Bridging the gap between folksonomies and the semantic web: an experience report

Abstract. While folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely hampered by being agnostic to the relations that exist between these tags. To overcome this limitation, several methods have been proposed to find groups of implicitly inter-related tags. We believe that content retrieval can be further improved by making the relations between tags explicit. In this paper we propose the semantic enrichment of folksonomy tags with explicit relations by harvesting the Semantic Web, i.e., dynamically selecting and combining relevant bits of knowledge from online ontologies. Our experimental results show that, while semantic enrichment needs to be aware of the particular characteristics of folksonomies and the Semantic Web, it is beneficial for both.

Reusing Ontologies to Enrich Semantically User Content in Web2.0: A Case Study on Folksonomies

Semantic Web and Web2.0 emerged during the past decade promising to achieve new frontiers for the Web. On the one hand, the Semantic Web is an interlinked web of data, supported by ontological semantics and allowing for intelligent applications such as semantic search and integration of heterogeneous content across systems and applications. On the other hand, Web2.0 represents the new technologies and paradigms that revolutionised the user engagement in content creation and introduced novel means towards social interaction. Bridging the gap between Web2.0 and the Semantic Web has been proposed as a means to better manage and interact with the large amounts of user contributed content, which is a new challenge for Web2.0. This thesis focuses on a popular paradigm of Web2.0, folksonomies. In particular, we investigate the semantic enrichment of folksonomy tagspaces by reusing ontologies available in the Semantic Web. We identify the need for methods that automatically apply semantic descriptions to user generated content without requiring user intervention or alteration of the current tagging paradigm. We use an iterative approach in order to identify the characteristics of folksonomies and the attributes of knowledge sources that influence the semantic enrichment of tagspaces. We build on the results of our experimental studies to implement a folksonomy enrichment algorithm, that given an input tagspace, automatically creates a semantic structure that describes the meaning and relations of tags. We introduce measures for the evaluation of enriched tagspaces and finally, we propose a search algorithm that exploits the semantic structures to improve folksonomy search

Evaluating the semantic web: a task-based approach

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape

Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.Comment: 6 pages, 2 figures, CIKM 2011: 20th ACM Conference on Information and Knowledge Managemen

Improving search in folksonomies: a task based comparison of WordNet and ontologies

Search in folksonomies is hampered by the fact that the meaning of tags and their relations are not made explicit in the system. This is typically addressed by using knowledge sources (KS) to semantically enrich tagspaces, most notably WordNet and (online) ontologies. However, there is no insight of how the different characteristics of these KS contribute to search improvement in folksonomies. In this work we compare these two KS in the context of folksonomy search. We show that while WordNet leads to richer tag structures than online ontologies do, its fine-grained sense hierarchy renders these structures less effective in search compared to the ones generated from ontologies

Personalizing and Improving Resource Recommendation by Analyzing Users Preferences in Social Tagging Activities

Collaborative tagging which is the keystone of the social practices of web 2.0 has been highly developed in the last few years. In this paper, we propose a new method to analyze user profiles according to their tagging activity in order to improve resource recommendation. We base upon association rules which is a powerful method to discover interesting relationships among large datasets on the web. Focusing on association rules we can find correlations between tags in a social network. Our aim is to recommend resources annotated with tags suggested by association rules, in order to enrich user profiles. The effectiveness of the recommendation depends on the resolution of social tagging drawbacks. In our recommender process, we demonstrate how we can reduce tag ambiguity and spelling variations problems by taking into account social similarities calculated on folksonomies, in order to personalize resource recommendation. We surmount also the lack of semantic links between tags during the recommendation process. Experiments are carried out with two different scenarios: the first one is a proof of concept over two baseline datasets and the second one is a real world application for diabetes disease

Dynamic Tagging for Enterprise Knowledge Sharing and Representation

The development of Web 2.0 technology provides an easy way for people to transfer and share knowledge. As a collaborative tagging tool, folksonomy is an efficient indexing method in Web 2.0 environments. After analyzing the pros and cons of current knowledge management systems, this research proposes a dynamic Tagging system that combines folksonomy technologies with other approaches including automatic schema enrichment and training. The proposed system improves access to a large, growing collection by supporting users collaboratively contribute to the building of tags. In addition, the proposed system provides an efficient way for firms to represent knowledge and share knowledge with customers and other firms

Spatio-semantic user profiles in location-based social networks

Knowledge of users’ visits to places is one of the keys to understanding their interest in places. User-contributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users’ association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets