005) Wisdom of Crowds, we develop a model of the factors that determine wiki content quality. In an empirical study of Wikipedia, we find strong support for our model. Our results indicate that increasing size and diversity of the author-base improves content quality. We conclude by highlighting implications for system design and suggesting avenues for future research.||0||0|