Kang-Pyo Lee

A social inverted index for social-tagging-based information retrieval

Keywords have played an important role not only for searchers who formulate a query, but also for search engines that index documents and evaluate the query. Recently, tags chosen by users to annotate web resources are gaining significance for improving information retrieval (IR) tasks, in that they can act as meaningful keywords bridging the gap between humans and machines. One critical aspect of tagging (besides the tag and the resource) is the user (or tagger); there exists a ternary relationship among the tag, resource, and user. The traditional inverted index, however, does not consider the user aspect, and is based on the binary relationship between term and document. In this paper we propose a social inverted index – a novel inverted index extended for social-tagging-based IR – that maintains a separate user sublist for each resource in a resource-posting list to contain each user’s various features as weights. The social inverted index is different from the normal inverted index in that it regards each user as a unique person, rather than simply count the number of users, and highlights the value of a user who has participated in tagging. This extended structure facilitates the use of dynamic resource weights, which are expected to be more meaningful than simple user-frequency-based weights. It also allows a flexible response to the conditional queries that are increasingly required in tag-based IR. Our experiments have shown that this user-considering indexing performs better in IR tasks than a normal inverted index with no user sublists. The time and space overhead required for index construction and maintenance was also acceptable.

Folksoviz: a subsumption-based folksonomy visualization using wikipedia texts

In this paper, targeting del.icio.us tag data, we propose a method, FolksoViz, for deriving subsumption relationships between tags by using Wikipedia texts, and visualizing a folksonomy. To fulfill this method, we propose a statistical model for deriving subsumption relationships based on the frequency of each tag on the Wikipedia texts, as well as the 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 the FolksoViz manages to find the correct subsumption pairs with high accuracy.

Tag Sense Disambiguation for Clarifying the Vocabulary of Social Tags

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 Disambigu-ation (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 Neighbor tags, the Global Neighbor tags, and finally the Neighbor tags that would be the useful key-words 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 cor-rect mappings with high accuracy.

FolksoViz: A Semantic Relation-Based Folksonomy Visualization Using the Wikipedia Corpus

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, Folk-soViz, 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 ef-fectively. The experiment shows that the FolksoViz manag-es to find the correct semantic relations with high accuracy.