Tag Meaning Disambiguation through Analysis of Tripartite Structure of Folksonomies

Collaborative tagging systems are becoming very popular recently. Web users use freely-chosen tags to describe shared resources, resulting in a folksonomy. One problem of folksonomies is that tags which appear in the same form may carry multiple meanings and represent different concepts. As this kind of tags are ambiguous, the precisions in both description and retrieval of the shared resources are reduced. We attempt to develop effective methods to disambiguate tags by studying the tripartite structure of folksonomies. This paper describes the network analysis techniques that we employ to discover clusters of nodes in networks and the algorithm for tag disambiguation. Experiments show that the method is very effective in performing the task

Semantic modelling of user interests based on cross-folksonomy analysis

The continued increase in Web usage, in particular participation in folksonomies, reveals a trend towards a more dynamic and interactive Web where individuals can organise and share resources. Tagging has emerged as the de-facto standard for the organisation of such resources, providing a versatile and reactive knowledge management mechanism that users find easy to use and understand. It is common nowadays for users to have multiple profiles in various folksonomies, thus distributing their tagging activities. In this paper, we present a method for the automatic consolidation of user profiles across two popular social networking sites, and subsequent semantic modelling of their interests utilising Wikipedia as a multi-domain model. We evaluate how much can be learned from such sites, and in which domains the knowledge acquired is focussed. Results show that far richer interest profiles can be generated for users when multiple tag-clouds are combine

Semantics, sensors, and the social web: The live social semantics experiments

The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data and technologies from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities at conferences. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes this application, and discusses and evaluates the results of its two deployment

Understanding the Semantics of Ambiguous Tags in Folksonomies

The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users' own way. In this paper, we present a possible way to analyze the tripartite graphs - graphs involving users, tags and resources - of folksonomies and discuss how these elements acquire their meanings through their associations with other elements, a process we call mutual contextualization. In particular, we demonstrate how different meanings of ambiguous tags can be discovered through such analysis of the tripartite graph by studying the tag sf. We also discuss how the result can be used as a basis to better understand the nature of folksonomies

Live Social Semantics

Social interactions are one of the key factors to the success of conferences and similar community gatherings. This paper describes a novel application that integrates data from the semantic web, online social networks, and a real-world contact sensing platform. This application was successfully deployed at ESWC09, and actively used by 139 people. Personal profiles of the participants were automatically generated using several Web~2.0 systems and semantic academic data sources, and integrated in real-time with face-to-face contact networks derived from wearable sensors. Integration of all these heterogeneous data layers made it possible to offer various services to conference attendees to enhance their social experience such as visualisation of contact data, and a site to explore and connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in this deployment

Tag disambiguation based on social network information

Within 20 years the Web has grown from a tool for scientists at CERN into a global information space. While returning to its roots as a read/write tool, its entering a more social and participatory phase. Hence a new, improved version called the Social Web where users are responsible for generating and sharing content on the global information space, they are also accountable for replicating the information. This collaborative activity can be observed in two of the most widely practised Social Web services such as social network sites and social tagging systems. Users annotate their interests and inclinations with free form keywords while they share them with their social connections. Although these keywords (tag) assist information organization and retrieval, theysuffer from polysemy.In this study we employ the effectiveness of social network sites to address the issue of ambiguity in social tagging. Moreover, we also propose that homophily in social network sites can be a useful aspect is disambiguating tags. We have extracted the ‘Likes’ of 20 Facebook users and employ them in disambiguation tags on Flickr. Classifiers are generated on the retrieved clusters from Flickr using K-Nearest-Neighbour algorithm and then their degree of similarity is calculated with user keywords. As tag disambiguation techniques lack gold standards for evaluation, we asked the users to indicate the contexts and used them as ground truth while examining the results. We analyse the performance of our approach by quantitative methods and report successful results. Our proposed method is able classify images with an accuracy of 6 out of 10 (on average). Qualitative analysis reveal some factors that affect the findings, and if addressed can produce more precise results

An integrated approach to discover tag semantics

Tag-based systems have become very common for online classification thanks to their intrinsic advantages such as self-organization and rapid evolution. However, they are still affected by some issues that limit their utility, mainly due to the inherent ambiguity in the semantics of tags. Synonyms, homonyms, and polysemous words, while not harmful for the casual user, strongly affect the quality of search results and the performances of tag-based recommendation systems. In this paper we rely on the concept of tag relatedness in order to study small groups of similar tags and detect relationships between them. This approach is grounded on a model that builds upon an edge-colored multigraph of users, tags, and resources. To put our thoughts in practice, we present a modular and extensible framework of analysis for discovering synonyms, homonyms and hierarchical relationships amongst sets of tags. Some initial results of its application to the delicious database are presented, showing that such an approach could be useful to solve some of the well known problems of folksonomies

Semantic disambiguation and contextualisation of social tags

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-28509-7_18This manuscript is an extended version of the paper ‘cTag: Semantic Contextualisation of Social Tags’, presented at the 6th International Workshop on Semantic Adaptive Social Web (SASWeb 2011).We present an algorithmic framework to accurately and efficiently identify the semantic meanings and contexts of social tags within a particular folksonomy. The framework is used for building contextualised tag-based user and item profiles. We also present its implementation in a system called cTag, with which we preliminary analyse semantic meanings and contexts of tags belonging to Delicious and MovieLens folksonomies. The analysis includes a comparison between semantic similarities obtained for pairs of tags in Delicious folksonomy, and their semantic distances in the whole Web, according to co-occurrence based metrics computed with results of a Web search engine.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and Universidad Autónoma de Madrid (CCG10-UAM/TIC-5877

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

Semantic contextualisation of social tag-based profiles and item recommendations

Proceedigns of 12th International Conference, EC-Web 2011, Toulouse, France, August 30 - September 1, 2011.The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-23014-1_9We present an approach that efficiently identifies the semantic meanings and contexts of social tags within a particular folksonomy, and exploits them to build contextualised tag-based user and item profiles. We apply our approach to a dataset obtained from Delicious social bookmarking system, and evaluate it through two experiments: a user study consisting of manual judgements of tag disambiguation and contextualisation cases, and an offline study measuring the performance of several tag-powered item recommendation algorithms by using contextualised profiles. The results obtained show that our approach is able to accurately determine the actual semantic meanings and contexts of tag annotations, and allow item recommenders to achieve better precision and recall on their predictions.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and the Community of Madrid (CCG10- UAM/TIC-5877