FolkRank: A Ranking Algorithm for Folksonomies

In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006

Exploring The Value Of Folksonomies For Creating Semantic Metadata

Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources

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

Measuring Similarity in Large-Scale Folksonomies

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. Unlike\ud taxonomies, which overimpose a hierarchical categorisation of content, folksonomies enable end-users to freely create and choose the categories (in this case, tags) that best\ud describe some content. However, as tags are informally de-\ud fined, continually changing, and ungoverned, social tagging\ud has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of\ud users and the noise they introduce. To address this issue, a\ud variety of approaches have been proposed that recommend\ud users what tags to use, both when labelling and when looking for resources. As we illustrate in this paper, real world\ud folksonomies are characterized by power law distributions\ud of tags, over which commonly used similarity metrics, including the Jaccard coefficient and the cosine similarity, fail\ud to compute. We thus propose a novel metric, specifically\ud developed to capture similarity in large-scale folksonomies,\ud that is based on a mutual reinforcement principle: that is,\ud two tags are deemed similar if they have been associated to\ud similar resources, and vice-versa two resources are deemed\ud similar if they have been labelled by similar tags. We offer an efficient realisation of this similarity metric, and assess its quality experimentally, by comparing it against cosine similarity, on three large-scale datasets, namely Bibsonomy, MovieLens and CiteULike

Integrating Folksonomies into Cultural Heritage Digital Collections: The Challenges and Opportunities of Web 2.0

In this presentation, the author defines Folksonomy and the advantages and disadvantages of Folksonomy. He begins with a background on information retrieval and changing technologies, discusses trends in technologies, and explains the use of tags and Folksonomy

Developing a Formal Model for Mind Maps

Mind map is a graphical technique, which is used to represent words, concepts, tasks or other connected items or arranged around central topic or idea. Mind maps are widely used, therefore exist plenty of software programs to create or edit them, while there is none format for the model representation, neither a standard format. This paper presents and effort to propose a formal mind map model aiming to describe the structure, content, semantics and social connections. The structure describes the basic mind map graph consisted of a node set, an edge set, a cloud set and a graphical connections set. The content includes the set of the texts and objects linked to the nodes. The social connections are the mind maps of other users, which form the neighborhood of the mind map owner in a social networking system. Finally, the mind map semantics is any true logic connection between mind map textual parts and a concept. Each of these elements of the model is formally described building the suggested mind map model. Its establishment will support the application of algorithms and methods towards their information extraction

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.

Creating structure from disorder: using folksonomies to create semantic metadata

This paper reports on an on-going research project to create educational semantic metadata out of folksonomies. The paper describes a simple scenario for the usage of the generated semantic metadata in teaching, and describes the ‘FolksAnnotation’ tool which applies an organization scheme to tags in a specific domain of interest. The contribution of this paper is to describe an evaluation framework which will allow us to validate our claim that folksonomies are potentially a rich source of metadata

Preliminary results in tag disambiguation using DBpedia

The availability of tag-based user-generated content for a variety of Web resources (music, photos, videos, text, etc.) has largely increased in the last years. Users can assign tags freely and then use them to share and retrieve information. However, tag-based sharing and retrieval is not optimal due to the fact that tags are plain text labels without an explicit or formal meaning, and hence polysemy and synonymy should be dealt with appropriately. To ameliorate these problems, we propose a context-based tag disambiguation algorithm that selects the meaning of a tag among a set of candidate DBpedia entries, using a common information retrieval similarity measure. The most similar DBpedia en-try is selected as the one representing the meaning of the tag. We describe and analyze some preliminary results, and discuss about current challenges in this area