Abstract. This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimental results demonstrate our model’s efficiency. 1

Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user’s profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user’s current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.

Abstract. While the Semantic Web requires a large amount of structured knowledge (triples) to allow machine reasoning, the acquisition of this knowledge still represents an open issue. Indeed, expressing expert knowledge in a given formalism is a tedious process. Less structured annotations such as tagging have, however, proved immensely popular, whilst existing unstructured or semi-structured collaborative knowledge bases such as Wikipedia have proven to be useful and scalable. Both processes are often regulated through social mechanisms such as wiki-like operations, recommendations, ratings, and collaborative games. To promote collaborative tagging as a means to acquire unstructured as well as structured knowledge we introduce the notion of Extreme Tagging, which describes systems which allow the tagging of resources, as well as of tags themselves and their relations. We provide a formal description of extreme tagging followed by examples and highlight the necessity of regulatory processes which can be applied to it. We also present a prototype implementation.

Folksonomies, otherwise known as Collaborative Tagging Systems, enable Internet users to share, annotate and search for online resources with user selected labels called tags. Tag recommendation, the suggestion of an ordered set of tags during the annotation process, reduces the user effort from a keyboard entry to a mouse click. By simplifying the annotation process tagging is promoted, noise in the data is reduced through the elimination of discrepancies that result in redundant tags, and ambiguous tags may be avoided. Tag recommenders can suggest tags that maximize utility, offer tags the user may not have previously considered or steer users toward adopting a core vocabulary. In sum, tag recommendation promotes a denser dataset that is useful in its own right or can be exploited by a myriad of data mining techniques for additional functionality. While there exists a long history of recommendation algorithms, the data structure of a Folksonomy is distinct from those found in traditional recommendation problems. We first explore two data reduction techniques, p-core processing and Hebbian deflation, then demonstrate how to adapt K-Nearest Neighbor for use with Folksonomies by incorporating user, resource and tag information into the algorithm. We further investigate multiple techniques for user modeling required to compute the similarity among users. Additionally we demonstrate that tag boosting, the promoting of tags previously applied by a user to a resource, improves the coverage and accuracy of K-Nearest Neighbor. These techniques are evaluated through extensive experimentation using data collected from two real Collaborative Tagging Web sites. Finally the modified K-Nearest Neighbor algorithm is compared with alternative techniques based on popularity and link analysis. We find that K-Nearest Neighbor modified for use with Folksonomies generates excellent recommendations, scales well with large datasets, and is applicable to both narrow and broadly focused Folksonomies.

Abstract: Web 2.0 approaches are revolutionizing the Internet, blurring lines between developers and users and enabling collaboration and social networks that scale into the millions of users. As we have discussed in previous work, the core technologies of Web 2.0 effectively define a comprehensive distributed computing environment that parallels many of the more complicated service‐oriented systems such as Web service and Grid service architectures. In this paper we build upon this previous work to discuss applications of Web 2.0 approaches to four different scenarios: client‐side JavaScript libraries for building and composing Grid services; integrating server‐side portlets with “rich client ” AJAX tools and web services for analyzing Global Positioning System data; building and analyzing folksonomies of scientific user communities through social bookmarking; and applying microformats and GeoRSS to problems in scientific metadata description and delivery. 1.

In this paper we investigate detection of high-level concepts in multimedia content through an integrated approach of visual thesaurus analysis and visual context. In the former, detection is based on model vectors that represent image composition in terms of region types, obtained through clustering over a large data set. The latter deals with two aspects, namely high-level concepts and region types of the thesaurus, employing a model of a priori specified semantic relations among concepts and automatically extracted topological relations among region types; thus it combines both conceptual and topological context. A set of algorithms is presented, which modify either the confidence values of detected concepts, or the model vectors based on which detection is performed. Visual context exploitation is evaluated on TRECVID and Corel data sets and compared to a number of related visual thesaurus approaches.

Abstract. Although social, collaborative classification through tagging has been the focus of recent research, the effect of multilingual tags is often overlooked. This work presents an early exploratory study of the production and consumption of multilingual tags in a European educational K-12 context. The data, produced by teachers bookmarking and tagging learning resources during three month period, was analysed. Thereafter, a focus group of teachers evaluated a sample of learning resources with metadata records containing both thesaurus terms and multilingual tags. The results of this early study suggest that some tags are found as useful as thesaurus terms and that users are divided about the benefits of multilingual tags. As some tags are useful for some users, “hiding all but the right tags ” becomes crucial for the success of a multilingual collaborative tagging system.

Abstract. Higher level semantics are considered useful in the geospatial domain, yet there is no general consensus on the form these semantics should take. Indeed, knowledge representation paradigms such as classification based ontologies do not always pay tribute to the complexity of geospatial semantics. Other approaches, originating from psychology, linguistics, philosophy or cognitive sciences are regularly investigated to enrich the GIScientist’s representational toolbox. However, each of these techniques is often used to the exclusion of others, creating new representational difficulties, or merely as a useful addendum to host theories with which they only superficially integrate. The present work is an attempt to introduce a common ground to these techniques by reducing them to the notion of differences or difference spaces. Differences are discernible properties of the environment, detected or produced by a computational process. I describe the following semantic frameworks: category-based ontologies, conceptual spaces, affordance based models, image schemata, and multi representation, explaining how each of them can be projected to a model based on differences. Illustrative examples from table top and geographic space are produced in order to show the model in use. 1

Collaborative tagging systems, sometimes referred to as “folksonomies, ” enable Internet users to annotate or search for resources using custom labels instead of being restricted by pre-defined navigational or conceptual hierarchies. However, the flexibility of tagging brings with it certain costs. Because users are free to apply any tag to any resource, tagging systems contain large numbers of redundant, ambiguous, and idiosyncratic tags which can render resource discovery difficult. Data mining techniques such as clustering can be used to ameliorate this problem by reducing noise in the data and identifying trends. In particular, discovered patterns can be used to tailor the system’s output to a user based on the user’s tagging behavior. In this paper, we propose a method to personalize a user’s experience within a folksonomy using clustering. A personalized view can overcome ambiguity and idiosyncratic tag assignment, presenting users with tags and resources that correspond more closely to their intent. Specifically, we examine unsupervised clustering methods for extracting commonalities between tags, and use the discovered clusters as intermediaries between a user’s profile and resources in order to tailor the results of search to the user’s interests. We validate this approach through extensive evaluation of proposed personalization algorithm and the underlying clustering techniques using data from a real collaborative tagging Web site.

Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. Furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications. 1.