Kangpyo Lee

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Kangpyo Lee is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(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 Collective intelligence
User-paragraph network
Visaphor
Wikipedia
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
SocialTrust++: Building community-based trust in social information systems Proceedings of the 6th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2010 English 2010 Social information systems - popularized by Facebook, Wikipedia, Twitter, and other social websites - are emerging as a powerful new paradigm for distributed social-powered information management. While there has been growing interest in these systems by businesses, government agencies, and universities, there remain important open challenges that must be addressed if the potential of these social systems is to be fully realized. For example, the presence of poor quality users and users intent on manipulating the system can disrupt the quality of socially-powered information and knowledge sharing applications. In this paper, we outline the SocialTrust++ project at Texas A&M University. The overall research goal of the SocialTrust++ project is to develop, analyze, deploy, and test algorithms for building, enabling, and leveraging community-based trust in Social Information Systems. Concretely, we are developing a trustworthy community-based information platform so that each user in a Social Information System can have transparent access to the community's trust perspective to enable more effective and efficient social information access. 0 0
FolksoViz: A Semantic Relation-Based Folksonomy Visualization Using the Wikipedia Corpus Folksonomy
Collaborative tagging
Semantic Relation
Visualisation
Wikipedia
Web 2.0
SNPD English 2009 0 0
FolksoViz: A semantic relation-based folksonomy visualization using the Wikipedia corpus Collaborative tagging
Folksonomy
Semantic Relation
Visualisation
Web 2.0
Wikipedia
10th ACIS Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2009, In conjunction with IWEA 2009 and WEACR 2009 English 2009 Tagging is one of the most popular services in Web 2.0 and folksonomy is a representation of collaborative tagging. Tag cloud has been the one and only visualization of the folksonomy. The tag cloud, however, provides no information about the relations between tags. In this paper, targeting del.icio.us tag data, we propose a technique, FolksoViz, for automatically deriving semantic relations between tags and for visualizing the tags and their relations. In order to find the equivalence, subsumption, and similarity relations, we apply various rules and models based on the Wikipedia corpus. The derived relations are visualized effectively. The experiment shows that the FolksoViz manages to find the correct semantic relations with high accuracy. 0 0
Tag sense disambiguation for clarifying the vocabulary of social tags Folksonomy
Social tagging
Vocabulary
Wikipedia
Word sense disambiguation
Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009 English 2009 Tagging is one of the most popular services in Web 2.0. As a special form of tagging, social tagging is done collaboratively by many users, which forms a so-called folksonomy. As tagging has become widespread on the Web, the tag vocabulary is now very informal, uncontrolled, and personalized. For this reason, many tags are unfamiliar and ambiguous to users so that they fail to understand the meaning of each tag. In this paper, we propose a tag sense disambiguating method, called Tag Sense Disambiguation (TSD), which works in the social tagging environment. TSD can be applied to the vocabulary of social tags, thereby enabling users to understand the meaning of each tag through Wikipedia. To find the correct mappings from del.icio.us tags to Wikipedia articles, we define the Local )eighbor tags, the Global )eighbor tags, and finally the )eighbor tags that would be the useful keywords for disambiguating the sense of each tag based on the tag co-occurrences. The automatically built mappings are reasonable in most cases. The experiment shows that TSD can find the correct mappings with high accuracy. 0 0
FolksoViz: A subsumption-based folksonomy visualization using the wikipedia Journal of KISS: Computing Practices 2008 Folksonomy, which is created through the collaborative tagging from many users, is one of the driving factors of Web 2.0. Tags are said to be the web metadata describing a web document. If we are able to find the semantic subsumption relationships between tags created through the collaborative tagging, it can help users understand the metadata more intuitively. In this paper, targeting del.icio.us tag data, we propose a method named {FolksoViz} for deriving subsumption relationships between tags by using Wikipedia texts. For this purpose, we propose a statistical model for deriving subsumption relationships based on the frequency of each tag on the Wikipedia texts, and {TSD} {(Tag} Sense Disambiguation) method for mapping each tag to a corresponding Wikipedia text. The derived subsumption pairs are visualized effectively on the screen. The experiment shows that our proposed algorithm managed to find the correct subsumption pairs with high accuracy. 0 0
Folksoviz: a subsumption-based folksonomy visualization using wikipedia texts Collaborative tagging
Folksonomy
Subsumption
Visualisation
Web 2.0
Wikipedia
World Wide Web English 2008 0 0