Social Semantic Cloud of Tag: Semantic Model for Social Tagging

Abstract

Abstract. Tagging has proven to be a successful and efficient way for creating metadata through a human collective intelligence. It can be considered not only an application of individuals for expressing one's interests, but also as a starting point for leveraging social connections through collaborative user participations. A number of users have contributed to tag resources in web sites such as Del.icio.us, Flickr etc.However, there is no uniform structure to describe tags and user's activities. This makes difficult to share and represent tag data among people. The SCOT (Social Semantic Cloud of Tags) ontology is aimed to represent the structure and semantics of a set of tags and promotes their global sharing. The paper introduce the SCOT ontology and methods of its representation.

Implementation of Folksonomy Based Tag Cloud Model for Information Retrieval from Document Repository in an Indian University

“…Rivadeneira et al [1] and Kim et al [4] studied tag clouds and its evolution. Trant et al [11] have proposed a framework for tagging system.…”

Mining various semantic relationships from unstructured user-generated web data

“…Some researchers [1,2,15,16,3,4,5,17,18] have constructed relationships by examining the associations between data sources. Some of these approaches [15,16] examine the associations between tags, taggedURLs, and users who give the tags and denote the associations in semantic relationships, such as "tagged by" or "used by" between tags and users.…”

“…Some of these approaches [15,16] examine the associations between tags, taggedURLs, and users who give the tags and denote the associations in semantic relationships, such as "tagged by" or "used by" between tags and users. Other approaches [1,2,3,4,5] address the associations between (tag, URL) pairs and (query, clicked URL) pairs in bipartite graphs.…”

Visual knowledge representation of conceptual semantic networks

“…The following list is not an exhaustive list of applications, but it provides an overview about the most recent areas of research where visualization became essential: a) Topic summarization: e.g., understanding newspaper articles, stories, reporting events, investigating crime reports, finding patterns in blogs, following the development of political campaigns, or observing topic trends in the bibliography of research approaches [7,3,25]; b) Visual Analysis of Social Networks: e.g., analyzing dynamic groups memberships in temporal social networks by using graphical representations [10,12,17,6,29]; c) Visual Clustering Analysis: e.g., using data mining techniques to find patterns in data to generate group of data based on (dis)similarity. Several visualization tools have been developed in this domain and gained great popularity, to mention some [21,2,28,5,30]; d) Semantic Visual Analysis: e.g., visual analysis of webpage/documents based on the semantic representation of text in a "semantic graph" [23,8,9,22,31], or exploring data in folksonomy systems based on a hierarchical semantic representation, "semantic cloud or tags" [11,14,24,23,4,15,16,22,26] …”